Connect@IPSDS Online Conference
Kreuter, Frauke
Keusch, Florian
Smith, Alan
Abeler, Johannes
Edinger-Schons, Laura Marie
Redmann, Katrin
Koomen, Maarten
CONNECT@IPSDS is an annual networking session as part of the International Program in Survey and Data Science (IPSDS). In 2020, CONNECT@IPSDS is also the final event of the IPSDS project, which ends on July 31, 2020. The conference is dedicated to facilitating information exchange in the area of survey and data science across various sectors (academia, industry, government, and non-profits). Topics presented will cover a wide range of domains from data ethics and privacy, healthcare, data visualization, and more. Due to the current evolution of the coronavirus, we will hold Connect@IPSDS 2020 as an online event to make sure we all stay safe and healthy.
 All right. Welcome everybody to our 2020 Connect event, Connect at IPSDS. Some of you were part of Connect in previous years. We had around this time a lovely event last year in Mannheim and quite sad to not have you there in person. It's a beautiful summer day today here, light breeze. And you know, I'm glad that at least we get to meet online and I'm actually super excited that we get to invite and host people who indicated previously that they can't join us and now can because we are online. So unlike my wedding celebration that would have happened next week, which needs to be moved, this one has not moved. And so welcome everybody to our 2020 Connect event. I am excited to introduce all the speakers and panelists we have, but I realize you, not all of you know me. So let me say a few words about myself. So if you know who is greeting you here. My name is Frauker Kreuter. I'm the director and founder of the International Program in Survey and Data Science at the University of Mannheim in Germany. And also I still the director of the joint program in the survey methodology for another five months at the University of Maryland in the US. And I was interested, many of you know that we love to train folks that work in the federal statistical systems and around. I do have ties to the Institute for Informative Research heading the other statistical methods research departments. So just a little bit of context. If you want to reach me with questions about the program or what we do here afterwards, my email address is right here. More importantly though, and this is my cue that I tell everybody that we do love to hear from you in the chat. And for at this point in the Corona crisis, I assume everyone has been plenty on Zoom and knows how the chat works. But if not, press those three dots on the side or look for a menu item that says chat and you can type in questions to everybody. And we already getting a hello from Japan. Hi, Pina. Great to see you. Obviously, there are too many people to introduce everybody, but I do use the chat and post questions. We have Q&A and Karolina and I will be monitoring the chat window and will fold those into the Q&A that we have later on. So I also want to extend my thanks to the University of Mannheim and the Mannheim Business School. While they're not physically hosting us, they have been instrumental in helping us set the event up and help with registration and the like. And they all, well, both institutions, hope you will come visit in person when we continue this event in the fall with its onsite portion, but also in the next year when we have our usual summer event. So let me introduce here briefly the agenda. I will leave this up for a couple of moments so that you can see it. We have, oops, sorry about that. We are starting with the career panel where you will see one of our faculty members, Flora and Koish, hosting current students in the program, friendly collaborators and future collaborators for a career panel. We try to make quite diverse in terms of backgrounds and look forward to that discussion. This has always been part of our connect event and I'm thrilled to open this up to everybody. Well, we created a little buffer, you know, often the Q&As go so long that it's helpful to have these buffers, but after a short break, we will then have keynote talk by Alan Smith from the Financial Times and second keynote talk by Johannes Avala from Oxford University. So my special welcome and hello to both of you, Alan and Johannes. And we'll have a closing discussion and then after another break, we have at 6 p.m. German time, different time, wherever you are in the time zone, and information session for all those that are planning to enroll in our master in applied data science and measurement and that will be hosted by Karin Fursinger and our colleagues from the Mannheim Business School. And so, as I said, I'm delighted to have you here. Anika, I'm not sure, is there a second slide deck I should be showing here? I only have this one, if so, I'll stop sharing my screen and you can upload that. Let me pass here for a second. No, that was the right one. Okay, perfect. All right. Good. So, already ahead of time, Florin, I told you I will make the intro brief and so we have plenty of time for the career panel and get to the interactive part. I love to see all the... Sorry, there were a couple of additional slides on this deck, I think. Okay, see. Sorry. Let's check. Oh, yeah, there are. Okay, I did... I thought that was a separate... Okay, all right. There you go. I told you it's warm here, so it's messing around with my... I thought this was the ending side, but this is the transition to me explaining what this program is about. So, let me take five more minutes for... Paul's hope to explain the international program in serving data science as it started and now the master program in applied data science and measurement. We have been starting five years ago under the auspice of the Federal Ministry of Education and Research. They kindly sponsored five years of program development to develop courses in data science probably defined, but for us with the clear focus on data collection and angle to the survey world. And we were fortunate to learn the online education part, what works, what doesn't work way before the corona crisis hit and then have the opportunity to teach our colleagues here in Mannheim and in Maryland a few tricks on how to do that. And it's future the program will be under the label Mannheim Master of Applied Data and Measurement housed and hosted by the Mannheim Business School and at the University of Maryland it is hosted by the joint program in survey methodology continue with its name international program in survey and data science. While these two universities are sort of the pillar and the flagship, there are a lot of faculty involved from partnering universities and University of Manchester for example is a strong pillar. Increasingly we're recruiting faculty from the University of Michigan in the United States but also all the way down to Chile we have faculty member there who has been active almost from the start of the program. Our goal with these related faculty members and universities is to eventually have a partner university on each continent and the possibility to enroll through these other schools. We also have a lot of faculty in this program that comes from industry or industry related I mean private and public sector but non-academic and you see here a list of them. People change jobs so I feel like every time I look at this slide deck you know I feel like oh wait a second this is outdated but I think this is at this moment in time actually a correct representation of our participating faculty and you see the diversity reflected here as well we just recruited two more people from Facebook that will be joining us teaching in this program they have been helping us to develop and sponsor the program on the US side as well. What is survey and data science? Not all of you know directly what this keyword means and for those a bit of context in the data science discussion I mean you hear people talk about data being the new oil and this and that they often talk about data that evolve through digitalization so through organic digital footprints that we leave in our daily endeavors and last year at Connect we had we had Roberto Ragon Bonn speak here and he designed this slide and beautifully captured sort of the difference in these data sources. On the survey side on the more traditional side and you know our speakers both of them actually have used or are using surveys in their work we always have been collecting data but in a very different way right we designed a data collection with a different purpose we had experiments we had surveys and to some extent influencing what administrative data gets collected and so what you know this program tries to do is to combine these two sources with each other and take the best of both. And Roberto whom I just mentioned has a beautiful project that does this combination in real life and has become even more important that kind of spirit in the last couple of months during the corona crisis and I'm talking about the price that index that they developed at MIT. Roberto is from Venezuela he got agitated when you know two decades ago he got the impression that Argentina might not be reflecting the inflation scores correctly or inflation indices correctly and that it could be useful to create an alternative indicator for inflation indices for countries. And so what he thought was well wait a second there are a lot of prices online these days we could just web scrape these online prices and use the changes in time from daily changes to reflect changes in the pricing. And what's interesting here while not the whole market of goods is online the changes in prices online likely reflect the changes in prices offline as well yet the information is available much quicker and so that was sort of the spirit and the idea of his project but as I mentioned it's a blending of two data sources because while they get the prices online they still do it or use it in for most countries data collected from the national statistical offices on the basket of goods that people buy and purchase and they use sampling strategies to have a good representation of the different prices and so a combination of the different skills that you would learn as a survey statistician and as you know what you see as data scientist elsewhere. Likewise statistic Netherlands has made its mission to be a zero footprint agency meaning no surveys it's interesting what statistics agencies think of zero footprint but anyway for statistic Netherlands it means no survey data collection ideally everything measured in the background and hence our name you know data applied designs and measurement it's really about how do you best measure what's going on in society and certainly right now what's going on society you need measures fast and reliably. The statistic Netherlands has used Twitter and in general social media sentiment data to reflect consumer confidence scores and traditionally they did this with the survey the survey data are depicted in the upper right corner here the Twitter scores are depicted in the larger part of the graph. Not going to talk in detail about this but what unifies us in all the courses and the kind of framework that we do is a mindset that comes from the survey world if you think of the way a survey statistic is produced you can think of a representation side that's depicted here on the right when we worry about what is the population we can draw inference to how well does our sampling frame where we actually take data covers that population and how can we collect the sample what are the respondents do the respondents do and how do we do adjustment to these sample data in stressful times like right now it's very difficult to do population samples you know sometimes you have to collect data on the fly and then make them work or you have to do shortcuts you know all that but without knowing the framework to begin with it will be hard to get the inference right and unfortunately we do see a lot of surveys that just take conveniently every answer they can get and then post in major outlets that X number of percent of people are doing whatever measure in the corona crisis. Now similarly difficult on the measurement side defining construct deciding what's the right measurement and what's the right response is something that requires some skill and knowledge and we're trying to teach that. If you think of that other set of data the digital footprints you often don't have as structured of a process you don't draw a sample you don't know what the measurement is you confronted with what comes in from many different sources and you know you people extract the data clean them more or less although that is a big chunk of work that a data scientist does and create a pipeline to load and store the data and you know then decide on how to analyze them how to filter how to create features and all of that. What we found in talking to practitioners and the faculty member from these other organizations is that it helps to know about this other framework to get this process here right because you still want to know which data do you not have what is missing where could they be doing do you have the right measurement and so that's the kind of thing that we are trying to teach you. If this little briefing was fun there's a couple of literature freely available online that I posted here and I encourage you to download and read up a little bit and but this kind of framework might help you to understand what the folks later on on the panel that are in our class courses have learned and will take with them into the future. So who are these people who is the program designed for we have currently four cohorts that we call them test cohorts that was more proposal writing right I mean they are really our students and participants in the IPSDS and then an incoming cohort with the master of flight data science and measurement and they really spread all over the world and what's missing here from this graph are actually all those that are enrolled at the University of Maryland then you would see Australia and Japan and you know a couple of other spots in Asian Africa colored because they too join our online program. We have been fortunate to be pretty diverse both age wise, region wise and gender wise and that has greatly enhanced the discussions and I hope those students here on the call that see this would agree that this work well. In general just briefly for the course structure just in case you are interested maybe in enrolling in some of the courses or all of it or get a certificate you can you know stay on until 6 p.m. our time and learn more on how to do that but in any case we have five modules we extensively discuss how certain types of data fit a certain research question how to think about data generating processes what do you need to learn to understand can I really use these data are they of high quality enough is that something that works. You learn how to curate and store your data how to manage your data and to be honest right now you're doing this global COVID symptom survey with Facebook where people get invited on the Facebook platform to participate in a survey and I have deep respect for anyone who does sort of real-time millions of data points online I mean management not a small feed and something that traditionally statistician or social scientists probably don't learn enough what they do learn a lot is data analysis and that's of course part of the program and so is data output and access and for that reason we have this visualization talk later on here not going to bore you with the details of the different credit points and the like will come back to that I just want to say that we have three text data inside survey methodology and survey statistics depending on what is of interest to you and as many online programs these days we have asynchronous and synchronous components or actually unlike many we have also synchronous component there's prerecorded material their readings their discussion forums and weekly online meetings as a package so you can prepare and then we assume have always the chance to talk to these instructors directly and I love the online teaching because everyone always sits in the first row and that's what I think makes the program so much fun we make sure that there are small virtual classrooms that sit together in these 15 minute sessions so unlike here we really can talk with everybody directly and don't even need to use the chat so this set our kickoff event for the newest cohort and you know a special welcome to all of the new students here is our connect event normally a two-day event first day where we create a community of the fellow students second day where we have the audience interested in data science and the series of speakers the speakers we have here and will continue on with them in the career panel as I mentioned Alan Smith Johannes Abla both giving the talks but on the career panel I want to highlight Laura mirror in the show on from the University of Mannheim and Patrick and Rayman from SAP with whom we organize women and data science in the previous years and yesterday and is becoming more and more an integral part of the program and maybe will appear soon on the logo faculty members that you saw on the industry partner connection line okay so I will not go into detail with the admissions we talk about these pieces later on you can learn about the prerequisites for those we try to make it as welcoming but if you are interested in those details we have an online info session on June 30 years from 1 to 2 p.m. and you are also welcome to visit our website or write an email to info.mdm at Mannheim and business or comm all right so without further ado your questions to the panel now and over to Florian for the career panel thank you for okay I'm going to share sorry I'm going to share my screen in a second that always takes a little bit okay thank you so I mentioned I'm one of the faculty and the international program of survey and data science my name is Florian Koish I have been one of the first employees actually in probably in the startup lingo I would have been employee number one of IPSDS when Frauke started founding the program I've been on work since in different roles I'm now part of the executive faculty board and I'm also a professor at the University of Mannheim today my role is all right and there goes Florence role I he was warning us that he's in a place where the Wi-Fi might kill him and it just did so no more employee one number one you can this spot is now there to fill someone else wants to join us are you back yeah I'm back I'm very sorry yeah so you want to give it another try otherwise you can do without the slides and since we have the panel online and yeah so yeah this slide right so maybe I'm I stopped sharing the slide because okay so we have four great panelists today I'm happy to introduce them to you first and then they'll have the chance to introduce themselves a little bit more about their background and I want to encourage everybody who is here on the call to ask questions to do so please post your questions in the chat right if you have never used to chat that's the little bubble that the way you can can post a question and I'll then relate them to our panelists so the four speakers the four guests that we have here today is first and in no particular order Ellen Smith who is the head of visual and data journalism at Financial Times so he's a data visualization specialist who also writes the Financial Times popular chart doctor column he's also a highly sought after speaker he gives lectures have had a couple of or had a TEDx talk called why you should love statistics so write out write our LA and I also learned that he's an officer of the order of the British Empire which is the first thing to me I've never spoke to a spoke to an officer of the order of the British Empire so that's that's great Ellen thanks for thanks for joining us next we have as Frau can mentioned Laura Marie eating a chance she's a professor of corporate social responsibility at the University of Mannheim her research focus are on corporate social responsibility on the communication of CSR and management of CSR but also on innovation practice sorry innovation pricing mechanisms and business models she publishes widely in academic journals and has received a number of awards for her work third Catherine Redman Catherine is innovation lead Germany Austria and Switzerland and next-gen innovation hub lead Southwest Germany at SAP but she worked at SAP in diverse positions she's also highly involved and responsible for innovation entrepreneurship and teaching and actually will probably tell you innovation and innovation with purpose and encourage founders and innovators to incorporate the UN global goals is one of her mission and then last but not least Martin Coleman he's data manager at the Swiss National Education Panel survey called transition from education to employment with the acronym three he has a background in anthropology economics and sociology and he has been working for social science survey infrastructure for the past 10 years as a data manager for example but also as a survey researcher and as Frau can mentioned he's currently a third-year student in our IPSDS program so welcome to the four of you I invite you to unmute your microphone so that we can we can start a discussion and I would I would really briefly like you to all four of you briefly talk a little bit about your background maybe past jobs and how you ended up in the job that you are right now and what your responsibilities are in these jobs so that that your that our guests get a better understanding of what your maybe potentially your day-to-day business looks like what type of data you work with for example and these type of things so again we don't go in any particular order on my screen I see Ellen next so Ellen why don't you get started thank you Florian firstly just it's wonderful to be with you all today I'm very excited to hear what all of my colleagues are gonna on the panel have to say I am head of visual and data journalism at the Financial Times that's my official title that's a senior editorial post I'm an executive editor of the Financial Times and I guess in in in a career panel sense the thing that's interesting about that job title is that it did not exist 10 years ago you know it did not exist five years ago that this is a you know the idea that we now have data in the newsroom of newspapers is something that I'll talk about later in my main talk but the role is new it's focused on data in the newsroom and data for storytelling the my journey to that role has been very interesting because I was very interested hearing fraca talk about official statistics because that's my real background in in career space I joined the Financial Times about five years ago after more than 15 years at the UK office for national statistics and I think the observation on that is it's an unusual career trajectory to go from the civil service to the newsroom but that says a lot about how these career paths are converging in the field of data science my actual academic background is in geography my undergraduate is in geography my masters is in geographic information systems so I've always been very much interested in how information is presented visually which on fraca's five modules that she showed is obviously sitting proudly at the top of those five modules is a final what Hans Rosling would call the sort of final six centimeters of the journey just into people's eyes so I'll pause there thank you thanks Alan so why don't we go to the next person on my screen and that's Laura Marie great to have you yeah thank you so much for having me as a guest I have to say I'm a big fan of focus work and also of the women in data science events so happy to be here and my name is Laura as Florian said I'm a professor of corporate social responsibility at the University of Mannheim also position that did not exist before and that way my background is that I studied economics and business management and then I did a PhD in experimental economics and I collected primary data in Indonesia running experiments on ultimatum game bargaining and when I was traveling in Indonesia I witnessed a lot of really the horrific impacts of our globalized markets on developing countries including working conditions that are looking like modern slavery is severe pollution exploitation of the natural environment and so on and when I finished my PhD in 2011 I decided I really would like to pursue an academic career and look more into the topics of sustainability and corporate social responsibility and I plan to use the methods skills that I had learned in my PhD running experiments but applying that to this topic in management and my direct surrounding my supervisors they all said don't do that bad idea you're never going to get a job and at that time luckily I didn't listen I was really passionate about the topic my doctoral supervisor gave me the advice that if I really want to do it I needed three things passion perseverance and good data so I set out to find companies to work with me and to collect data in companies and one of the biggest projects that I did and I have been in contact about this project with Prog in the past was a data collection at IKEA Germany I did a panel data collection with 240,000 IKEA family customers over eight time points so one year and I was granted a lot of opportunities by IKEA for example to manipulate the IKEA newsletter between subjects to include subsequent surveys to interview the store managers and so on I got to media tracking data and so on so I also combined data sources from data sets from various different sources and started to look at them and started to learn a lot about new methods longitudinal analysis cross leg models and so on yeah and that data set actually really gave me the opportunity to publish in these leading top journals which then was my entrance ticket into the academic job market another thing that happened through this big data set I really got a lot of awareness in industry and a couple of other companies asked me to do similar things so since then in 2015 I got the offer to come to Mannheim and since then we've been working with a lot of companies to understand better the topic of sustainability expectations of stakeholders reactions of stakeholders we also have a lot of links to the social entrepreneurship and the innovation community I also met Katrin a couple of times before so yeah exciting job I'm super happy to have pursued this path but it's probably not the most obvious career path in academia great thanks Laura next on my screen is Katrin yes very warm welcome also from my side for the whole participants and also the panel companions here thank you very much for the invite Frauke I really love to be here I'm I think not the classical data scientist so I'm more the user for data data what can you do out of it at SAP I'm responsible for the university alliances so I'm very interesting interested in innovation in exactly such career paths that we listen to here in order to connect dots them so I'm doing this at the moment as a platform as a personal platform me myself but I'm very interested in how can you leverage networks founded on database how can you combine people projects future projects how can you involve trends and so on so we heard yesterday a couple of interesting new yeah also new proof new jobs like decision strategies and I'm very interested in in yeah developing the whole thing so I collect data on my own but I really should do that now because the quantity is huge and the quality also yeah leverage my network by database so if somebody has an idea here I'm very keen and hearing more from you in my main role at SAP I'm responsible for the global innovation portfolio my role just recently changed and I have another connection dot with with this panel here I'm studying since one year now at the Mannheim Business School beside my job and yeah it's quite stressful time but I learned a lot about analytics people analytics and all the things that Mannheim Business School is providing as tools and strategy supporting methods so I'm keen in applying them soon when I finished in November thank you great thanks Katrin great to have you and then last but of course not least Martin and I have to give a special thanks to Martin because he's joining us although he just became a father of twins so he's sharing parentable responsibilities here and so found time to join us so thanks Martin for coming yeah if you hear some screaming on the background it's not because I have somebody locked up in my closet that's my new born girl they'll be complaining but they have to be pretty good today so I'm hoping it will be fine yeah thank you for the invite and thank you all for joining I'm very happy to be here after three years of studying I finally made it on the career panel as the student representative so very happy I could say about myself I probably have a more kind of traditional job when you think about you know working for a survey infrastructure and I kind of rolled into this job but while studying anthropology because many of my my professors had ready wild ideas and plans but they were not so savvy on the technical side usually and I started to kind of help them out like turning some of those ideas into practice and you know doing kind of data management on the side as a student and I think that you know over the years I've worked for many different projects I worked for the European Union and run some of the surveys for them and the more the further I got the more complex the surveys have become and now the survey that I work for is so complex that you really need a really dedicated team to kind of like even manage the data like if we've very far away from having like one kind of data collection event they have several events over several modes and there's like a lot of like data addition that's being done with like secondary data of course so just just working for one survey can be quite complex and taxing and I am very happy that I found this program it has taught me a lot and yeah and hopefully I can continue learning from it it's been a great resource for me personally. Great thanks Martin. Again I want to remind everybody that please feel free to shoot questions at us and I'll forward them to the speakers but I've organized I've tried to organize this panel a little bit ahead of time of course and thought about like okay what could we talk about and one thing that I would ask our panelists to tell us a little bit more is kind of like how their day-to-day work looks like and also what type of skills they need to do this like and where they feel like where have they learned them is this something to have like self taught them maybe where the designated courses this is something they learned back at the university or did they learn something completely different and had to relearn everything once they were on the job so maybe a little bit about that and again I'll start with Elin here. Yes thanks Florian I mean it's fairly easy for me to answer the question of you know did I learn my skills at university or did I learn them since I left you know I am so old and ain't that when I left university the internet didn't really exist so as you can imagine there has been a lot of learning for me to do away from university and I guess actually for me that's a really important challenge for everyone you know I graduated in a recession at a time where our main technological vehicle at the moment didn't exist and similarly students graduating now are going to be graduating at a time of huge uncertainty and you know the most valuable skill that I've carried with me I think from university is this sense of wanting to keep going right like in terms of you never get to the end of a learning journey and if you think that your learning journey stops when you've hit the four or five modules and you've ticked those boxes then you know careers are never normally so neat. I also think that the thing I've learned since you know you asked me I think one of the discussions we had before the event was you know what skills did you have to learn after university and I couldn't really code when I left university not really I mean I did a little bit of turbopascal right but the problem with being taught turbopascal in an academic environment in the early 90s was what's the context what is the motivation you know like teaching turbopascal in the early 90s involved teaching people how to code an alarm or a currency converter and I could see no real practical application of that to what I wanted to do was make maps right like my cartography that I did at undergrad was all with pen and ink right like pure manual cartography. The big change for me came when I learned how I applied some of the amazing technology that was arriving with the internet to things that I love doing which is to code to make graphics and suddenly I had an incentive and a purpose right so abstract skill sets can sometimes difficult things to learn in a pure academic setting when you don't know how well they're going to be used in your onward journey. But the other thing as well is just I know we talk about these things called soft skills all the time right like everyone talks about soft skills that's not a very complimentary phrase soft skills it kind of sounds a bit optional right like the soft skills are actually some of the most important skills that I've learned in the last you know couple of decades and that includes it's not just how can you stand up and do a TEDx talk but it's also can you go and talk to an editor and convince him or her of a story idea and why you think it's so good you know can you can you get people enthusiastic about an idea can you know can you listen as well as talk that side of things and so I've tried to develop those you know soft skills I will call them but I reluctantly I will call them soft skills try to develop those as well as the technical because they will enhance each other so for me it's been you know the university side of what I learned was very good at understanding you know I think I left with a fairly good basic data literacy and a sense of my own domain knowledge in the field of geography but the world shaped so much more afterwards one one side left and when I did my masters I did my masters while I was working at the official statistics agency and the experience of doing a masters alongside work was transformative right that was so totally different to my undergrad because it was much more applied much more focused on particular questions that I was interested in exploring and allowing me to focus. So that's great because we actually got one of a couple of questions and one is directly to what you just said is how did you transition then from official statistics into into the world of media and and what basically is there anything you learned there that do you know I mean I guess there's a lot you can apply now in your job but also kind of like what are the differences maybe could you elaborate on that? Yeah I mean it's a really good question and I'm mindful that I'm being ordered and there are you know nearly a hundred people but I'm going to be quite candid in my response to that because it's a very good question right how did that happen and why did that happen? So I already realized that my work at the statistics office was I was working with data visualization at the statistics office and the reason that I was doing that work is we started I think in official statistics to start to there was a fundamental change in the role of official statistics agencies happening and I think it's very relevant to data science which is historically official statistics agencies thought of themselves as number factories their job was to produce numbers in a factory sort of kind of workflow and then to just publish the numbers to leave the numbers outside the factory for other people to then you know to collect the numbers and go collect. When the when the web came along I think it started to question some fundamental principles of official statistics and you know official statistics started to rise oh hang on we need to use the internet to communicate and very quickly this phrase dissemination this word dissemination appeared official statistics agencies started saying oh we're going to disseminate our information again I don't think that's a very good word for data because it's an agricultural term and it's the one where basically a farmer can throw seeds into the field and not particularly care about where they land some of them will grow some of them won't. What we increasingly started to realize is that we actually needed to turn into much better communicators in the world of official statistics like you know the insight that you collect as part of official statistics methodology that's valuable insight that should be shared with the world and we wanted to do as much as we could to not just connect with the traditional policy makers and business decision makers but with a much wider group of people you know interested citizens and so that's where that work started from and that changed our view of what success historically success for statistics official statistics was nobody complains that was a great release because nobody complained now the easiest way to make sure that nobody complains is to make sure that nobody looks right to not advertise yourself particularly well but actually we changed our mindset and we wanted people to look we wanted to generate debate and discussion and so therefore I started to talk much much more with people who were working in the media and we started to syndicate our content to the media which was an interesting thing and eventually talking to some of those people in the media led to them saying well look these skills are very valuable that you've learned at the statistics office would you like to come and work in a newsroom doing them and so that's how the journey happened and the biggest difference between those two environments is simply speed so rather than ten years to do a census I usually have 24 hours to produce you know quarter of a million newspapers but you get the idea that this the processes are very similar but the speed is slightly different. Yeah great thanks that's that's very insightful I want to move on to Laura you already mentioned a lot of the skills you have learned as part of your PhD and working with this type of data any other things that you feel like well in the past couple of years this is something that I can't live without anymore these days in terms of the skills and also maybe you mentioned this already can you talk a little bit about this intersection between academia and industry and how you made this decision of pursuing a career in academia but it sounds like you still have a lot of like connections with industry. Right I also just wanted to pick up the question from Annika from the chat how did you choose your industry academia versus corporate and for me this was actually a tough decision because my main motivation was to change something in the business world and I had exactly that question where can I be more effective going into companies or staying in academia and my decision actually was based on the rationale that in academia I was I would have the maximum freedom and the independence right so if I would be in one company I would actually have to work along the lines of the agenda of one company and if I'm in academia I'm independent and I can freely choose the topics and my position on the topics but I also had the idea that if I go to academia I will really want to build strong bridges to the business world and actually engage with the real world out there and not just be an academic that's you know running data analysis and publishing papers behind closed doors. In the last five years in Monheim I've actually spent a lot of my time building these bridges trying to collaborate with companies and also talk to companies not only the ones that are super sustainable but also trying to understand you know the processes the hindrances and motivations of companies that are not really transitioning so really trying to understand the struggles and go into a dialogue there. And yeah you said so what are the things that I needed on the way I also like Alan I come from the past where in university we learned toro Pascal we actually programmed with pencil on paper in the exam and I had to learn everything basically that I need today by myself and as Alan said it's a continuous journey right you always learn new things along the way and I think the best thing is just to have a problem and then learn by trying to find a solution right so really working on the stuff and by working on it learning how to do it when I did my master thesis I started to do structural equation modeling then during my PhD I used experiments and later on longitudinal analysis and when I started to learn about data analysis there was actually nothing like YouTube tutorials or anything right so I really had to go to the library get a book from the shelf and read a book so I mean all of you guys out there that are now studying these things I think you are in such a luxury situation that you can just use online tutorials and videos and MOOCs and everything like that so I think that's such a new world of learning and I totally enjoy it now when I want to actually acquire new skills and about the soft skill question so I think as an academic that is trying to build these bridges to industry for me it was super important to actually have these soft skills of trying to understand both worlds right because academia and the business world are usually very separate and there are not so many links and if you ask practitioners why don't you work with academics they say oh you know they are they just interested in theories and not in the real problems and on the other hand if you ask the the academics why don't you work with a business world they also have a lot of stereotypes right and say you know the practitioners are not interested in gaining knowledge they just want to solve a talk problems they want to increase profits so I think there is a lot of potential but you need these soft skills of really understanding the different stakeholders and trying to bring different parties together to to build these platforms and that's I think the strongest skill that I had to learn and again there I think I'm still on the journey of learning from university I have to say I don't I didn't get so much that I use today I also got the maybe the skill of perseverance having big lectures of 1000 participants sitting there trying to figure out how to solve the exam but everything basically that I need on that I need on the job today I had to learn by myself but I have to say that I had a very passionate doctoral supervisor and he really taught me this passion for research and I think if you have that then you know you have the right intrinsic motivation to also go this way right and find the necessary information teach yourself how to solve these problems and I think this really needs a lot of passion for problem solving and just you know not being scared of starting just getting started and just trying to do it. Yeah and today what are the things that I'm trying to learn I if I have problems today that I want to solve I use YouTube videos tutorials MOOCs and so on for this summer I have a long learning list again things I would like to know more about and I actually want to use a couple of these really cool upcoming new online courses for example to learn more about Python and I'm also very lucky to be married to a computer scientist who's also teaching me a couple of things like programming machine learning skills and so on so I think it's really a continuous journey and I for me the biggest learning was that I need a problem and then I you know if I have the passion to solve the problem then I will find the solution and the resources are getting better and better to solve these problems. Thanks Laura I'll move on to Catherine because you mentioned already a couple of things and especially I think it's a great transition because Catherine as you told us you're in charge of the university alliance so kind of like this this this intersection again between academia and industry so maybe we can tell a little bit more about that but also from a personal perspective what made you come back to now the modern business school and make this decision to after potentially being out from the university for a couple of years pursue kind of like another degree or learn something new. Yeah it's quite a couple of years you're right so the first two days were a really interesting journey for me I thought I will never stand sitting nine hours here in the audience lecture hall but yeah finally I was perceived variant enough to do it for one year now already and soon I hope that I will have finished it because it's quite the double with family it's three columns that you are at the moment serving us three pillars and that's quite exhausting I must say so I really need the vacation afterwards because you don't have any weekend it's really beside your full-time job so I think that's exactly what what you said Laura it's lifelong learning I think that's the most important thing and yeah so find your I think the most important item you have to find out is find your your strengths and and your and stick to it yeah don't let you be influenced by people saying you will never learn that you will never be able to do that if you have the impression that it's the right journey just go for it because you only can't be you only can be the best the most the best person in in something if you really have your your own confidence in yourself and and know that you can do it so I think lifelong learning find your strength and safeguard your your values and energy after afterwards when you when you find something that gives you energy that gives you success then try to to elaborate on this and on this area and try to be an expert in it so that's something yeah also to have a like a break and not only run and go forward forward forward but also take your time and if you have the impression you can serve to something or you can learn a lot in one area then stick to that and try to be even better in it so then comes I think because I have a quite long experience also now a professional wise there comes times where you have to reinvent yourself that can be if you give birth to a child also Martin I think he will recognize now that there are things becoming more important suddenly than your professional career it can also be negative things and now in corona times I think there can be more and more people and families also affected by things they never would have thought that happened to their families so it's something like resilience try to to take life and also of course professional challenges like they come to you and not try to to to fight against that sometimes you have to stay with a flow and and make the best out of it even if in the first glance it's really you think I know why does that happen to me now there's always some sense behind I think and that's why I think why you need to be courageous innovative yeah make like resumes about yourself how went the year and what do I want to to go further I always always do mind maps about myself not because I'm a narcissist or something and it's only to to have a point in time where you can where you see okay that's my that's my balance sheet at the moment and I have these goals how can I develop further myself also and if you have children you know that you have to do it in a dual way you do it for yourself to get forward and to learn more but also for your children how can you motivate them to think about themselves also and not only to stick to school grades and and learn the stuff in school but also to learn things around the school and the education items and that's that thing I always learned is what I live and that is something that combines a lot of people here in the audience and in the panel so shaped your future because it's your future yeah that's why I love now to to teach and I wanted to become a scholar professor when I left school and my parents said no no there's no chance to be a professor there are too many and not enough jobs don't do that so I choose another thing I became a bank clerk first what was completely wrong for me afterwards I discovered okay even something that you didn't like like a bank formation can help you later because now when I counsel startups or mentor people there's always the balance on the bottom between cost and revenue is always filled in because I know how important that is to convince investors or people who can help you so shape the future in a co innovative way and mix teams that's because I'm a design thinking coach since 10 years and I discovered that linear planning you can do it but so you have to revise your planning often so I choose now the way to be iteratively planning and look what comes to me and then try to integrate a new project and less than you have of course drop other projects that are less vivid so in the iteratively planning and not only linear that's very important you can't apply that to everything that you need to play both methods of planning in my view and to the intersection of industry and academia I'm also glad that I got this job in six years because it's exactly what I always wanted to do combine the bridges like Laura also said and focus also doing this every day so not only staying stick to your world where your comfort comfortable but also look 360 degrees around yourself your world and try to combine other areas in order to get innovation disruptive ideas and teaching at universities that gives a lot to me because I learned from you from the younger generation how you solve problems how you think how you capture your we have that already how can you gain new knowledge and it's really completely changed from the way I learned at university so I'm keen now at the Mannheim Business School to learn how my peers there which methods they have to capture knowledge also and to gain more insights and at SAP I can really say I'm an entrepreneur I have like my little own business insight SAP university life so it's also possible to free yourself with a couple of years that you have experience and know people and yeah be your your own entrepreneur even at a big corporate so I'm looking forward to your questions thank you so thanks Catherine I also want to before we we already got a couple of questions that I want to for the right way I also want to briefly give Mark Martin a chance to also tell us a little bit about his story why he decided to for example start at IPSDS and and what were the motivations there what were the why did you feel you needed something to learn more like what what what was missing more or less in your your life that made you go through that right I had too much time I needed something to film my time and now I have twins though I mean I should stop now funny enough it's it's has something to do with what Ellen called soft skills which I also I don't like that term either but I think I was hired in my team to be kind of a conduit between our IT staff and our academic staff and I during my time there I realized there is a ton of miscommunication going on all the time between and if they go back and forth between design and implementation and then you know you lose so much time fixing for mistakes and miscommunications and I saw this program and thought that it really fitted that bill you know it's it's it's it's kind of like in in the middle layer although I still think that maybe it could be a little bit more heavy on on information science for my taste but it's it really has a useful resource for me to think about also developing those soft skills in terms of how do you communicate between those different aspects of my work and you know it another thing that I think is really great that I want to impress on young people thinking about taking this program I mean I I I the resource is not only very very broad and very interesting in a interdisciplinary aspect but it's also extremely flexible which I now extremely appreciate I have like little hours in between the day like at night or whatever and I can just always now take some device somewhere in my house and do you watch a lecture video or do some homework or whatever it's it's great to for that as well so it's easy to fit in my in my in my day but yeah I I think just this kind of like interdisciplinary kind of aspect of it was really kind of key for me because I was looking at other programs too of course you can go deeper into statistics or you can go hardcore information science or something like that but I feel like this program has a good balance between the two thanks Martin so there's already more questions coming in so I'm ask our our panelists to answer them so one one question came for Laura asking how much interest is there actually in the business sector and using accurate and reliable data for decision-making on strategic priorities such as the sustainable development goals so you have worked with a number of companies as I take it so maybe you can stay a little bit about that yeah I think that's a very interesting question so and I'm a little bit ambivalent so I can explain what my experiences are at the beginning of course there is a lot of interest from the business world because they know that they on the one hand they know that data analytics are important on the other hand they have to start to report about sustainability since 2017 we have a EU regulation that tells companies that they have to report on non-financial performance so everybody wants to explore ways that they can use data on understanding their social impacts and environmental impacts better on the other hand I have to say that if you start talking to the companies about the specific processes and the necessary steps then you have to do a lot of explanation work right because there is this diffuse idea of what you know a scientific study is going to look like but if you then go into really working on it of course you have to yeah you know go along a lot of ways that take time good science takes time you have to do a lot of explanation why you need different data sources why you need a good measurement for example why you cannot go just with a single item or in some cases why you need objectively observed behavioral data and so on and I think this explanation work is really important and understanding the other side and telling them you know this would help us to understand that in a lot of my collaborations I had to invest a lot at the beginning to do this explanation work but then if the companies see what they can get out of it they really get interested and they are willing to go this long away and doing the work for you know good science and I think that sometimes it makes sense to go step by step right because what the practitioners often need is some quick wins that they can show to the management so they need something like numbers to show okay look at this this is great we need to go further and as a scientist maybe sometimes we think that you know we have to produce the perfect results first and then after one year or two years we can publish that in a scientific journal and I think for the collaborations with businesses in many cases it's important to really give them these quick wins and to do a little study first and show them you know look at this this will help you to explain to your management that you have a positive social impact one example in our field is really social impact measurement and a lot of companies don't even believe that this is possible they know that they have a lot of data but they say you know can you actually measure social impact and then when you start to work with them and you give them little quick wins and say look at this data you can go to your you know to your manager and show this a couple of slides then they are really motivated and say okay how can we do more let's sit down and I think that's that's important in the process that you understand that the other side needs to learn what what it takes to produce valid results right and that's the explanatory work that we need to do. Yeah great thanks so there's there's somewhat a related question came in for for Ellen on this relationship or the role of the new or big data sources comparing that between what's collected in the statistical agencies and in the national statistical offices and then now on the other side for for the financial times right and in the media so do you see a shift happening there also from from the most traditional players such as the national statistical offices using them or do you think there's still like a big difference in how and what type of data the different places are using? Yeah I mean wow there's a lot to unpack in that set of questions I mean the first thing that I'd say is that I left the world of official statistics just as just as this real interest in alternative data sources started to become not just not just an opportunity but also a pressure because there's an assumed knowledge now I think a public knowledge that there is data out there and the longer that the world of official statistics didn't use it the more questions there would be and but of course it does bring with it a whole question of the legal framework for doing so how do you prevent this becoming like the wild west of you know having spent years trying to get survey methodology workflows in official statistics organizations you know as as well drilled and as efficient as possible how do you introduce this chaos of data that was not collected for the purpose of your survey to be used for your survey you know that kind of thing and I think it's been really interesting for me personally to watch that develop so much in the last few years from a slight distance so you know if you go to my old UK official statistics office you know they now have a very well established data science center that did not exist five years ago and so to see this investment in official statistics being able to leverage these sources of data I think is tremendously tremendously useful and in fact you only need to look at the current crisis to see how valuable that has been because you know the UK public official statistics have been super responsive during coronavirus to be doing all sorts of really interesting things on on much tighter timescales than they are used to do to doing and I think the really interesting thing in the slide that Frauka showed earlier was this this complementary set of sources allowing you to use two very different techniques to get two very different angles on data you know for from using sources like big data that are maybe less good at answering some of the absolute questions but very quick and responsive and relative right and positioning those together is quite a powerful proposition I think the one thing I would say from the Financial Times perspective is obviously I'm working in a sector now where the commodity the end product is not the statistics the end product is something slightly different and we are obviously in a world where we are trying to play our role in the reporting that we do about kind of responsible capitalism about about making sure that the direction of business is is sustainable and responsible and you know things like the ethics of data is an absolute story that we are you know partly involved in in telling you know we we have writers who come in and do pieces that I mean people like Shoshana Zuboff and that the surveillance capitalism space these are things that our readers are tremendously interested in you know the ethics of data and the which data is starting to become you know it's it's a bit of a cliche to say it's the new oil because there's lots of reasons why it's not it's something very different but we're working in a very very different space to the official statistics agencies and my biggest my big lesson when I left the world of official statistics and arrived in the newsroom was to realize how lucky I was for the previous 15 years working with teams who had such who always gave me very high quality data to work with that was well formatted that was well documented that had a methodology that was explained because the use of data out in the world where I work now it's just generally messy from the start right like and it's just half of the job is working out what you can use and what you can't and that kind of evaluation of data quality particularly when it's data that's not come from official sources is a huge challenge for us I'll just finish with one quick example I had it's not uncommon for this sort of thing to happen in my job right you will get an email from a PR rep who will say we've got some exclusive analysis on property prices in the UK and we've got some shock results about which areas have gone up the most and it's not where you think now my you know my my sort of first guess was it's probably London where the prices have gone up the most and I looked at the data that they sent as part of this press release and there were no London places in it at all and I thought well this is really crazy so I better ask what they're doing here and all this agency had done had been to take an official statistics data set on property prices and remove the London locations in the data to make something more interesting and then repackage that data and push it on and so like here in things like this you start to see how misinformation spreads that there's this kind of a very badly policed area out there where I think actually you know the world of official statistics is obviously a great reference point for best practice and for the right way of doing things but it shouldn't stop there right like we've got to make sure that our society is functioning along data literate lines and I think that's a combined effort I'd like to think that both official statistics agencies and organizations like mine would all see a part in in resolving that yeah so yeah thanks thanks Alan so I see I see more questions coming in especially for Alan but we will hear more from him very soon so at this point looking at the time I would really want to thank all our participants here the panelists Alan Smith, Laura Marie, Eding Ashons, Katrin Riedmann and Martin Komen, thanks for taking the time to talking to us let's give if you can like a virtual round of applause and I believe the next time point is that we take a short break and we will be back at 4.30 for the talks I believe Frika anything else we need to say at this point? No that's it Anika will advise us if there's anything she wants people to do here but mostly we wanted to give you the chance to go to the bathroom without your cell phone while you're listening to us or make yourself a cup of coffee and then be ready for the talks and questions and discussions afterwards and my two heartfelt thank you to the panelists I always love the career panel and every time I'm learning so it's never end thank you all thank you Anika can you say a few words say you're on here yeah we'll share the program during the break so if new people come in they know where we are and yeah but that's it I think everyone else can leave their videos on or turn them off whatever we'll see you back here at 4.30 thank you all it's good to have you all here and it's wonderful to have Ellen Smith here who will do one of the two keynote talks now the first one is by Ellen and the second one will be by Johannes Abelo and we will have a quick Q&A session after both of the talks and I will moderate this with together with my colleague Markus Heckerlotz from IPSDS and it's wonderful that you're here and it was already interesting to hear all your your insights into your career path and into your career at the financial times and your talk today will be about data visualization and journalism in the age of coronavirus and I do not want to steal any time from you so without further ado the zoom floor is yours thank you Caroline and let me just share my screen so that you will stop seeing me in my spare room and hopefully see something a little bit more interesting which is a presentation here so I hope you can see my screen okay and you might be thinking that with a talk title of data visualization and journalism in the age of coronavirus why would I start with a picture of a cat sitting in a tree well this kind of takes me back to my early discussions about data journalism when I was still working in official statistics and I was very skeptical of the field of data journalism I was at a conference in Australia with an economics commentator from the Sydney Morning Herald Ross Gittins and I said to him Ross do you have data journalism in Australia and he said oh yes yes we have data journalism in Australia now Alan he said because rather than report that the cat got stuck up the tree we can now confidently report how far the cat got stuck up the tree and so journal data journalism for me this was very much my impression of what we were talking about with data journalism is that not much of it was really data science that a lot of what we were seeing was the spurious use of numbers to try and kind of inject some sense of analytical work that maybe wasn't necessarily relevant and the reason why I kind of start with that is that even with the coronavirus and even at the FT the very early reporting on coronavirus was of that sort you know these are not quite the cats in the tree but this is what tends to happen when the news cycle is happening very very quickly and information reliable information is difficult to to come by the the little information that you have and in this case it's the number the daily changing cases inside China this was from very early in the story in January was almost the best that you could do in very very sort of quick turnaround circumstances and unlike a lot of other news agencies I think we're very aware that when we do something like this it has a very much a limit in terms of how long that sort of reporting is going to be able to be uh carrying a story so you know I think throughout the coronavirus we were seeing a lot of news agencies just throwing data onto maps and charts in the hope that some of it would stick what we realized very early on with coronavirus was that actually this is an opportunity for data journalism to really come into its own in the Financial Times newsroom and so we set up a page at the FT specifically to track the data that we started to collect from places like Johns Hopkins University from the European Center for Disease Disease Prevention and Control and lots of data sources that as we were starting to work with them we were assessing are these reliable sources of data and if if one source of data on its own is not reliable enough for everything how are we pulling together data from different sources to make it so this page became it was set up very early in in in the story and we started to put content onto this page that went beyond just the simple maps of cases and deaths I also thought I'd just draw a point that when this story first broke of course it was a story that broke in Asia and you can tell actually even just from the photos on the page that at this time this had a very strict geographical focus to the story as the story has developed we have changed you know as the epicenter moved to Europe as it moved to the US and as it has now moved indeed to Latin America the use of things like photographs is actually an incredibly important part of our visual journalism which is to to carry empathy and understanding of the human conditions that data alone maybe can't you know so these are composite pages that contain not just data visualizations but also some explanation some links to other kinds of content that are related and as we started to add content to this page one chart type more than any other started to become to resonate with readers and it was this chart which was created by my colleague John Byrne Murdoch and it was a chart that's been you know a long time in the making and refining which was pulling data together from lots of different sources to try and come up with some sort of form of meaning for international comparison about what was going on and working out how to present that now so that's a point to stress here is that the way that these get created is not that somebody goes away and does traditional academic research on a deadline and comes back just having worked you know it's not just a case of accelerate in the normal processes the process is the generating these sort of visuals are generally very iterative you know post something look at it see how people are looking at it you know look at the responses and evolve the contents of these graphics evolved a lot over time and we've paid a lot of attention to things like for example if you can notice that there's a log scale on the on the it's like a semi log chart because the the y-axis is a log scale and you know we spent a lot of time thinking you know is this the right kind of scale for this chart will our readers be able to understand what's going on will they be able to recognize that this is a chart of acceleration not absolute levels you know and and so on and the chart certainly was popular enough I mean it went completely viral and it's a chart that's certainly was popular enough I mean it went completely viral I think John had to stop checking his Twitter account at one because it was just so amazingly popular on Twitter but it one of the things that was really fascinating for me was that LSE went on to do a piece of analysis where they said well you know actually the public do not understand logarithmic graphs on these COVID charts they said not only do they not understand them they're better at reading the data if you just give them data in a linear scale format which I think this is really interesting because this is the sort of dynamic that has been rated by the crisis you would not get this action and response in normal times though so this has been very very interesting and I still have some issues here about making hard and fast statements about the public do not understand logged graphs you know like that this is definitely a period where we can start to push people's data literacy and understand allow them to understand why we're presenting data in a certain way I've written articles about what in the FT about why log scales are a good thing and why you should learn how to read them so this is a log scale of the Dow Jones industrial average right and when it's on a log scale you can clearly see that it took 25 years for the index to recover from the Wall Street right on a linear scale it looks like that and I'd like to give that to the LSE authors and say yes the linear scale should be used in all circumstances you can brush the Wall Street crash under the carpet very neatly with it and so I've started to write some articles in our work and career section of the financial times I've had a pause for about a year now but we're going to start writing these again soon about the decisions that we make when we're presenting information and this is an area which I feel has traditionally underrepresented in academic space both on the arts and the sciences side for very different reasons and one of the things that's lovely is that this is an area that's right for huge amount of academic exploration and I do you know this was one of my favorite projects from last year which was sonification of economic data 40 years of the US economic data on the marimba you know some really wonderful opportunities to rethink how are we presenting data to people and should we be and this was quite an interesting process because what started off as a little bit of a sort of pet experimental project turned into something very real when I realized there were huge accessibility opportunities from presenting data in sonic form and working with you know I had somebody who was blind drew this US yield curve for me from listening to this music and it was a fascinating sort of project to work on but anyway back to the chart our solution to improving this so that people could not complain that it was log or linear was to make it completely interactive okay and so if I can just I'll just I'll just come out of this presentation mode so that you can see this in action so this is this is the interactive version of the coronavirus tracker so you can select your own countries that you may you want to make a comparison between so we're looking at the rolling out just seven-day rolling average of the deaths in Germany attributed to COVID-19 and we can compare that to other countries so you know let's put that in here and you know this is a simple sort of form of interactivity we can flick between death and cases we can switch between cumulative totals and daily cases we can also do the famous shift between linear and log there we go as the United States do very very well on the linear scale and we can switch between the raw numbers and also the per million because actually that dynamic changed quite a lot as the story developed because a lot of outbreaks start in localized instances actually the raw numbers were enough in early on but as the virus said it made more and more sense to standardize the data using per million so we tend to use interactivity when it's important to personalize the content around the reader and what we see generally is that interactivity is very often used very lazily like if your only use of interactivity is for someone to mouse over something and for something to pop up that's not enough it needs to be deeply personalizable and indeed the personalization that I make on this chart if you can see at the bottom of the screen if the url becomes fully customizable so as you make amendments to the chart you have a unique url to your view of the chart and you can share that with reporters with readers with people on social media you can use it to start debates hey look at this log scale chart of new deaths between UK and Germany and people can deep link to those sort of visualizations so that for us has been incredibly important and we've been using a full suite of data visualization techniques try and visualize for people how this crisis is unfolding with all of the graphics designed to try and look at some sort of angle on on the the evolution of the pandemic and so for example this chart here which is a something called a stream graph was our attempt to de-emphasize the map right like we were seeing a lot of maps where people were just putting bubbles on maps everywhere right that's how you visualize a pandemic but actually yes this is geographical data but actually it can be quite useful to see the geographical data in a different form and so this stream graph allows us to see this sort of onion skin style evolution of the crisis and this kind of shift in emphasis from Europe to North America to America becomes much more evident than it would do on a on a map and actually the metrics that we're using for describing the crisis have evolved over time as well it started off with cases and we know that there are problems with testing and you know international comparisons based on different testing methodology COVID it's incredibly difficult as is the reporting of deaths because every country has slightly different methodologies for for the techniques that they use for births and deaths and so this becomes difficult and so instead what happened was the team collated excess deaths data internationally so scouring lots of different official statistics websites and official sources of deaths data and making comparisons between deaths now and the equivalent deaths in recent years and one of the things that's been important on that to make this honestly tell you if you work in one statistics agent that's very different from trying to be a user of your 40 statistics agency websites so the pain in assembling all this data together we ended up sharing this data publicly on the code sharing site github so that's all of this combined csv data for excess deaths publicly available for people to take the data and use it which i think is a really interesting that conversation we had before on the balance between official statistics agencies and news organizations that for me this is quite a sophisticated approach for news organization to be doing with to be actually supplying some of the underlying data there is traditionally no expectation that we would be doing that and then what we realized in the process of this evolving story is that our role as journalists was changing produced on off stores you know like the newspaper story that you know is front page news one and then is like in the in the in the rubbish bin or the recycling bin the next day we were actually moving from kind of storytelling individual stories to managing products managing data products so that involves as soon as you have pulled all of this information together of course you get increased demand from other journalists for for our help because data is at the heart of so many stories at the moment and journalists who are not who don't feel themselves data journalists are coming to our team for advice and for help and we also have increased scrutiny from readers and what do i mean by that what i mean is the page that i just showed you our coronavirus tracker is the most viewed page in the history of the financial times website ever by a magnitude of about five right so data journalism has absolutely dominated this crisis and with that we put data in front of a huge audience relative to our normal audience which means everyone is asking us questions about the data and the techniques that we're using for visualizing it where we're sourcing the data from and so on and in fact that data sourcing and updating is become a huge part of product management for for the stories so what we did was we set up a dedicated email address on the page saying help us to improve this if you if you know of some sources of data that we are unaware of please tell us and we'll try and incorporate this into what we're doing and just to give you a preview that's the sort of response that we were getting in the inbox on the back of that request you know everybody from sort of talking about that i've tracked down some all-cause mortality data for Serbia for the last five years these are the sort of emails that are precious to us because it's allowed the team to respond to reader feedback in huge numbers and you can see on some of the tags there some of those tags are academic right which is again a dynamic of the crisis right that is definitely something that's new and should be encouraged um we of course have then been using kind of techniques for visual journalism to take the data that we've sourced and bring it into the storytelling space and for me when we were talking about the skills that I didn't learn at university earlier this is one of them because at the time no one was teaching people about how to present data and I think if that was one of the things that I'm most happy about the evolution of data science compared to statistics as a as an academic field it's the emphasis that data science places on being able to communicate what you've done at the end right like that's tremendously important so this is the financial times visual vocabulary this is our own resource that we've created at the financial times we have it big on our newsroom wall it's designed to allow people to to visualize stories based on the relationships in the data that they're working with and you can get hold of your own copy of this just if you go to ft.com forward slash vocabulary um and just to kind of round this off we've made it multilingual because guess what presenting data is a multilingual international language so this chart's available in chinese and japanese and spanish and french I loved learning that a pie chart is a camembert chart in in France um you know this is all good stuff um how does that what does that look like it's the thing that allows you to decide should I show the changing lockdowns of the world on a map or on a chart you know how different is how important is it to show changing patterns as opposed to spatial patterns you know and the answer in this case was to do both because they were both offering us something very very different about what was happening with the the government responses around the world um diagrams that have come out of biomedical science are pretty good for showing how airlines have been affected with their origin and destination routes um charts that look like petri dishes are pretty good at showing the difference in financial impact on the share prices and stock value stock market value of different kinds of company um a whole range of chart types that our readers probably have not seen four or five years ago have become a default method of reporting on stories with kind of a much broader range of chart types based on the underlying flows of data so this is this is this is trade data that is core data this to an ft reader understanding the world of trade but it's actually also quite complex data because trade flows are matrices right like so you need techniques for visualizing data in matrices of which this is one um you know we had a great design decision about this one where we were trying to show the fact that if you could believe the chinese numbers that they had managed to contain the disease to one province in general terms and that that was very different to the us and we were tempted to do that using a map but nothing seemed as effective as doing it like this you know just to use a slightly different technique um and a lot of our charts are actually relatively straightforward but the the the thing to to note with the design of these charts well there's a couple of things these charts are all what I would call um they are like visual rhetoric okay they are meant to make an argument to inform some um interpretation or view of the data so this is different to the world of official statistics sometimes where you're just saying here are the numbers so if you look all three of these charts on screen have a narrative title they're actually trying to guide the reader on how to interpret um the data and that it's yes it's important to show what the data is and that can happen in the subtitle right like in in the footnotes um but also we're very keen to try and draw people's eye to the data we want people to look at which is in this case the highlighted colors you know and the other colors here are for context um alternative data sources have become incredibly important to us energy usage google maps football data these are all things that allow us to understand how is the world changing um some data was more difficult to collect than others this is the we did a huge amount of work uh Steve Bernhard and David Blood analyzing how much flight patterns have changed between April and between January and April extraordinary stuff um and so our language of visual journalism I think has evolved through the crisis uh we had done some of this stuff before but the thing that's been really fascinating has been to apply this kind of thinking on deadline in a rapidly changing story space and and the results of that I think have been very warmly received by our readers um I ought to say not everything is a chart right as well as the visuals visual storytelling can take all sorts of different routes and actually illustrative journalism that explains complex technical uh and complex science to people who are not scientific specialists is also a hugely difficult challenge and we're lucky to have a couple of visual journalists who specialize in this type of visual journalism which has been again tremendously useful as our understanding of uh COVID team has developed um and sometimes these things can combine this is kind like a chart in illustration you know how far does a sneeze travel and how a cough these are sort of very interesting sort of and real world examples um the thing that I'll just finish with is to show you that one of the things that we can do on the ft website now is people can actually if you can see in the top menu here we have graphics is its own thing right and we have a searchable database of all of our visual journalism so you can search for coronavirus and it will allow you to see all of the coronavirus graphics that we're producing in a chronological thing or if you want to go to donald trump you can search on that you know um that has also been a really interesting thing to do because using graphics as the way of discovering stories has become an important part of what we're doing as well so um you know data journalism is definitely not new it's definitely more about than about how high the cat is in the tree um but I think if there's one positive that people are looking for in the world of data science for their careers is um here it is right like organizations are embracing this sort of work at the heart of their core business model um so I'll stop there and hopefully you've got time for a few uh questions thank you thank you so much and my my head is spinning from all these beautiful graphs and visualizations and I don't know which one I want to first look at in detail um after today so thank you very much um we already have some questions uh from frouker uh and she's really interested in how many people are there on your team how many people are producing this multitude of graphs of visualizations and how do you get to those designs so I'll talk about the team first because I'm very proud of them they're all brilliant um we have got a team so overall the FT's got around about 600 journalists and my team is about 25 people um and that's about 20 or so in our nominally based in London or certainly UK based now um and we have two in New York or one in Hong Kong um that covers a real range of disciplines though we've got everything from sort of graphic artists and visual journalists to data journalists and statistics experts and so um the way that we organize ourselves is to try and take advantage of that overlap in skills and for me this is a big difference so like 20 years ago if you were graduating in statistics you were probably anticipating if you're going to work with other statisticians right like that you you cover less with groups of people who have the same skill set and interdisciplinary skill sets are actually in my view much more challenging and rewarding and equal measure right that you learn and value everybody else's skills and try and integrate them um in the context of how we do things there's one thing that will determine how what does the process look like and that is time right like if I've got if I've got two hours it's not unusual for somebody to say hey Alan somebody just said something in parliament at 3 p.m. and we need a we need a chart of it oh by the way we're going to print it a quarter of a million times at six o'clock right so you've got two hours or three hours to just from what is effectively a blank page to come up with something that is ready and finished and obviously you're going to do that in a different way to if someone says hey Alan next month we're you know we're doing an investigative piece so we've got one month to run at something well we're going to take something for three months um so we have a we what we do is we assign people so that sometimes we have a group of people who are we always have a group of people who are covering the news cycle and their job when they arrive on those days is just to cover rapidly changing news events you know it could be an earthquake somewhere you know obviously in the world of covid it will be something somewhere right like but something that we didn't know about yes but that we need to know about today um and so people uh spend some of their time in that space and some of their other time on deeper projects like something that you know is going to happen like the us election well you know you're going to have six months to plan for that for example and you can approach that differently and i like to make sure that everybody has some time in both contexts because if you have three hours to do something it certainly helps with your decision-making skill set right like you know it focuses the mind and but then again you know it's very different when you have a longer term project and you need to manage your time and understand what's possible within a longer time period so it's it's challenging and rewarding but that's kind of how we do it yeah um people have been asking about your tools like do you autograph visualizations with pin and paper and then when you move to um to the computer which which kind of tools do you use yeah so apple pencil is the best of both worlds right like i use an apple pencil all the time for sketching and for doing things and it it feels organic but it takes me into digital space um we we we use a range of tools um if you're a data scientist most data scientists will be championing python or r or something like that you know those tools are used in our team and that's great for people who are learning those skills um but some people get by on excel because they're doing something slightly different you know um we use a lot of web programming skills for our visualizations things like d3 we use a lot um and what we do is we have a big library of those visualizations pre-created and pre-formatted for print online social media so that we can generate them very very rapidly if we use something like our visual vocabulary as a decision-making framework that we can go very very quickly from sketch to finished graphic we have another question um for the music project that you did and how did you get the idea to do that well that i that was one of my favorite things because music is a sort of slightly uh separate in the keyboard in the back there right like there you go um i have been doing some work which involved animating some us economic data that was how the visualization started we were we were animating some us economic data and then the video team came to talk to me about making a video about that action and the guy doing the video tom said you know what the big problem here is he said it's very just just watching this animation he said i bet you can't you know can you do a sound track um so i kind of experimented with could we use the data to not only drive the visualization but connect it to a synthesizer to drive some sounds as well i mean that we made that connection i was super excited because that's just classic serendipity you know i had absolutely no concept of what we were doing until we just got to a certain point with it we have a lot of questions rolling in and we have very little time but i want to ask one more questions and maybe and you can just look in the chat afterwards and maybe answer one or two more questions that would be wonderful um christoph is asking uh at the financial times do you differentiate between decision making graphics for politicians and informative graphics for a broad audience or don't do you do not make a difference between those two i think i think i mean we produce a lot of graphics i mean i did a i did an audit of this a little while ago and i think i came up within one month we made 800 graphics um and that even that doesn't include the newspaper or social media but just on the website 800 graphics and i think that's probably almost too many right like i think i think where we want to focus our graphics the distinction that we make is i want people to be slightly surprised by what we show them that's the big thing because i think that's a sign that if you want to inform decision making that you might be correcting some preconceptions for example right and that doesn't matter if you're a politician or just a reader trying to understand where the virus is spreading right like that you want to be at least partially surprised by the data and if all you're doing is showing people charts of things that they already kind of know um that's maybe those are maybe the charts to not make thank you thank you so much for your participation in the career panel and for your keynote and also thank you for asking all those questions that are still coming so a lot of interested here thank you very much thanks carolina and i'll do my best to answer any other questions people might have in the chat thank you so a round of applause for elin and our next speaker will be johannes arvila johannes arvila is an associate professor at the university of oxford and he currently focuses on the role of tracking apps in the corona pandemic and his talk will be on this today and we are very happy to have you here johannes and the floor is yours thank you very much um let me share my screen um so before i start i have to say i'm a big fan of of of graphics not just for sort of in in my newspaper but also in my in my work so i can already tell you that obviously academia has always been very graph heavy but this is going so much stronger recently and it's perhaps sort of it's a cycle that sort of people read it in the newspaper and they also want to read it in the academic papers but when i now see papers that used to have basically just tables now you often have the tables basically just in the appendix and the main story is told in graphs so i can totally see that i'm a i'm a i'm a very big fan of of of graphs and figures um to the dismay of my default students who always have to redo their graphs again and again um so today i i i want to talk about the step before the graph mostly in terms of tools and in terms of methods i want to talk about how we get the data and then also talk about the data we got um i have to warn you that uh today uh the internet at my house is sort of a bit dodgy so if i'm if i'm if i'm leaving the presentation it's not about you um and don't you worry i'll be hopefully back in a minute um but i hope it lasts for 20 minutes at least so um i want to show you some data about the acceptability of app-based contact tracing which we started in uh this project we started in march um and then at some point some some names that you that you might know frederick frauke and ruben uh joined us so it's sort of a it's a big international cooperation now um so when we started this in march nothing of the things that we've seen in the last two two months were sort of very much possible we knew back then that or sort of there were the first ideas that contact tracing apps would be useful or could be useful uh to help um ease the lockdown particularly if you combine it with a large testing program you could automate and and and make the um the contact tracing programs are more effective and if these programs are effective enough you could actually end the lockdown or at least loosen the lockdown obviously if you have a vaccine that's even better but that's sort of still in the future the problem was that that app-based contact tracing it was actually a totally new idea um it has never been used before at this type of scale it wasn't has been used a little bit on smaller scale but sort of on this really population-wide level had never been used before and it was clear from the get-go that the app take-up was very crucial so and i think we've seen one of the many graphs that ellen show was actually a graph that exactly came from this variety wyman an out paper from the Fraser Group in Oxford that showed that if lots of people use the app then it's really useful but if if nobody used the app then obviously it's not very useful and in in March nobody knew how how much people would actually download this whether people would like this type of idea or or not it's very it's relatively straightforward it's not easy as the UK has just found out it's not easy to program such an app but the harder part is then to convince 60 million people to spend one minute each to download download this app and to decide to download and to notice it at all so when we started our surveys we ran the surveys in at the end of March um there was no public debate about that and there was really no idea about what people would think about it it's it's potentially it's a it's a tremendous um privacy or threat to privacy um run by the government a government surveillance app on your phone something like that has never happened before in at least in western countries so it was really unclear what what people would think about uh what people would think about this idea so our target group basically and someone mentioned the target group our target group initially was really political and public health decision makers they'll we try to inform not so much the academic debate and not so much the sort of the public debate but really sort of the technical debate within the health ministries but then obviously because it's such an important topic and nobody everybody sitting at home and wants to know about covid we realized that this was spillover into sort of into a broader broader audience that's why we already started as soon as we had all the data we made them available you can find them on this on this website all the reports everything is there and you're happy to reuse them and build nicer graphs for example then um then we uh then we were able to do so the questions in our surveys was mostly about the future willingness to install such an app would you install such an app but the problem was that this was at a point in time when nobody had ever heard of this type of app so um it could only give a very rough indication of acceptability because um we could describe it in only in in in one way but really our sort of our aim was to see whether there's any chance at all I don't think we aim to get exactly the right number of downloads right sort of to really sort of predict what was going to happen but try to understand what people like uh or not like about the app think about the motives so that's I think that's the advantage of these type of of surveys or early on that you get a rough but a very early indication of of acceptability before any of this has been sort of programmed or or released the disadvantage obviously is if you do these surveys in March and now we look at this in June um the things will change over time uh the the stuff that we would measure if we ran the same survey today we would see different things probably we would see different things but also that ultimately decision to install is very different from what we could ask about which is something like the general willingness to install um but in the end you want to know what how many people will actually have it on their on their phone so that's something we we couldn't ask back then and you will I'll show you some extra data later on so comparing our survey data to the actual download data so this is what we find we run these first surveys in five country in the uk germany italy france in the us and um so you see on the y-axis this this is basically stacked histograms the the share of people uh let's say they would definitely install or probably install in green or then in red probably not install or definitely not install you see across these five countries it's actually pretty similar that about 70 to 80 60 to 80 percent uh say they would install such an app that's quite encouraging um in particular if you interpret this general willingness to install the app as an upper bound for the actual installation in in the end because there's quite a lot of things that even if you're willing to install they come sort of between the app and and so between your willingness to install and actually your the the app showing up on your on your phone we also find that people that trust the um trust the government less are less willing to install the app or they say they're less willing to install the app so on the left hand side so we had a question about how much do you trust government from uh fully agree to so do you trust the go sentenced I trust the government to do what is right most of the time people could fully agree to fully disagree to that we see that the people on the left hand side who say they fully agree with that are much more likely to say they would also install the app compared to the ones that fully disagree but you might remember from the last page that um we have us data in here and you might say well trust in government uh in the u.s is quite a different beast than say in Germany but we find the same pattern of behavior across all five countries obviously the level of trust is different um and also the UK and obviously the trust in government is also changing over time um but at that point in time the same we see the same pattern in all five countries so it looks very very similar this graph looks very similar across all five countries we also ask for motives um because this was still in the design phase of the of the app we also wanted to give uh information to to decision makers how to how they could design the app um so we ask for reasons for installing the app and against installing the app and people could click from a list of potential reasons they could click or they could also nominate other reasons if they wanted to and we see that first of all many more positive reasons are chosen and I don't show you that graph what I show you is the graph uh for the main reasons against installing the app and we see that the main reasons against installing are really to do with trust a bit like in the in the previous page the concerns about government surveillance after the epidemic um people 42 percent of our respondents say that would be that's one of their reasons against installing the app this is often people who will say they would install the app but it's still a reason against and that might be overwritten by other things but it's still a concern people have if you are worried about their phone getting hacked people might feel more anxious um uh that was actually quite surprising for me because I would I thought that um uh the app could give you a piece of mind if I know I haven't been in contact with someone and I can I can check every day um that that would be quite good but it seems that many people are actually quite anxious about or say they would be more anxious about this um in one of the servers in the US we also ask um we offer as one of the reasons um I believe that not enough people will download and in when we offer that but also people also say that that's an important reason if they think this doesn't work and they don't bother um installing the app or they might that might be a reason against it I think sort of I'm in the UK um you might have heard that the UK doesn't have such an app at the moment um you might have heard that the UK is struggling with responding to the pandemic in in general um uh so uh someone on Twitter said the other day because um so the UK had an app almost ready but then they postponed it and they say oh it's not coming first of June but it might come in winter but who knows someone on Twitter said um the government isn't able to organize a piss app in the brewery um so it's uh uh so which I found quite funny but sort of there's sort of a generalized sort of trust issue here coming again sort of trust not in will the government partly will the government um misuse my data down the line but also is the government able to pull this off in a in a in a good way and if if they're not um then uh I think that's bad news it gives sort of our our results would suggest there's bad news for the number of people who download the app now I said all these data I showed you are really from end of March it's really almost historic evidence I'm showing you today so how does that compare to actual behavior so you you surely know that Germany has um introduced an app um the Corona Van app about a week ago so these are the extra download data from uh from the Robert Koch Institute which I plotted as the installations as a percent of the eligible population so in Germany you'll have to be 17 or at least that says it has to be you have to be over 17 to download the app and you have to have a smartphone that has to have an iOS or uh Android um operating system and it has to be new enough so this is dividing the number of downloads by the number of people who could download it at all and that is actually also most comparable to the data I showed you before because we only surveyed people who have a smartphone and who are older than 18 so that's sort of the closest comparison and you see that in Germany over the last nine days almost 30% of the eligible of the eligible population have downloaded the app um we said in our survey sort of this very rough estimate that's about so going back to Germany is sort of um uh roughly 70% um so now you well there's only after nine days how can we predict the future really difficult we can look at Singapore because Singapore um introduced a similar app already in March so we have longer data for the for the download statistics for Singapore so this is the same data now sort of just on a longer scale because Singapore um has been out for longer so blue here is Germany and orange is Singapore but on the xx is still dates and since launch so sort of the the orange curve starts in March while the the blue curve starts in starts in June but you see where we have overlap it's it's almost bang on it's like the same people are downloading uh these apps so it's very very similar um now that this graph sort of waves its head really strongly at you to say that obviously what happened in Singapore will happen in Germany and I will be a little bit more careful about this um but you see that in in Singapore downloads have gone up it's really funny here that you would expect that there's plateaus all the people who are really into this all the people that read the financial times every day or really informed they downloaded early on and then sort of it sort of peat us out and you would think oh it doesn't go about 40 percent something happened in Singapore there's something that happened we think is really difficult to establish causality is that they uh loosened the lockdown and had a new wave of of cases and so suddenly hey you got went out to meet actually other people so suddenly the app is actually much more useful compared to if you just sit at home you don't don't meet anybody anyway and on top people started dying again and so people dying is really a good motivator it's obviously a very sad event um but to motivate people to do something uh that's really useful so um I'm not hoping that in Germany we have a second wave of of deaths but sort of you see that perhaps there's a chance that Germany over a long period of time um can get um many downloads but I think the best guess is more that we sort of end up between sort of a range of 30 to 40 percent just looking at these these graphs from Singapore. I want to talk a little bit about the um sort of about the tools and the methods and not just about COVID so in particular when you compare this if you charted this graph for France and Italy that have also released apps a little bit later than Germany they also have released apps the installation numbers are much much smaller so in Italy and France about two to three million people have downloaded the app so this is much lower Germany has about 13 million Germany's also a little bit bigger but it's perhaps a quarter of these these numbers. When in our study we found roughly similar amount of sort of similar values for Italy and France and and Germany so so how can we explain that and now this is really sort of speculation um first of all did we measure willingness to install correctly was something in our survey wrong or is something wrong with surveys in general perhaps people just told us what they thought we wanted to hear um so say a social desirability bias that uh our respondents didn't think about this at all they just told us what they thought we wanted to hear and perhaps they thought we want to hear apps are great and and and so they told us that even though they didn't think that's that that wasn't actually their true opinion. Perhaps our explanation of the app was different from um what people actually saw our um our explanation was neutral but we also highlighted the positive things um I think the the discussion since then in particular in France has been quite negative as focused mainly on on privacy not so much on what can it uh what can we sort of what's the positive side of this app rather about what could go wrong and that's rightly so because it's a new um uh it's a it's a new technology with lots of risks um but it's also with upsides so I think perhaps our explanation was more positive than the than the information that people had over time between March and and um and now but perhaps also we measured everything correctly it's just that the willingness to install has changed so surveys that have been run very similar service to ours that have been run in Germany for example find newer service over time find actually that the willingness to install goes down ever so slightly over this period of time perhaps because the epidemic is receding similarly in in Italy in end of March we were actually close to Armageddon in northern Italy the hospitals were sending people away and to die at home was a really really very tough situation and that situation has been has has been luckily has has improved very much but that obviously also reduced the urgency to to do anything about it and then obviously and I mentioned this already before there's a clear difference between what we can measure even in the perfect world what we can measure in these type of services is the willingness to install and there's will always be difference between that and the actual installation I think the biggest thing the biggest difference is the attention in our survey we chucked all this information into people's face and they're there and they basically have to make a decision I mean they can drop out but it's there and they have to they are aware that this app exists and they have to press a button and there you go and then they press a button well in real life you might see this on tv you might not you might never have heard about this you might not have a tv so it might be that you're not aware of this app and even if you're aware there's also 300 000 things that you also have to do today you have to do the dishes you have to do this you have your kids all kinds of reasons that will take your attention away from this so even if we asked you again if this popped up on your phone you wouldn't say yes I'm happy to install this app but you that might not be enough for you to go out to find the link and to do that and obviously it's costly to do that takes a minute or five or ten if you're older perhaps you don't even know how to do this you don't for me it's easy to say oh yeah you go to the google play shop shop and then you search for it and then you install it my parents might have a hard time with that um they might procrastinate and finally also I'm in my normal life I'm a I'm a behavior economist and I see that the just the general concept of the app is actually pretty complicated I think everybody can understand that if you stay at home during the lockdown if you stay at home you don't meet anybody it's really hard to get infected I think there's a really easy proposition to do and if the prime minister says they stay at home because if you stay home then nobody can infect you and you don't die that's something that is really very straightforward similar with face mask if I wear face mask I can't cuff it other people it's harder to infect others that's relatively straightforward to understand with the app is much much harder if I install the app nothing good is happening to me the only thing that can happen to me to happen is that if I already got infected I will still infect my first degree contacts but if I have the app and my first degree contacts have the app then they can go into quarantine early enough and I can perhaps protect my second degree contacts you see that's really complicated nobody of you has already heard of second degree contacts and so it's it's much much harder to understand there's lots of trust involved again um and so that's much much much much much harder as a oh I fully understand this and I'm I'm happy to do this honestly we are running a little bit out of time just yes yeah so that's the last that's the that's the last slide I said I just want to say that the so in these attitudinal surveys um I think they're very useful tool to get this early indication just what like we use them but you shouldn't expect that you will be able to fully predict future behavior um and that's what wasn't our intention um I think the data and motives should be more generalizable so I lied to you there's not the last slide this is the last slide so in your spare time do download the app they're available in almost all countries except in the UK and in the US um but there you go thank you very much thank you so much Johannes for your very interesting talk and your insights into the pitfalls of different data and we already have a question right on on the topic um Georg is asking um whether the install number or also um covers reinstallations or deinstallations and I would like to add um is it installations or is it really activation of the yeah because for the for the German app you have to um clicks through some some slides and say okay I accept um that my data will be used and etc uh no very good question so the the data I showed you is is the download data so this is from the robot conference web page um so this download this doesn't this doesn't doesn't correct for deinstallation doesn't correct for anything the data on from Singapore is from the website traced together and one of my worries is that this sort of increase here means something about the data quality which I can't track so I just say this is the this is the number on the traced together website they this is the number they give me but what it means about um working app installations um in in real life are to say and even it's even worse than that because even if you have it on your app on your phone and you click yes to everything where you have to click yes to you have to have the phone with you your battery has to be running and all kinds of other things have to happen so it's uh uh this is um yeah so there's quite a lot of things I have to go right thank you um another question um you mentioned that you were doing the survey especially from ministries of health and and uh politicians did you get a lot of feedback did people in office react to you um um well many countries installed these apps so I think that's I take that as obviously totally driven by our survey uh no so this is sort of partly um so this this ferretti at al study here um so full disclosure my wife is one of the co-authors on this that's how I got into this um she's the stake in our family I'm just a side salad uh doing the social sciences uh side of this they were working with NHS x the the department that developed the the app in Britain so in that sense we had them in in that respect um we were in contact with pet quite a lot when was you might remember um germany had a centralized app before they moved to the decentralized a system so we were involved with them quite a lot early on we were also involved with the the so we yeah so in that sense in the sense yes these data were taken up um yes we got some feedback but then on the other hand we were not the main main game in town I don't think they I think the data that I showed you is relatively self-explanatory um so um I um we were actually quite happy um about them about the impact in that sense so you see I'm thinking about academia we always have to think about impact and so I was quite happy about this there's another question um uh deliverance is asking how can we advocate to get the app in the US yeah good luck with that um so um so frankly now after three months I think both in the US and in the UK I think having a an app that is based on voluntary downloads I think is a very very difficult project now um because so many things have happened even in the best of cases and I think in Germany if you follow the public debate there was basically everybody saying this is an amazing thing and we totally need this is basically I've only found one politician who said she wouldn't want to have this but it's across the spectrum everybody said everybody said this is amazing it's the bonus leaguer whatever um while in the UK the debate has been rightly so actually quite uh quite quite negative about privacy in the US obviously all kinds of weird stuff is happening um so I think with this type of background um I think it's very hard to get high numbers of um of voluntary downloads I think my only hope for the UK and the US is the second phase of the Apple and Google uh installation I'm not entirely sure um whether this is coming but when they launched their their API they said in seconds that we'll just do the contact tracing for you it will just happen on your phone and then the the app the NHS app or the US whatever or highway just has to do has to be downloaded once you're infected you don't have to have an app penetration of 60 in the population you can download it as soon as you get infected so that's a totally different decision and I think that's the best guess for for that but we also see how difficult it is due to contact tracing you see it in Singapore you see it in sort of countries that were actually very very successful it's really really really difficult to contain a local outbreak you see it in Guter slow one of my favorite cities um is really difficult and this process has to be super super efficient and it's really hard to do it without an app and we we're seeing this and I think we will see this more over the next couple of months great thank you Hannes thank you very much for um presenting our research and showing the importance of sort of connecting these different datasets I was hoping we have enough time for Ellen and you to discuss how these graphs can be made prettier because now of course we all have conscious or not having beautiful ft graphs but we do have a graph here on the slide and I want to use the the closing sessions here for a moment to invite everybody to take the annotation feature of zoom which is either on the top row for you on the bottom in the menu and it's it's annotated and then you can pick the the use your mouse to mark the country you are from and so I never tried this let's see if this works with 60 people 70 people still on the call and I'll give you about two minutes and then we would like to close this out with a group picture before I let everybody go so but before we do that a big round of applause and everybody since no one is speaking feel free to like turn your videos off already sorry on already so that we can see you and see your class either with the yellow hands or in real time next time you Hannes we will get prepared to do a little voting on the fly survey and hearing who installed the app but I didn't think of this ahead of time so all right love to see I'm I'm still missing our Chilean participants and the Brazilians I hope you can figure out how to annotate I've seen you in the participant list so hope to see you soon on the screen here and and I do want to thank everybody who participated and because we had yesterday women in data science I do want to say that in our full blown connect program we had a lot of female speakers as well and workshop leaders and whatnot it just so happened that as we pared this down we ended up with two males which didn't you know I'm locked these two talks I'm happy we have them but I feel a little self-conscious on the imbalance that this created so while this is not an excuse at least be aware that we are aware all right well I love the signals coming in thank you everybody and I do also want to say that in November we will have mid-November sort of the second half of this event hopefully live then in manheim and we hope that you can find your way you are interested in coming admittedly November is not the best time to visit Germany weather-wise but flights will be cheap so you know it's always a trade-off with the data we have all right Annika I would love to hand over to you I know that you do want to guide people with the map but also with the picture taking so I'm gonna mute myself now and hand it over to you thank you everybody yeah before going into the break again we just wanted to take a group picture so maybe everybody could turn on the camera now and maybe Falki you can unshare your screen so that yeah perfect so I think you won't fit into one picture and I can say because we heard that data ethics and privacy is so important in both of these talks that not post this with everybody's name on them right so that just for this is also a privacy sensitive but in new york having you all in manheim at the group picture we at least thought we do this here and I guess Annika you go through three rounds of this with the three panels we have so yeah and while Annika is doing that I do want to remind all the present students and those that are incoming that at 6 p.m. after the break there will be a Q&A session with Karin and Kai and Olga to answer all the questions you might have with respect to the program and going forward and finishing up and changing and I do want to remind everybody that we have another info session on June 30th for those that are now in ties to hear a little bit more so I think we're done with the all right then I want to take one more big round of applause to all the speakers and panelists thank you for joining us today and I look forward to seeing all of you in person or virtually at one of the upcoming talks or in one of the courses many of the courses can be taken at standalone and Ellen I think we're all ready to take an internship at the FT including me so all right thank you all and have a lovely weekend
 All right.  Welcome everybody to our 2020 Connect event, Connect at IPSDS.  Some of you were part of Connect in previous years.  We had around this time a lovely event last year in Mannheim and quite sad to not have  you there in person.  It's a beautiful summer day today here, light breeze.  And you know, I'm glad that at least we get to meet online and I'm actually super excited  that we get to invite and host people who indicated previously that they can't join us  and now can because we are online.  So unlike my wedding celebration that would have happened next week, which needs to be  moved, this one has not moved.  And so welcome everybody to our 2020 Connect event.  I am excited to introduce all the speakers and panelists we have, but I realize you,  not all of you know me.  So let me say a few words about myself.  So if you know who is greeting you here.  My name is Frauker Kreuter.  I'm the director and founder of the International Program in Survey and Data Science at the  University of Mannheim in Germany.  And also I still the director of the joint program in the survey methodology for another  five months at the University of Maryland in the US.  And I was interested, many of you know that we love to train folks that work in the federal  statistical systems and around.  I do have ties to the Institute for Informative Research heading the other statistical methods  research departments.  So just a little bit of context.  If you want to reach me with questions about the program or what we do here afterwards,  my email address is right here.  More importantly though, and this is my cue that I tell everybody that we do love to hear  from you in the chat.  And for at this point in the Corona crisis, I assume everyone has been plenty on Zoom  and knows how the chat works.  But if not, press those three dots on the side or look for a menu item that says chat  and you can type in questions to everybody.  And we already getting a hello from Japan.  Hi, Pina.  Great to see you.  Obviously, there are too many people to introduce everybody, but I do use the chat and post questions.  We have Q&A and Karolina and I will be monitoring the chat window and will fold those into the  Q&A that we have later on.  So I also want to extend my thanks to the University of Mannheim and the Mannheim Business  School.  While they're not physically hosting us, they have been instrumental in helping us set the  event up and help with registration and the like.  And they all, well, both institutions, hope you will come visit in person when we continue  this event in the fall with its onsite portion, but also in the next year when we have our  usual summer event.  So let me introduce here briefly the agenda.  I will leave this up for a couple of moments so that you can see it.  We have, oops, sorry about that.  We are starting with the career panel where you will see one of our faculty members, Flora  and Koish, hosting current students in the program, friendly collaborators and future  collaborators for a career panel.  We try to make quite diverse in terms of backgrounds and look forward to that discussion.  This has always been part of our connect event and I'm thrilled to open this up to everybody.  Well, we created a little buffer, you know, often the Q&As go so long that it's helpful  to have these buffers, but after a short break, we will then have keynote talk by Alan Smith  from the Financial Times and second keynote talk by Johannes Avala from Oxford University.  So my special welcome and hello to both of you, Alan and Johannes.  And we'll have a closing discussion and then after another break, we have at 6 p.m. German  time, different time, wherever you are in the time zone, and information session for  all those that are planning to enroll in our master in applied data science and measurement  and that will be hosted by Karin Fursinger and our colleagues from the Mannheim Business  School.  And so, as I said, I'm delighted to have you here.  Anika, I'm not sure, is there a second slide deck I should be showing here?  I only have this one, if so, I'll stop sharing my screen and you can upload that.  Let me pass here for a second.  No, that was the right one.  Okay, perfect.  All right.  Good.  So, already ahead of time, Florin, I told you I will make the intro brief and so we have  plenty of time for the career panel and get to the interactive part.  I love to see all the...  Sorry, there were a couple of additional slides on this deck, I think.  Okay, see.  Sorry.  Let's check.  Oh, yeah, there are.  Okay, I did...  I thought that was a separate...  Okay, all right.  There you go.  I told you it's warm here, so it's messing around with my...  I thought this was the ending side, but this is the transition to me explaining what this  program is about.  So, let me take five more minutes for...  Paul's hope to explain the international program in serving data science as it started  and now the master program in applied data science and measurement.  We have been starting five years ago under the auspice of the Federal Ministry of Education  and Research.  They kindly sponsored five years of program development to develop courses in data science  probably defined, but for us with the clear focus on data collection and angle to the  survey world.  And we were fortunate to learn the online education part, what works, what doesn't work  way before the corona crisis hit and then have the opportunity to teach our colleagues  here in Mannheim and in Maryland a few tricks on how to do that.  And it's future the program will be under the label Mannheim Master of Applied Data  and Measurement housed and hosted by the Mannheim Business School and at the University of  Maryland it is hosted by the joint program in survey methodology continue with its name  international program in survey and data science.  While these two universities are sort of the pillar and the flagship, there are a lot of  faculty involved from partnering universities and University of Manchester for example is  a strong pillar.  Increasingly we're recruiting faculty from the University of Michigan in the United States  but also all the way down to Chile we have faculty member there who has been active almost  from the start of the program.  Our goal with these related faculty members and universities is to eventually have a partner  university on each continent and the possibility to enroll through these other schools.  We also have a lot of faculty in this program that comes from industry or industry related  I mean private and public sector but non-academic and you see here a list of them.  People change jobs so I feel like every time I look at this slide deck you know I feel  like oh wait a second this is outdated but I think this is at this moment in time actually  a correct representation of our participating faculty and you see the diversity reflected  here as well we just recruited two more people from Facebook that will be joining us teaching  in this program they have been helping us to develop and sponsor the program on the  US side as well.  What is survey and data science?  Not all of you know directly what this keyword means and for those a bit of context in the  data science discussion I mean you hear people talk about data being the new oil and this  and that they often talk about data that evolve through digitalization so through organic  digital footprints that we leave in our daily endeavors and last year at Connect we had  we had Roberto Ragon Bonn speak here and he designed this slide and beautifully captured  sort of the difference in these data sources.  On the survey side on the more traditional side and you know our speakers both of them  actually have used or are using surveys in their work we always have been collecting  data but in a very different way right we designed a data collection with a different  purpose we had experiments we had surveys and to some extent influencing what administrative  data gets collected and so what you know this program tries to do is to combine these two  sources with each other and take the best of both.  And Roberto whom I just mentioned has a beautiful project that does this combination in real  life and has become even more important that kind of spirit in the last couple of months  during the corona crisis and I'm talking about the price that index that they developed at  MIT.  Roberto is from Venezuela he got agitated when you know two decades ago he got the impression  that Argentina might not be reflecting the inflation scores correctly or inflation indices  correctly and that it could be useful to create an alternative indicator for inflation indices  for countries.  And so what he thought was well wait a second there are a lot of prices online these days  we could just web scrape these online prices and use the changes in time from daily changes  to reflect changes in the pricing.  And what's interesting here while not the whole market of goods is online the changes  in prices online likely reflect the changes in prices offline as well yet the information  is available much quicker and so that was sort of the spirit and the idea of his project  but as I mentioned it's a blending of two data sources because while they get the prices  online they still do it or use it in for most countries data collected from the national  statistical offices on the basket of goods that people buy and purchase and they use  sampling strategies to have a good representation of the different prices and so a combination  of the different skills that you would learn as a survey statistician and as you know what  you see as data scientist elsewhere.  Likewise statistic Netherlands has made its mission to be a zero footprint agency meaning  no surveys it's interesting what statistics agencies think of zero footprint but anyway  for statistic Netherlands it means no survey data collection ideally everything measured  in the background and hence our name you know data applied designs and measurement it's  really about how do you best measure what's going on in society and certainly right now  what's going on society you need measures fast and reliably.  The statistic Netherlands has used Twitter and in general social media sentiment data  to reflect consumer confidence scores and traditionally they did this with the survey  the survey data are depicted in the upper right corner here the Twitter scores are depicted  in the larger part of the graph.  Not going to talk in detail about this but what unifies us in all the courses and the  kind of framework that we do is a mindset that comes from the survey world if you think  of the way a survey statistic is produced you can think of a representation side that's  depicted here on the right when we worry about what is the population we can draw inference  to how well does our sampling frame where we actually take data covers that population  and how can we collect the sample what are the respondents do the respondents do and  how do we do adjustment to these sample data in stressful times like right now it's very  difficult to do population samples you know sometimes you have to collect data on the  fly and then make them work or you have to do shortcuts you know all that but without  knowing the framework to begin with it will be hard to get the inference right and unfortunately  we do see a lot of surveys that just take conveniently every answer they can get and  then post in major outlets that X number of percent of people are doing whatever measure  in the corona crisis.  Now similarly difficult on the measurement side defining construct deciding what's the  right measurement and what's the right response is something that requires some skill and  knowledge and we're trying to teach that.  If you think of that other set of data the digital footprints you often don't have as  structured of a process you don't draw a sample you don't know what the measurement is you  confronted with what comes in from many different sources and you know you people extract the  data clean them more or less although that is a big chunk of work that a data scientist  does and create a pipeline to load and store the data and you know then decide on how to  analyze them how to filter how to create features and all of that.  What we found in talking to practitioners and the faculty member from these other organizations  is that it helps to know about this other framework to get this process here right because you  still want to know which data do you not have what is missing where could they be doing  do you have the right measurement and so that's the kind of thing that we are trying to teach  you.  If this little briefing was fun there's a couple of literature freely available online  that I posted here and I encourage you to download and read up a little bit and but  this kind of framework might help you to understand what the folks later on on the panel that  are in our class courses have learned and will take with them into the future.  So who are these people who is the program designed for we have currently four cohorts  that we call them test cohorts that was more proposal writing right I mean they are really  our students and participants in the IPSDS and then an incoming cohort with the master  of flight data science and measurement and they really spread all over the world and  what's missing here from this graph are actually all those that are enrolled at the University  of Maryland then you would see Australia and Japan and you know a couple of other spots  in Asian Africa colored because they too join our online program.  We have been fortunate to be pretty diverse both age wise, region wise and gender wise  and that has greatly enhanced the discussions and I hope those students here on the call  that see this would agree that this work well.  In general just briefly for the course structure just in case you are interested maybe in enrolling  in some of the courses or all of it or get a certificate you can you know stay on until  6 p.m. our time and learn more on how to do that but in any case we have five modules  we extensively discuss how certain types of data fit a certain research question how  to think about data generating processes what do you need to learn to understand can I really  use these data are they of high quality enough is that something that works.  You learn how to curate and store your data how to manage your data and to be honest right  now you're doing this global COVID symptom survey with Facebook where people get invited  on the Facebook platform to participate in a survey and I have deep respect for anyone  who does sort of real-time millions of data points online I mean management not a small  feed and something that traditionally statistician or social scientists probably don't learn enough  what they do learn a lot is data analysis and that's of course part of the program and  so is data output and access and for that reason we have this visualization talk later  on here not going to bore you with the details of the different credit points and the like  will come back to that I just want to say that we have three text data inside survey  methodology and survey statistics depending on what is of interest to you and as many  online programs these days we have asynchronous and synchronous components or actually unlike  many we have also synchronous component there's prerecorded material their readings their discussion  forums and weekly online meetings as a package so you can prepare and then we assume have  always the chance to talk to these instructors directly and I love the online teaching because  everyone always sits in the first row and that's what I think makes the program so much fun  we make sure that there are small virtual classrooms that sit together in these 15 minute  sessions so unlike here we really can talk with everybody directly and don't even need  to use the chat so this set our kickoff event for the newest cohort and you know a special  welcome to all of the new students here is our connect event normally a two-day event  first day where we create a community of the fellow students second day where we have the  audience interested in data science and the series of speakers the speakers we have here  and will continue on with them in the career panel as I mentioned Alan Smith Johannes  Abla both giving the talks but on the career panel I want to highlight Laura mirror in  the show on from the University of Mannheim and Patrick and Rayman from SAP with whom  we organize women and data science in the previous years and yesterday and is becoming  more and more an integral part of the program and maybe will appear soon on the logo faculty  members that you saw on the industry partner connection line okay so I will not go into  detail with the admissions we talk about these pieces later on you can learn about the prerequisites  for those we try to make it as welcoming but if you are interested in those details we  have an online info session on June 30 years from 1 to 2 p.m. and you are also welcome  to visit our website or write an email to info.mdm at Mannheim and business or comm all right  so without further ado your questions to the panel now and over to Florian for the career  panel thank you for okay I'm going to share sorry I'm going to share my screen in a second  that always takes a little bit okay thank you so I mentioned I'm one of the faculty and  the international program of survey and data science my name is Florian Koish I have been  one of the first employees actually in probably in the startup lingo I would have been employee  number one of IPSDS when Frauke started founding the program I've been on work since in different  roles I'm now part of the executive faculty board and I'm also a professor at the University  of Mannheim today my role is all right and there goes Florence role I he was warning  us that he's in a place where the Wi-Fi might kill him and it just did so no more employee  one number one you can this spot is now there to fill someone else wants to join us are you  back yeah I'm back I'm very sorry yeah so you want to give it another try otherwise  you can do without the slides and since we have the panel online and yeah so yeah this  slide right so maybe I'm I stopped sharing the slide because okay so we have four great  panelists today I'm happy to introduce them to you first and then they'll have the chance  to introduce themselves a little bit more about their background and I want to encourage  everybody who is here on the call to ask questions to do so please post your questions in the  chat right if you have never used to chat that's the little bubble that the way you can  can post a question and I'll then relate them to our panelists so the four speakers the  four guests that we have here today is first and in no particular order Ellen Smith who  is the head of visual and data journalism at Financial Times so he's a data visualization  specialist who also writes the Financial Times popular chart doctor column he's also a highly  sought after speaker he gives lectures have had a couple of or had a TEDx talk called  why you should love statistics so write out write our LA and I also learned that he's  an officer of the order of the British Empire which is the first thing to me I've never  spoke to a spoke to an officer of the order of the British Empire so that's that's great  Ellen thanks for thanks for joining us next we have as Frau can mentioned Laura Marie  eating a chance she's a professor of corporate social responsibility at the University of  Mannheim her research focus are on corporate social responsibility on the communication  of CSR and management of CSR but also on innovation practice sorry innovation pricing  mechanisms and business models she publishes widely in academic journals and has received  a number of awards for her work third Catherine Redman Catherine is innovation lead Germany  Austria and Switzerland and next-gen innovation hub lead Southwest Germany at SAP but she  worked at SAP in diverse positions she's also highly involved and responsible for innovation  entrepreneurship and teaching and actually will probably tell you innovation and innovation  with purpose and encourage founders and innovators to incorporate the UN global goals is one  of her mission and then last but not least Martin Coleman he's data manager at the Swiss  National Education Panel survey called transition from education to employment with the acronym  three he has a background in anthropology economics and sociology and he has been working  for social science survey infrastructure for the past 10 years as a data manager for example  but also as a survey researcher and as Frau can mentioned he's currently a third-year  student in our IPSDS program so welcome to the four of you I invite you to unmute your  microphone so that we can we can start a discussion and I would I would really briefly like you  to all four of you briefly talk a little bit about your background maybe past jobs and  how you ended up in the job that you are right now and what your responsibilities are in  these jobs so that that your that our guests get a better understanding of what your maybe  potentially your day-to-day business looks like what type of data you work with for example  and these type of things so again we don't go in any particular order on my screen I  see Ellen next so Ellen why don't you get started thank you Florian firstly just it's  wonderful to be with you all today I'm very excited to hear what all of my colleagues  are gonna on the panel have to say I am head of visual and data journalism at the Financial  Times that's my official title that's a senior editorial post I'm an executive editor of  the Financial Times and I guess in in in a career panel sense the thing that's interesting  about that job title is that it did not exist 10 years ago you know it did not exist five  years ago that this is a you know the idea that we now have data in the newsroom of newspapers  is something that I'll talk about later in my main talk but the role is new it's focused  on data in the newsroom and data for storytelling the my journey to that role has been very  interesting because I was very interested hearing fraca talk about official statistics  because that's my real background in in career space I joined the Financial Times about five  years ago after more than 15 years at the UK office for national statistics and I think  the observation on that is it's an unusual career trajectory to go from the civil service  to the newsroom but that says a lot about how these career paths are converging in the  field of data science my actual academic background is in geography my undergraduate is in geography  my masters is in geographic information systems so I've always been very much interested in  how information is presented visually which on fraca's five modules that she showed is  obviously sitting proudly at the top of those five modules is a final what Hans Rosling  would call the sort of final six centimeters of the journey just into people's eyes so  I'll pause there thank you thanks Alan so why don't we go to the next person on my screen  and that's Laura Marie great to have you yeah thank you so much for having me as a guest  I have to say I'm a big fan of focus work and also of the women in data science events  so happy to be here and my name is Laura as Florian said I'm a professor of corporate  social responsibility at the University of Mannheim also position that did not exist before  and that way my background is that I studied economics and business management and then  I did a PhD in experimental economics and I collected primary data in Indonesia running  experiments on ultimatum game bargaining and when I was traveling in Indonesia I witnessed  a lot of really the horrific impacts of our globalized markets on developing countries  including working conditions that are looking like modern slavery is severe pollution exploitation  of the natural environment and so on and when I finished my PhD in 2011 I decided I really  would like to pursue an academic career and look more into the topics of sustainability  and corporate social responsibility and I plan to use the methods skills that I had learned  in my PhD running experiments but applying that to this topic in management and my direct  surrounding my supervisors they all said don't do that bad idea you're never going to get  a job and at that time luckily I didn't listen I was really passionate about the topic my  doctoral supervisor gave me the advice that if I really want to do it I needed three things  passion perseverance and good data so I set out to find companies to work with me and  to collect data in companies and one of the biggest projects that I did and I have been  in contact about this project with Prog in the past was a data collection at IKEA Germany  I did a panel data collection with 240,000 IKEA family customers over eight time points  so one year and I was granted a lot of opportunities by IKEA for example to manipulate the IKEA newsletter  between subjects to include subsequent surveys to interview the store managers and so on I  got to media tracking data and so on so I also combined data sources from data sets from  various different sources and started to look at them and started to learn a lot about new  methods longitudinal analysis cross leg models and so on yeah and that data set actually really  gave me the opportunity to publish in these leading top journals which then was my entrance  ticket into the academic job market another thing that happened through this big data  set I really got a lot of awareness in industry and a couple of other companies asked me to  do similar things so since then in 2015 I got the offer to come to Mannheim and since  then we've been working with a lot of companies to understand better the topic of sustainability  expectations of stakeholders reactions of stakeholders we also have a lot of links to  the social entrepreneurship and the innovation community I also met Katrin a couple of times  before so yeah exciting job I'm super happy to have pursued this path but it's probably  not the most obvious career path in academia great thanks Laura next on my screen is Katrin  yes very warm welcome also from my side for the whole participants and also the panel  companions here thank you very much for the invite Frauke I really love to be here I'm  I think not the classical data scientist so I'm more the user for data data what can you  do out of it at SAP I'm responsible for the university alliances so I'm very interesting  interested in innovation in exactly such career paths that we listen to here in order to connect  dots them so I'm doing this at the moment as a platform as a personal platform me myself  but I'm very interested in how can you leverage networks founded on database how can you combine  people projects future projects how can you involve trends and so on so we heard yesterday  a couple of interesting new yeah also new proof new jobs like decision strategies and  I'm very interested in in yeah developing the whole thing so I collect data on my own  but I really should do that now because the quantity is huge and the quality also yeah  leverage my network by database so if somebody has an idea here I'm very keen and hearing  more from you in my main role at SAP I'm responsible for the global innovation portfolio  my role just recently changed and I have another connection dot with with this panel here I'm  studying since one year now at the Mannheim Business School beside my job and yeah it's  quite stressful time but I learned a lot about analytics people analytics and all the things  that Mannheim Business School is providing as tools and strategy supporting methods  so I'm keen in applying them soon when I finished in November thank you great thanks  Katrin great to have you and then last but of course not least Martin and I have to give  a special thanks to Martin because he's joining us although he just became a father of twins  so he's sharing parentable responsibilities here and so found time to join us so thanks  Martin for coming yeah if you hear some screaming on the background it's not because I have  somebody locked up in my closet that's my new born girl they'll be complaining but  they have to be pretty good today so I'm hoping it will be fine yeah thank you for the invite  and thank you all for joining I'm very happy to be here after three years of studying  I finally made it on the career panel as the student representative so very happy I could  say about myself I probably have a more kind of traditional job when you think about you  know working for a survey infrastructure and I kind of rolled into this job but while studying  anthropology because many of my my professors had ready wild ideas and plans but they were  not so savvy on the technical side usually and I started to kind of help them out like  turning some of those ideas into practice and you know doing kind of data management  on the side as a student and I think that you know over the years I've worked for many  different projects I worked for the European Union and run some of the surveys for them  and the more the further I got the more complex the surveys have become and now the survey  that I work for is so complex that you really need a really dedicated team to kind of like  even manage the data like if we've very far away from having like one kind of data collection  event they have several events over several modes and there's like a lot of like data  addition that's being done with like secondary data of course so just just working for one  survey can be quite complex and taxing and I am very happy that I found this program  it has taught me a lot and yeah and hopefully I can continue learning from it it's been  a great resource for me personally.  Great thanks Martin.  Again I want to remind everybody that please feel free to shoot questions at us and I'll  forward them to the speakers but I've organized I've tried to organize this panel a little  bit ahead of time of course and thought about like okay what could we talk about and one  thing that I would ask our panelists to tell us a little bit more is kind of like how their  day-to-day work looks like and also what type of skills they need to do this like and where  they feel like where have they learned them is this something to have like self taught  them maybe where the designated courses this is something they learned back at the university  or did they learn something completely different and had to relearn everything once they were  on the job so maybe a little bit about that and again I'll start with Elin here.  Yes thanks Florian I mean it's fairly easy for me to answer the question of you know  did I learn my skills at university or did I learn them since I left you know I am so  old and ain't that when I left university the internet didn't really exist so as you  can imagine there has been a lot of learning for me to do away from university and I guess  actually for me that's a really important challenge for everyone you know I graduated  in a recession at a time where our main technological vehicle at the moment didn't exist and similarly  students graduating now are going to be graduating at a time of huge uncertainty and you know  the most valuable skill that I've carried with me I think from university is this sense  of wanting to keep going right like in terms of you never get to the end of a learning  journey and if you think that your learning journey stops when you've hit the four or  five modules and you've ticked those boxes then you know careers are never normally  so neat.  I also think that the thing I've learned since you know you asked me I think one of the discussions  we had before the event was you know what skills did you have to learn after university  and I couldn't really code when I left university not really I mean I did a little bit of turbopascal  right but the problem with being taught turbopascal in an academic environment in the early 90s  was what's the context what is the motivation you know like teaching turbopascal in the  early 90s involved teaching people how to code an alarm or a currency converter and  I could see no real practical application of that to what I wanted to do was make maps  right like my cartography that I did at undergrad was all with pen and ink right like pure manual  cartography.  The big change for me came when I learned how I applied some of the amazing technology  that was arriving with the internet to things that I love doing which is to code to make  graphics and suddenly I had an incentive and a purpose right so abstract skill sets can  sometimes difficult things to learn in a pure academic setting when you don't know how well  they're going to be used in your onward journey.  But the other thing as well is just I know we talk about these things called soft skills  all the time right like everyone talks about soft skills that's not a very complimentary  phrase soft skills it kind of sounds a bit optional right like the soft skills are actually  some of the most important skills that I've learned in the last you know couple of decades  and that includes it's not just how can you stand up and do a TEDx talk but it's also  can you go and talk to an editor and convince him or her of a story idea and why you think  it's so good you know can you can you get people enthusiastic about an idea can you  know can you listen as well as talk that side of things and so I've tried to develop those  you know soft skills I will call them but I reluctantly I will call them soft skills  try to develop those as well as the technical because they will enhance each other so for  me it's been you know the university side of what I learned was very good at understanding  you know I think I left with a fairly good basic data literacy and a sense of my own  domain knowledge in the field of geography but the world shaped so much more afterwards  one one side left and when I did my masters I did my masters while I was working at the  official statistics agency and the experience of doing a masters alongside work was transformative  right that was so totally different to my undergrad because it was much more applied  much more focused on particular questions that I was interested in exploring and allowing  me to focus.  So that's great because we actually got one of a couple of questions and one is directly  to what you just said is how did you transition then from official statistics into into the  world of media and and what basically is there anything you learned there that do you know  I mean I guess there's a lot you can apply now in your job but also kind of like what  are the differences maybe could you elaborate on that?  Yeah I mean it's a really good question and I'm mindful that I'm being ordered and there  are you know nearly a hundred people but I'm going to be quite candid in my response to  that because it's a very good question right how did that happen and why did that happen?  So I already realized that my work at the statistics office was I was working with data  visualization at the statistics office and the reason that I was doing that work is we  started I think in official statistics to start to there was a fundamental change in  the role of official statistics agencies happening and I think it's very relevant to data science  which is historically official statistics agencies thought of themselves as number factories  their job was to produce numbers in a factory sort of kind of workflow and then to just publish  the numbers to leave the numbers outside the factory for other people to then you know  to collect the numbers and go collect. When the when the web came along I think it started  to question some fundamental principles of official statistics and you know official  statistics started to rise oh hang on we need to use the internet to communicate and very  quickly this phrase dissemination this word dissemination appeared official statistics  agencies started saying oh we're going to disseminate our information again I don't think that's  a very good word for data because it's an agricultural term and it's the one where basically  a farmer can throw seeds into the field and not particularly care about where they land  some of them will grow some of them won't. What we increasingly started to realize is  that we actually needed to turn into much better communicators in the world of official  statistics like you know the insight that you collect as part of official statistics  methodology that's valuable insight that should be shared with the world and we wanted to  do as much as we could to not just connect with the traditional policy makers and business  decision makers but with a much wider group of people you know interested citizens and  so that's where that work started from and that changed our view of what success historically  success for statistics official statistics was nobody complains that was a great release  because nobody complained now the easiest way to make sure that nobody complains is  to make sure that nobody looks right to not advertise yourself particularly well but actually  we changed our mindset and we wanted people to look we wanted to generate debate and discussion  and so therefore I started to talk much much more with people who were working in the media  and we started to syndicate our content to the media which was an interesting thing and  eventually talking to some of those people in the media led to them saying well look  these skills are very valuable that you've learned at the statistics office would you  like to come and work in a newsroom doing them and so that's how the journey happened  and the biggest difference between those two environments is simply speed so rather than  ten years to do a census I usually have 24 hours to produce you know quarter of a million  newspapers but you get the idea that this the processes are very similar but the speed  is slightly different.  Yeah great thanks that's that's very insightful I want to move on to Laura you already mentioned  a lot of the skills you have learned as part of your PhD and working with this type of  data any other things that you feel like well in the past couple of years this is something  that I can't live without anymore these days in terms of the skills and also maybe you  mentioned this already can you talk a little bit about this intersection between academia  and industry and how you made this decision of pursuing a career in academia but it sounds  like you still have a lot of like connections with industry.  Right I also just wanted to pick up the question from Annika from the chat how did you choose  your industry academia versus corporate and for me this was actually a tough decision  because my main motivation was to change something in the business world and I had exactly that  question where can I be more effective going into companies or staying in academia and  my decision actually was based on the rationale that in academia I was I would have the maximum  freedom and the independence right so if I would be in one company I would actually have  to work along the lines of the agenda of one company and if I'm in academia I'm independent  and I can freely choose the topics and my position on the topics but I also had the idea that  if I go to academia I will really want to build strong bridges to the business world  and actually engage with the real world out there and not just be an academic that's you  know running data analysis and publishing papers behind closed doors.  In the last five years in Monheim I've actually spent a lot of my time building these bridges  trying to collaborate with companies and also talk to companies not only the ones that are  super sustainable but also trying to understand you know the processes the hindrances and  motivations of companies that are not really transitioning so really trying to understand  the struggles and go into a dialogue there.  And yeah you said so what are the things that I needed on the way I also like Alan I come  from the past where in university we learned toro Pascal we actually programmed with pencil  on paper in the exam and I had to learn everything basically that I need today by myself and  as Alan said it's a continuous journey right you always learn new things along the way  and I think the best thing is just to have a problem and then learn by trying to find  a solution right so really working on the stuff and by working on it learning how to  do it when I did my master thesis I started to do structural equation modeling then during  my PhD I used experiments and later on longitudinal analysis and when I started to learn about  data analysis there was actually nothing like YouTube tutorials or anything right so I really  had to go to the library get a book from the shelf and read a book so I mean all of you  guys out there that are now studying these things I think you are in such a luxury situation  that you can just use online tutorials and videos and MOOCs and everything like that  so I think that's such a new world of learning and I totally enjoy it now when I want to  actually acquire new skills and about the soft skill question so I think as an academic  that is trying to build these bridges to industry for me it was super important to actually  have these soft skills of trying to understand both worlds right because academia and the  business world are usually very separate and there are not so many links and if you ask  practitioners why don't you work with academics they say oh you know they are they just interested  in theories and not in the real problems and on the other hand if you ask the the academics  why don't you work with a business world they also have a lot of stereotypes right and say  you know the practitioners are not interested in gaining knowledge they just want to solve  a talk problems they want to increase profits so I think there is a lot of potential but  you need these soft skills of really understanding the different stakeholders and trying to bring  different parties together to to build these platforms and that's I think the strongest  skill that I had to learn and again there I think I'm still on the journey of learning  from university I have to say I don't I didn't get so much that I use today I also got the  maybe the skill of perseverance having big lectures of 1000 participants sitting there  trying to figure out how to solve the exam but everything basically that I need on that  I need on the job today I had to learn by myself but I have to say that I had a very  passionate doctoral supervisor and he really taught me this passion for research and I  think if you have that then you know you have the right intrinsic motivation to also go  this way right and find the necessary information teach yourself how to solve these problems  and I think this really needs a lot of passion for problem solving and just you know not  being scared of starting just getting started and just trying to do it.  Yeah and today what are the things that I'm trying to learn I if I have problems today  that I want to solve I use YouTube videos tutorials MOOCs and so on for this summer I  have a long learning list again things I would like to know more about and I actually want  to use a couple of these really cool upcoming new online courses for example to learn more  about Python and I'm also very lucky to be married to a computer scientist who's also  teaching me a couple of things like programming machine learning skills and so on so I think  it's really a continuous journey and I for me the biggest learning was that I need a  problem and then I you know if I have the passion to solve the problem then I will find  the solution and the resources are getting better and better to solve these problems.  Thanks Laura I'll move on to Catherine because you mentioned already a couple of things and  especially I think it's a great transition because Catherine as you told us you're in  charge of the university alliance so kind of like this this this intersection again  between academia and industry so maybe we can tell a little bit more about that but  also from a personal perspective what made you come back to now the modern business  school and make this decision to after potentially being out from the university for a couple  of years pursue kind of like another degree or learn something new.  Yeah it's quite a couple of years you're right so the first two days were a really interesting  journey for me I thought I will never stand sitting nine hours here in the audience lecture  hall but yeah finally I was perceived variant enough to do it for one year now already and  soon I hope that I will have finished it because it's quite the double with family it's three  columns that you are at the moment serving us three pillars and that's quite exhausting  I must say so I really need the vacation afterwards because you don't have any weekend it's really  beside your full-time job so I think that's exactly what what you said Laura it's lifelong  learning I think that's the most important thing and yeah so find your I think the most  important item you have to find out is find your your strengths and and your and stick  to it yeah don't let you be influenced by people saying you will never learn that you  will never be able to do that if you have the impression that it's the right journey  just go for it because you only can't be you only can be the best the most the best person  in in something if you really have your your own confidence in yourself and and know that  you can do it so I think lifelong learning find your strength and safeguard your your  values and energy after afterwards when you when you find something that gives you energy  that gives you success then try to to elaborate on this and on this area and try to be an  expert in it so that's something yeah also to have a like a break and not only run and  go forward forward forward but also take your time and if you have the impression you can  serve to something or you can learn a lot in one area then stick to that and try to be  even better in it so then comes I think because I have a quite long experience also now a  professional wise there comes times where you have to reinvent yourself that can be if  you give birth to a child also Martin I think he will recognize now that there are things  becoming more important suddenly than your professional career it can also be negative  things and now in corona times I think there can be more and more people and families also  affected by things they never would have thought that happened to their families so it's something  like resilience try to to take life and also of course professional challenges like they  come to you and not try to to to fight against that sometimes you have to stay with a flow  and and make the best out of it even if in the first glance it's really you think I know  why does that happen to me now there's always some sense behind I think and that's why I  think why you need to be courageous innovative yeah make like resumes about yourself how  went the year and what do I want to to go further I always always do mind maps about  myself not because I'm a narcissist or something and it's only to to have a point in time where  you can where you see okay that's my that's my balance sheet at the moment and I have  these goals how can I develop further myself also and if you have children you know that  you have to do it in a dual way you do it for yourself to get forward and to learn more  but also for your children how can you motivate them to think about themselves also and not  only to stick to school grades and and learn the stuff in school but also to learn things  around the school and the education items and that's that thing I always learned is  what I live and that is something that combines a lot of people here in the audience and in  the panel so shaped your future because it's your future yeah that's why I love now to  to teach and I wanted to become a scholar professor when I left school and my parents  said no no there's no chance to be a professor there are too many and not enough jobs don't  do that so I choose another thing I became a bank clerk first what was completely wrong  for me afterwards I discovered okay even something that you didn't like like a bank formation  can help you later because now when I counsel startups or mentor people there's always the  balance on the bottom between cost and revenue is always filled in because I know how important  that is to convince investors or people who can help you so shape the future in a co innovative  way and mix teams that's because I'm a design thinking coach since 10 years and I discovered  that linear planning you can do it but so you have to revise your planning often so I choose  now the way to be iteratively planning and look what comes to me and then try to integrate  a new project and less than you have of course drop other projects that are less vivid so  in the iteratively planning and not only linear that's very important you can't apply that  to everything that you need to play both methods of planning in my view and to the intersection  of industry and academia I'm also glad that I got this job in six years because it's exactly  what I always wanted to do combine the bridges like Laura also said and focus also doing  this every day so not only staying stick to your world where your comfort comfortable  but also look 360 degrees around yourself your world and try to combine other areas  in order to get innovation disruptive ideas and teaching at universities that gives a  lot to me because I learned from you from the younger generation how you solve problems  how you think how you capture your we have that already how can you gain new knowledge  and it's really completely changed from the way I learned at university so I'm keen now  at the Mannheim Business School to learn how my peers there which methods they have to  capture knowledge also and to gain more insights and at SAP I can really say I'm an entrepreneur  I have like my little own business insight SAP university life so it's also possible  to free yourself with a couple of years that you have experience and know people and yeah  be your your own entrepreneur even at a big corporate so I'm looking forward to your questions  thank you so thanks Catherine I also want to before we we already got a couple of questions  that I want to for the right way I also want to briefly give Mark Martin a chance to also  tell us a little bit about his story why he decided to for example start at IPSDS and and  what were the motivations there what were the why did you feel you needed something to learn  more like what what what was missing more or less in your your life that made you go through  that right I had too much time I needed something to film my time and now I have twins though  I mean I should stop now funny enough it's it's has something to do with what Ellen called  soft skills which I also I don't like that term either but I think I was hired in my  team to be kind of a conduit between our IT staff and our academic staff and I during  my time there I realized there is a ton of miscommunication going on all the time between  and if they go back and forth between design and implementation and then you know you lose  so much time fixing for mistakes and miscommunications and I saw this program and thought that it  really fitted that bill you know it's it's it's it's kind of like in in the middle layer  although I still think that maybe it could be a little bit more heavy on on information  science for my taste but it's it really has a useful resource for me to think about also  developing those soft skills in terms of how do you communicate between those different  aspects of my work and you know it another thing that I think is really great that I  want to impress on young people thinking about taking this program I mean I I I the resource  is not only very very broad and very interesting in a interdisciplinary aspect but it's also  extremely flexible which I now extremely appreciate I have like little hours in between  the day like at night or whatever and I can just always now take some device somewhere  in my house and do you watch a lecture video or do some homework or whatever it's it's great  to for that as well so it's easy to fit in my in my in my day but yeah I I think just  this kind of like interdisciplinary kind of aspect of it was really kind of key for me  because I was looking at other programs too of course you can go deeper into statistics  or you can go hardcore information science or something like that but I feel like this  program has a good balance between the two thanks Martin so there's already more questions  coming in so I'm ask our our panelists to answer them so one one question came for Laura asking  how much interest is there actually in the business sector and using accurate and reliable  data for decision-making on strategic priorities such as the sustainable development goals  so you have worked with a number of companies as I take it so maybe you can stay a little  bit about that yeah I think that's a very interesting question so and I'm a little bit  ambivalent so I can explain what my experiences are at the beginning of course there is a  lot of interest from the business world because they know that they on the one hand they know  that data analytics are important on the other hand they have to start to report about sustainability  since 2017 we have a EU regulation that tells companies that they have to report on non-financial  performance so everybody wants to explore ways that they can use data on understanding  their social impacts and environmental impacts better on the other hand I have to say that  if you start talking to the companies about the specific processes and the necessary steps  then you have to do a lot of explanation work right because there is this diffuse idea of  what you know a scientific study is going to look like but if you then go into really  working on it of course you have to yeah you know go along a lot of ways that take time  good science takes time you have to do a lot of explanation why you need different data  sources why you need a good measurement for example why you cannot go just with a single  item or in some cases why you need objectively observed behavioral data and so on and I think  this explanation work is really important and understanding the other side and telling  them you know this would help us to understand that in a lot of my collaborations I had to  invest a lot at the beginning to do this explanation work but then if the companies see what they  can get out of it they really get interested and they are willing to go this long away  and doing the work for you know good science and I think that sometimes it makes sense  to go step by step right because what the practitioners often need is some quick wins  that they can show to the management so they need something like numbers to show okay look  at this this is great we need to go further and as a scientist maybe sometimes we think  that you know we have to produce the perfect results first and then after one year or two  years we can publish that in a scientific journal and I think for the collaborations  with businesses in many cases it's important to really give them these quick wins and to  do a little study first and show them you know look at this this will help you to explain  to your management that you have a positive social impact one example in our field is  really social impact measurement and a lot of companies don't even believe that this  is possible they know that they have a lot of data but they say you know can you actually  measure social impact and then when you start to work with them and you give them little  quick wins and say look at this data you can go to your you know to your manager and show  this a couple of slides then they are really motivated and say okay how can we do more  let's sit down and I think that's that's important in the process that you understand  that the other side needs to learn what what it takes to produce valid results right and  that's the explanatory work that we need to do.  Yeah great thanks so there's there's somewhat a related question came in for for Ellen on  this relationship or the role of the new or big data sources comparing that between what's  collected in the statistical agencies and in the national statistical offices and then  now on the other side for for the financial times right and in the media so do you see  a shift happening there also from from the most traditional players such as the national  statistical offices using them or do you think there's still like a big difference in how  and what type of data the different places are using?  Yeah I mean wow there's a lot to unpack in that set of questions I mean the first thing  that I'd say is that I left the world of official statistics just as just as this real interest  in alternative data sources started to become not just not just an opportunity but also  a pressure because there's an assumed knowledge now I think a public knowledge that there  is data out there and the longer that the world of official statistics didn't use it  the more questions there would be and but of course it does bring with it a whole question  of the legal framework for doing so how do you prevent this becoming like the wild west  of you know having spent years trying to get survey methodology workflows in official statistics  organizations you know as as well drilled and as efficient as possible how do you introduce  this chaos of data that was not collected for the purpose of your survey to be used  for your survey you know that kind of thing and I think it's been really interesting for  me personally to watch that develop so much in the last few years from a slight distance  so you know if you go to my old UK official statistics office you know they now have a  very well established data science center that did not exist five years ago and so to  see this investment in official statistics being able to leverage these sources of data  I think is tremendously tremendously useful and in fact you only need to look at the current  crisis to see how valuable that has been because you know the UK public official statistics  have been super responsive during coronavirus to be doing all sorts of really interesting  things on on much tighter timescales than they are used to do to doing and I think the really  interesting thing in the slide that Frauka showed earlier was this this complementary  set of sources allowing you to use two very different techniques to get two very different  angles on data you know for from using sources like big data that are maybe less good at  answering some of the absolute questions but very quick and responsive and relative right  and positioning those together is quite a powerful proposition I think the one thing  I would say from the Financial Times perspective is obviously I'm working in a sector now where  the commodity the end product is not the statistics the end product is something slightly different  and we are obviously in a world where we are trying to play our role in the reporting that  we do about kind of responsible capitalism about about making sure that the direction  of business is is sustainable and responsible and you know things like the ethics of data  is an absolute story that we are you know partly involved in in telling you know we  we have writers who come in and do pieces that I mean people like Shoshana Zuboff and  that the surveillance capitalism space these are things that our readers are tremendously  interested in you know the ethics of data and the which data is starting to become you  know it's it's a bit of a cliche to say it's the new oil because there's lots of reasons  why it's not it's something very different but we're working in a very very different  space to the official statistics agencies and my biggest my big lesson when I left the  world of official statistics and arrived in the newsroom was to realize how lucky I was  for the previous 15 years working with teams who had such who always gave me very high  quality data to work with that was well formatted that was well documented that had a methodology  that was explained because the use of data out in the world where I work now it's just  generally messy from the start right like and it's just half of the job is working out what  you can use and what you can't and that kind of evaluation of data quality particularly  when it's data that's not come from official sources is a huge challenge for us I'll just  finish with one quick example I had it's not uncommon for this sort of thing to happen in  my job right you will get an email from a PR rep who will say we've got some exclusive analysis  on property prices in the UK and we've got some shock results about which areas have  gone up the most and it's not where you think now my you know my my sort of first guess was  it's probably London where the prices have gone up the most and I looked at the data that they sent  as part of this press release and there were no London places in it at all and I thought well  this is really crazy so I better ask what they're doing here and all this agency had done had been  to take an official statistics data set on property prices and remove the London locations in the  data to make something more interesting and then repackage that data and push it on and so like  here in things like this you start to see how misinformation spreads that there's this kind of  a very badly policed area out there where I think actually you know the world of official  statistics is obviously a great reference point for best practice and for the right way of doing  things but it shouldn't stop there right like we've got to make sure that our society is functioning  along data literate lines and I think that's a combined effort I'd like to think that both  official statistics agencies and organizations like mine would all see a part in in resolving that  yeah so yeah thanks thanks Alan so I see I see more questions coming in especially for Alan but  we will hear more from him very soon so at this point looking at the time I would really want  to thank all our participants here the panelists Alan Smith, Laura Marie, Eding Ashons, Katrin  Riedmann and Martin Komen, thanks for taking the time to talking to us let's give if you can like a  virtual round of applause and I believe the next time point is that we take a short break and we  will be back at 4.30 for the talks I believe Frika anything else we need to say at this point?  No that's it Anika will advise us if there's anything she wants people to do here but mostly  we wanted to give you the chance to go to the bathroom without your cell phone while you're  listening to us or make yourself a cup of coffee and then be ready for the talks and  questions and discussions afterwards and my two heartfelt thank you to the panelists I  always love the career panel and every time I'm learning so it's never end thank you all  thank you Anika can you say a few words say you're on here  yeah we'll share the program during the break so if new people come in they know where we are  and yeah but that's it I think everyone else can leave their videos on or turn them off whatever  we'll see you back here at 4.30 thank you all  it's good to have you all here and it's wonderful to have Ellen Smith here who will do one of the  two keynote talks now the first one is by Ellen and the second one will be by Johannes Abelo and  we will have a quick Q&A session after both of the talks and I will moderate this with together  with my colleague Markus Heckerlotz from IPSDS and it's wonderful that you're here and it was  already interesting to hear all your your insights into your career path and into your career at the  financial times and your talk today will be about data visualization and journalism in the age of  coronavirus and I do not want to steal any time from you so without further ado the zoom floor  is yours thank you Caroline and let me just share my screen so that you will stop seeing me in my  spare room and hopefully see something a little bit more interesting which is a presentation here  so I hope you can see my screen okay and you might be thinking that with a talk title of  data visualization and journalism in the age of coronavirus why would I start with a picture of a  cat sitting in a tree well this kind of takes me back to my early discussions about data journalism  when I was still working in official statistics and I was very skeptical of the field of data  journalism I was at a conference in Australia with an economics commentator from the Sydney  Morning Herald Ross Gittins and I said to him Ross do you have data journalism in Australia  and he said oh yes yes we have data journalism in Australia now Alan he said because rather than  report that the cat got stuck up the tree we can now confidently report how far the cat got stuck  up the tree and so journal data journalism for me this was very much my impression of what we  were talking about with data journalism is that not much of it was really data science that a lot  of what we were seeing was the spurious use of numbers to try and kind of inject some sense of  analytical work that maybe wasn't necessarily relevant and the reason why I kind of start with  that is that even with the coronavirus and even at the FT the very early reporting on coronavirus  was of that sort you know these are not quite the cats in the tree but this is what tends to happen  when the news cycle is happening very very quickly and information reliable information is difficult  to to come by the the little information that you have and in this case it's the number the  daily changing cases inside China this was from very early in the story in January was almost  the best that you could do in very very sort of quick turnaround circumstances and unlike a lot of  other news agencies I think we're very aware that when we do something like this it has a very much  a limit in terms of how long that sort of reporting is going to be able to be uh carrying a story so  you know I think throughout the coronavirus we were seeing a lot of news agencies just throwing  data onto maps and charts in the hope that some of it would stick what we realized very early on  with coronavirus was that actually this is an opportunity for data journalism to really come  into its own in the Financial Times newsroom and so we set up a page at the FT specifically to track  the data that we started to collect from places like Johns Hopkins University from  the European Center for Disease Disease Prevention and Control and lots of data sources that as we  were starting to work with them we were assessing are these reliable sources of data and if if one  source of data on its own is not reliable enough for everything how are we pulling together data  from different sources to make it so this page became it was set up very early in in in the story  and we started to put content onto this page that went beyond just the simple maps of  cases and deaths I also thought I'd just draw a point that when this story first broke of course  it was a story that broke in Asia and you can tell actually even just from the photos on the page  that at this time this had a very strict geographical focus to the story as the story has developed  we have changed you know as the epicenter moved to Europe as it moved to the US and as it has now  moved indeed to Latin America the use of things like photographs is actually an incredibly important  part of our visual journalism which is to to carry empathy and understanding of the human  conditions that data alone maybe can't you know so these are composite pages that contain not just  data visualizations but also some explanation some links to other kinds of content that are related  and as we started to add content to this page one chart type more than any other started to become  to resonate with readers and it was this chart which was created by my colleague John Byrne Murdoch  and it was a chart that's been you know a long time in the making and refining which was pulling  data together from lots of different sources to try and come up with some sort of form of  meaning for international comparison about what was going on and working out how to present that  now so that's a point to stress here is that the way that these get created is not that somebody  goes away and does traditional academic research on a deadline and comes back just having worked  you know it's not just a case of accelerate in the normal processes the process is the  generating these sort of visuals are generally very iterative you know post something look at it  see how people are looking at it you know look at the responses and evolve the contents of these  graphics evolved a lot over time and we've paid a lot of attention to things like for example if  you can notice that there's a log scale on the on the it's like a semi log chart because the the y-axis  is a log scale and you know we spent a lot of time thinking you know is this the right kind of scale  for this chart will our readers be able to understand what's going on will they be able to  recognize that this is a chart of acceleration not absolute levels you know and and so on and the  chart certainly was popular enough I mean it went completely viral and it's a chart that's  certainly was popular enough I mean it went completely viral I think John had to stop checking  his Twitter account at one because it was just so amazingly popular on Twitter but it one of the  things that was really fascinating for me was that LSE went on to do a piece of analysis where they  said well you know actually the public do not understand logarithmic graphs on these COVID charts  they said not only do they not understand them they're better at reading the data if you just  give them data in a linear scale format which I think this is really interesting because this is  the sort of dynamic that has been rated by the crisis you would not get this action and response  in normal times though so this has been very very interesting and I still have some issues here about  making hard and fast statements about the public do not understand logged graphs you know like that  this is definitely a period where we can start to push people's data literacy and understand  allow them to understand why we're presenting data in a certain way I've written articles about  what in the FT about why log scales are a good thing and why you should learn how to read them so  this is a log scale of the Dow Jones industrial average right and when it's on a log scale you  can clearly see that it took 25 years for the index to recover from the Wall Street right  on a linear scale it looks like that and I'd like to give that to the LSE authors and say yes  the linear scale should be used in all circumstances you can brush the Wall Street crash under the  carpet very neatly with it and so I've started to write some articles in our work and career  section of the financial times I've had a pause for about a year now but we're going to start  writing these again soon about the decisions that we make when we're presenting information  and this is an area which I feel has traditionally underrepresented in academic space both on the  arts and the sciences side for very different reasons and one of the things that's lovely is  that this is an area that's right for huge amount of academic exploration and I do you know this was  one of my favorite projects from last year which was sonification of economic data  40 years of the US economic data on the marimba you know some really wonderful opportunities to  rethink how are we presenting data to people and should we be and this was quite an interesting  process because what started off as a little bit of a sort of pet experimental project turned into  something very real when I realized there were huge accessibility opportunities from presenting  data in sonic form and working with you know I had somebody who was blind drew this US yield  curve for me from listening to this music and it was a fascinating sort of project to work on  but anyway back to the chart our solution to improving this so that people could not complain  that it was log or linear was to make it completely interactive okay and so if I can just I'll just  I'll just come out of this presentation mode so that you can see this in action so this is  this is the interactive version of the coronavirus tracker so you can select your own  countries that you may you want to make a comparison between so we're looking at the  rolling out just seven-day rolling average of the deaths in Germany attributed to COVID-19 and we  can compare that to other countries so you know let's put that in here and you know this is a  simple sort of form of interactivity we can flick between death and cases we can switch between  cumulative totals and daily cases we can also do the famous shift between linear and log  there we go as the United States do very very well on the linear scale and we can switch between  the raw numbers and also the per million because actually that dynamic changed quite a lot as the  story developed because a lot of outbreaks start in localized instances actually the raw numbers  were enough in early on but as the virus said it made more and more sense to standardize the data  using per million so we tend to use interactivity when it's important to personalize the content  around the reader and what we see generally is that interactivity is very often used very lazily  like if your only use of interactivity is for someone to mouse over something and for something  to pop up that's not enough it needs to be deeply personalizable and indeed the personalization  that I make on this chart if you can see at the bottom of the screen if the url becomes fully  customizable so as you make amendments to the chart you have a unique url to your view of the chart  and you can share that with reporters with readers with people on social media you can use it to  start debates hey look at this log scale chart of new deaths between UK and Germany and people  can deep link to those sort of visualizations so that for us has been incredibly important and  we've been using a full suite of data visualization techniques try and visualize for people how this  crisis is unfolding with all of the graphics designed to try and look at some sort of angle  on on the the evolution of the pandemic and so for example this chart here which is a something  called a stream graph was our attempt to de-emphasize the map right like we were seeing a lot of maps  where people were just putting bubbles on maps everywhere right that's how you visualize a pandemic  but actually yes this is geographical data but actually it can be quite useful to see the  geographical data in a different form and so this stream graph allows us to see this sort of  onion skin style evolution of the crisis and this kind of shift in emphasis from Europe to  North America to America becomes much more evident than it would do on a on a map and actually the  metrics that we're using for describing the crisis have evolved over time as well it started off with  cases and we know that there are problems with testing and you know international comparisons  based on different testing methodology COVID it's incredibly difficult as is the reporting of deaths  because every country has slightly different methodologies for for the techniques that they use for  births and deaths and so this becomes difficult and so instead what happened was the team collated  excess deaths data internationally so scouring lots of different official statistics websites and  official sources of deaths data and making comparisons between deaths now and the equivalent  deaths in recent years and one of the things that's been important on that to make this  honestly tell you if you work in one statistics agent that's very different from trying to be  a user of your 40 statistics agency websites so the pain in assembling all this data together  we ended up sharing this data publicly on the code sharing site github so that's all of this  combined csv data for excess deaths publicly available for people to take the data and use it  which i think is a really interesting that conversation we had before on the balance  between official statistics agencies and news organizations that for me this is quite a  sophisticated approach for news organization to be doing with to be actually supplying some  of the underlying data there is traditionally no expectation that we would be doing that  and then what we realized in the process of this evolving story is that our role as journalists  was changing produced on off stores you know like the newspaper story that you know is front page news  one and then is like in the in the in the rubbish bin or the recycling bin the next day we were  actually moving from kind of storytelling individual stories to managing products managing  data products so that involves as soon as you have pulled all of this information together of  course you get increased demand from other journalists for for our help because data  is at the heart of so many stories at the moment and journalists who are not who don't feel themselves  data journalists are coming to our team for advice and for help and we also have increased  scrutiny from readers and what do i mean by that what i mean is the page that i just showed you  our coronavirus tracker is the most viewed page in the history of the financial times website ever  by a magnitude of about five right so data journalism has absolutely dominated this crisis  and with that we put data in front of a huge audience relative to our normal audience which  means everyone is asking us questions about the data and the techniques that we're using for  visualizing it where we're sourcing the data from and so on and in fact that data sourcing and  updating is become a huge part of product management for for the stories so what we did  was we set up a dedicated email address on the page saying help us to improve this if you if  you know of some sources of data that we are unaware of please tell us and we'll try and  incorporate this into what we're doing and just to give you a preview that's the sort of response  that we were getting in the inbox on the back of that request you know everybody from sort of  talking about that i've tracked down some all-cause mortality data for Serbia for the last five years  these are the sort of emails that are precious to us because it's allowed the team to respond  to reader feedback in huge numbers and you can see on some of the tags there some of those tags  are academic right which is again a dynamic of the crisis right that is definitely something  that's new and should be encouraged um we of course have then been using kind of techniques  for visual journalism to take the data that we've sourced and bring it into the storytelling  space and for me when we were talking about the skills that I didn't learn at university earlier  this is one of them because at the time no one was teaching people about how to present data  and I think if that was one of the things that I'm most happy about the evolution of data science  compared to statistics as a as an academic field it's the emphasis that data science places on  being able to communicate what you've done at the end right like that's tremendously important  so this is the financial times visual vocabulary this is our own resource that we've created at  the financial times we have it big on our newsroom wall it's designed to allow people to to visualize  stories based on the relationships in the data that they're working with and you can get hold  of your own copy of this just if you go to ft.com forward slash vocabulary um and just to kind of  round this off we've made it multilingual because guess what presenting data is a multilingual  international language so this chart's available in chinese and japanese and spanish and french  I loved learning that a pie chart is a camembert chart in in France um you know this is all good  stuff um how does that what does that look like it's the thing that allows you to decide  should I show the changing lockdowns of the world on a map or on a chart you know how different is  how important is it to show changing patterns as opposed to spatial patterns you know and the answer  in this case was to do both because they were both offering us something very very different  about what was happening with the the government responses around the world um diagrams that have  come out of biomedical science are pretty good for showing how airlines have been affected  with their origin and destination routes um charts that look like petri dishes are pretty good at  showing the difference in financial impact on the share prices and stock value stock market  value of different kinds of company um a whole range of chart types that our readers probably  have not seen four or five years ago have become a default method of reporting on stories with kind  of a much broader range of chart types based on the underlying flows of data so this is this is  this is trade data that is core data this to an ft reader understanding the world of trade  but it's actually also quite complex data because trade flows are matrices right like so you need  techniques for visualizing data in matrices of which this is one um you know we had a great  design decision about this one where we were trying to show the fact that if you could believe the  chinese numbers that they had managed to contain the disease to one province in general terms and  that that was very different to the us and we were tempted to do that using a map but nothing  seemed as effective as doing it like this you know just to use a slightly different technique um and a  lot of our charts are actually relatively straightforward but the the the thing to to  note with the design of these charts well there's a couple of things these charts are all what I would  call um they are like visual rhetoric okay they are meant to make an argument to inform some  um interpretation or view of the data so this is different to the world of official statistics  sometimes where you're just saying here are the numbers so if you look all three of these charts  on screen have a narrative title they're actually trying to guide the reader on how to interpret  um the data and that it's yes it's important to show what the data is and that can happen in  the subtitle right like in in the footnotes um but also we're very keen to try and draw people's  eye to the data we want people to look at which is in this case the highlighted colors you know  and the other colors here are for context um alternative data sources have become incredibly  important to us energy usage google maps football data these are all things that allow us to understand  how is the world changing um some data was more difficult to collect than others this is the we  did a huge amount of work uh Steve Bernhard and David Blood analyzing how much flight patterns  have changed between April and between January and April extraordinary stuff um and so our language  of visual journalism I think has evolved through the crisis uh we had done some of this stuff before  but the thing that's been really fascinating has been to apply this kind of thinking on deadline  in a rapidly changing story space and and the results of that I think have been very warmly  received by our readers um I ought to say not everything is a chart right as well as the visuals  visual storytelling can take all sorts of different routes and actually illustrative  journalism that explains complex technical uh and complex science to people who are not  scientific specialists is also a hugely difficult challenge and we're lucky to have a couple of  visual journalists who specialize in this type of visual journalism which has been again tremendously  useful as our understanding of uh COVID team has developed um and sometimes these things can  combine this is kind like a chart in illustration you know how far does a sneeze travel and how  a cough these are sort of very interesting sort of and real world examples um the thing that I'll  just finish with is to show you that one of the things that we can do on the ft website now is  people can actually if you can see in the top menu here we have graphics is its own thing right  and we have a searchable database of all of our visual journalism so you can search for coronavirus  and it will allow you to see all of the coronavirus graphics that we're producing in a chronological  thing or if you want to go to donald trump you can search on that you know um that has also  been a really interesting thing to do because using graphics as the way of discovering stories  has become an important part of what we're doing as well so um you know data journalism is definitely  not new it's definitely more about than about how high the cat is in the tree um but I think if  there's one positive that people are looking for in the world of data science for their careers is  um here it is right like organizations are embracing this sort of work at the heart of their core  business model um so I'll stop there and hopefully you've got time for a few uh questions thank you  thank you so much and my my head is spinning from all these beautiful graphs and visualizations  and I don't know which one I want to first look at in detail um after today so thank you very much  um we already have some questions uh from frouker uh and she's really interested in how many people  are there on your team how many people are producing this multitude of graphs of  visualizations and how do you get to those designs so I'll talk about the team first  because I'm very proud of them they're all brilliant um we have got a team so overall the  FT's got around about 600 journalists and my team is about 25 people um and that's about 20 or so in  our nominally based in London or certainly UK based now um and we have two in New York or one in  Hong Kong um that covers a real range of disciplines though we've got everything from sort of graphic  artists and visual journalists to data journalists and statistics experts and so um the way that we  organize ourselves is to try and take advantage of that overlap in skills and for me this is a big  difference so like 20 years ago if you were graduating in statistics you were probably  anticipating if you're going to work with other statisticians right like that you you cover less  with groups of people who have the same skill set and interdisciplinary skill sets are actually in my  view much more challenging and rewarding and equal measure right that you learn and value  everybody else's skills and try and integrate them um in the context of how we do things there's one  thing that will determine how what does the process look like and that is time right like if I've got  if I've got two hours it's not unusual for somebody to say hey Alan somebody just said  something in parliament at 3 p.m. and we need a we need a chart of it oh by the way we're going to  print it a quarter of a million times at six o'clock right so you've got two hours or three hours to  just from what is effectively a blank page to come up with something that is ready and finished  and obviously you're going to do that in a different way to if someone says hey Alan next month  we're you know we're doing an investigative piece so we've got one month to run at something  well we're going to take something for three months um so we have a we what we do is we assign people  so that sometimes we have a group of people who are we always have a group of people who are covering  the news cycle and their job when they arrive on those days is just to cover rapidly changing news  events you know it could be an earthquake somewhere you know obviously in the world of covid it will  be something somewhere right like but something that we didn't know about yes but that we need to  know about today um and so people uh spend some of their time in that space and some of their other  time on deeper projects like something that you know is going to happen like the us election  well you know you're going to have six months to plan for that for example and you can approach  that differently and i like to make sure that everybody has some time in both contexts because  if you have three hours to do something it certainly helps with your decision-making  skill set right like you know it focuses the mind and but then again you know it's very different  when you have a longer term project and you need to manage your time and understand what's possible  within a longer time period so it's it's challenging and rewarding but that's kind of how we do it  yeah um people have been asking about your tools like do you autograph visualizations with pin and  paper and then when you move to um to the computer which which kind of tools do you use yeah so  apple pencil is the best of both worlds right like i use an apple pencil all the time for  sketching and for doing things and it it feels organic but it takes me into digital space um we  we we use a range of tools um if you're a data scientist most data scientists will be  championing python or r or something like that you know those tools are used in our team and that's  great for people who are learning those skills um but some people get by on excel because they're  doing something slightly different you know um we use a lot of web programming skills for our  visualizations things like d3 we use a lot um and what we do is we have a big library of those  visualizations pre-created and pre-formatted for print online social media so that we can  generate them very very rapidly if we use something like our visual vocabulary as a decision-making  framework that we can go very very quickly from sketch to finished graphic  we have another question um for the music project that you did and how did you get the idea  to do that well that i that was one of my favorite things because music is a sort of slightly uh  separate in the keyboard in the back there right like there you go um i have been doing some work  which involved animating some us economic data that was how the visualization started we were  we were animating some us economic data and then the video team came to talk to me about making a  video about that action and the guy doing the video tom said you know what the big problem here is he  said it's very just just watching this animation he said i bet you can't you know can you do a sound  track um so i kind of experimented with could we use the data to not only drive the visualization  but connect it to a synthesizer to drive some sounds as well i mean that we made that connection  i was super excited because that's just classic serendipity you know i had absolutely no concept  of what we were doing until we just got to a certain point with it we have a lot of questions  rolling in and we have very little time but i want to ask one more questions and maybe and you can  just look in the chat afterwards and maybe answer one or two more questions that would be wonderful  um christoph is asking uh at the financial times do you differentiate between decision making graphics  for politicians and informative graphics for a broad audience or don't do you do not make a  difference between those two i think i think i mean we produce a lot of graphics i mean i did a  i did an audit of this a little while ago and i think i came up within one month we made 800  graphics um and that even that doesn't include the newspaper or social media but just on the  website 800 graphics and i think that's probably almost too many right like i think i think where  we want to focus our graphics the distinction that we make is i want people to be slightly surprised  by what we show them that's the big thing because i think that's a sign that if you want to inform  decision making that you might be correcting some preconceptions for example right and that  doesn't matter if you're a politician or just a reader trying to understand where the virus is  spreading right like that you want to be at least partially surprised by the data and if all you're  doing is showing people charts of things that they already kind of know um that's maybe those are  maybe the charts to not make thank you thank you so much for your participation in the career panel  and for your keynote and also thank you for asking all those questions that are still coming so a  lot of interested here thank you very much thanks carolina and i'll do my best to answer any other  questions people might have in the chat thank you so a round of applause for elin and our next  speaker will be johannes arvila johannes arvila is an associate professor at the university of  oxford and he currently focuses on the role of tracking apps in the corona pandemic and his talk  will be on this today and we are very happy to have you here johannes and the floor is yours  thank you very much um let me share my screen um so before i start i have to say i'm a big fan of  of of graphics not just for sort of in in my newspaper but also in my in my work so i can  already tell you that obviously academia has always been very graph heavy but this is going  so much stronger recently and it's perhaps sort of it's a cycle that sort of people read it in  the newspaper and they also want to read it in the academic papers but when i now see papers that  used to have basically just tables now you often have the tables basically just in the appendix  and the main story is told in graphs so i can totally see that i'm a i'm a i'm a very big fan of  of of graphs and figures um to the dismay of my default students who always have to redo their  graphs again and again um so today i i i want to talk about the step before the graph mostly in  terms of tools and in terms of methods i want to talk about how we get the data and then also  talk about the data we got um i have to warn you that uh today uh the internet at my house is sort  of a bit dodgy so if i'm if i'm if i'm leaving the presentation it's not about you um and don't  you worry i'll be hopefully back in a minute um but i hope it lasts for 20 minutes at least so um i  want to show you some data about the acceptability of app-based contact tracing which we started in  uh this project we started in march um and then at some point some some names that you that you  might know frederick frauke and ruben uh joined us so it's sort of a it's a big international  cooperation now um so when we started this in march nothing of the things that we've seen in  the last two two months were sort of very much possible we knew back then that or sort of there  were the first ideas that contact tracing apps would be useful or could be useful uh to help um  ease the lockdown particularly if you combine it with a large testing program you could automate and  and and make the um the contact tracing programs are more effective and if these programs are  effective enough you could actually end the lockdown or at least loosen the lockdown obviously if  you have a vaccine that's even better but that's sort of still in the future the problem was that  that app-based contact tracing it was actually a totally new idea um it has never been used before  at this type of scale it wasn't has been used a little bit on smaller scale but sort of on this  really population-wide level had never been used before and it was clear from the get-go that the  app take-up was very crucial so and i think we've seen one of the many graphs that ellen show was  actually a graph that exactly came from this variety wyman an out paper from the Fraser  Group in Oxford that showed that if lots of people use the app then it's really useful but if if  nobody used the app then obviously it's not very useful and in in March nobody knew how  how much people would actually download this whether people would like this type of idea  or or not it's very it's relatively straightforward it's not easy as the UK has just found out it's  not easy to program such an app but the harder part is then to convince 60 million people to  spend one minute each to download download this app and to decide to download and to notice it at all  so when we started our surveys we ran the surveys in at the end of March um there was no public  debate about that and there was really no idea about what people would think about it it's it's  potentially it's a it's a tremendous um privacy or threat to privacy um run by the government a  government surveillance app on your phone something like that has never happened before in at least  in western countries so it was really unclear what what people would think about uh what people  would think about this idea so our target group basically and someone mentioned the target group  our target group initially was really political and public health decision makers they'll we try to  inform not so much the academic debate and not so much the sort of the public debate but really  sort of the technical debate within the health ministries but then obviously because it's such  an important topic and nobody everybody sitting at home and wants to know about covid we realized  that this was spillover into sort of into a broader broader audience that's why we already started  as soon as we had all the data we made them available you can find them on this on this  website all the reports everything is there and you're happy to reuse them and build nicer graphs  for example then um then we uh then we were able to do so the questions in our surveys  was mostly about the future willingness to install such an app would you install such an app  but the problem was that this was at a point in time when nobody had ever heard of this type of app  so um it could only give a very rough indication of acceptability because um we could describe it  in only in in in one way but really our sort of our aim was to see whether there's any chance at all  I don't think we aim to get exactly the right number of downloads right sort of to really sort  of predict what was going to happen but try to understand what people like uh or not like about  the app think about the motives so that's I think that's the advantage of these type of of surveys  or early on that you get a rough but a very early indication of of acceptability before any of this  has been sort of programmed or or released the disadvantage obviously is if you do these surveys  in March and now we look at this in June um the things will change over time uh the the stuff  that we would measure if we ran the same survey today we would see different things probably we  would see different things but also that ultimately decision to install is very different from what  we could ask about which is something like the general willingness to install um but in the end  you want to know what how many people will actually have it on their on their phone so that's something  we we couldn't ask back then and you will I'll show you some extra data later on so comparing our  survey data to the actual download data so this is what we find we run these first surveys in five  country in the uk germany italy france in the us and um so you see on the y-axis this this is basically  stacked histograms the the share of people uh let's say they would definitely install or probably  install in green or then in red probably not install or definitely not install you see across  these five countries it's actually pretty similar that about 70 to 80 60 to 80 percent uh say they  would install such an app that's quite encouraging um in particular if you interpret this general  willingness to install the app as an upper bound for the actual installation in in the end because  there's quite a lot of things that even if you're willing to install they come sort of between the  app and and so between your willingness to install and actually your the the app showing up on your  on your phone we also find that people that trust the um trust the government less are less willing  to install the app or they say they're less willing to install the app so on the left hand side so we  had a question about how much do you trust government from uh fully agree to so do you trust the go  sentenced I trust the government to do what is right most of the time people could fully agree  to fully disagree to that we see that the people on the left hand side who say they fully agree with  that are much more likely to say they would also install the app compared to the ones that fully  disagree but you might remember from the last page that um we have us data in here and you might say  well trust in government uh in the u.s is quite a different beast than say in Germany but we find  the same pattern of behavior across all five countries obviously the level of trust is different  um and also the UK and obviously the trust in government is also changing over time um but at  that point in time the same we see the same pattern in all five countries so it looks very very  similar this graph looks very similar across all five countries we also ask for motives um because  this was still in the design phase of the of the app we also wanted to give uh information to to  decision makers how to how they could design the app um so we ask for reasons for installing the  app and against installing the app and people could click from a list of potential reasons  they could click or they could also nominate other reasons if they wanted to and we see that first  of all many more positive reasons are chosen and I don't show you that graph what I show you is the  graph uh for the main reasons against installing the app and we see that the main reasons against  installing are really to do with trust a bit like in the in the previous page the concerns about  government surveillance after the epidemic um people 42 percent of our respondents say that  would be that's one of their reasons against installing the app this is often people who  will say they would install the app but it's still a reason against and that might be overwritten by  other things but it's still a concern people have if you are worried about their phone getting hacked  people might feel more anxious um uh that was actually quite surprising for me because I  would I thought that um uh the app could give you a piece of mind if I know I haven't been in contact  with someone and I can I can check every day um that that would be quite good but it seems that  many people are actually quite anxious about or say they would be more anxious about this um  in one of the servers in the US we also ask um we offer as one of the reasons um I believe that  not enough people will download and in when we offer that but also people also say that that's  an important reason if they think this doesn't work and they don't bother um installing the  app or they might that might be a reason against it I think sort of I'm in the UK um you might have  heard that the UK doesn't have such an app at the moment um you might have heard that the UK is  struggling with responding to the pandemic in in general um uh so uh someone on Twitter said the  other day because um so the UK had an app almost ready but then they postponed it and they say oh  it's not coming first of June but it might come in winter but who knows someone on Twitter said um  the government isn't able to organize a piss app in the brewery um so it's uh uh so which I found  quite funny but sort of there's sort of a generalized sort of trust issue here coming again sort of  trust not in will the government partly will the government um misuse my data down the line but also  is the government able to pull this off in a in a in a good way and if if they're not um  then uh I think that's bad news it gives sort of our our results would suggest there's bad news for  the number of people who download the app now I said all these data I showed you are really from  end of March it's really almost historic evidence I'm showing you today so how does that compare  to actual behavior so you you surely know that Germany has um introduced an app um the Corona  Van app about a week ago so these are the extra download data from uh from the Robert Koch Institute  which I plotted as the installations as a percent of the eligible population so in Germany you'll  have to be 17 or at least that says it has to be you have to be over 17 to download the app  and you have to have a smartphone that has to have an iOS or uh Android um operating system  and it has to be new enough so this is dividing the number of downloads by the number of people  who could download it at all and that is actually also most comparable to the data I showed you  before because we only surveyed people who have a smartphone and who are older than 18 so that's  sort of the closest comparison and you see that in Germany over the last nine days almost 30% of  the eligible of the eligible population have downloaded the app um we said in our survey  sort of this very rough estimate that's about so going back to Germany is sort of um uh roughly  70% um so now you well there's only after nine days how can we predict the future really difficult  we can look at Singapore because Singapore um introduced a similar app already in March so we  have longer data for the for the download statistics for Singapore so this is the same data now sort  of just on a longer scale because Singapore um has been out for longer so blue here is Germany  and orange is Singapore but on the xx is still dates and since launch so sort of the the orange  curve starts in March while the the blue curve starts in starts in June but you see where we have  overlap it's it's almost bang on it's like the same people are downloading uh these apps so it's  very very similar um now that this graph sort of waves its head really strongly at you to say that  obviously what happened in Singapore will happen in Germany and I will be a little bit more careful  about this um but you see that in in Singapore downloads have gone up it's really funny here  that you would expect that there's plateaus all the people who are really into this all the people  that read the financial times every day or really informed they downloaded early on and then sort of  it sort of peat us out and you would think oh it doesn't go about 40 percent something happened in  Singapore there's something that happened we think is really difficult to establish causality is that  they uh loosened the lockdown and had a new wave of of cases and so suddenly hey you got went out  to meet actually other people so suddenly the app is actually much more useful compared to if you  just sit at home you don't don't meet anybody anyway and on top people started dying again  and so people dying is really a good motivator it's obviously a very sad event um but to motivate  people to do something uh that's really useful so um I'm not hoping that in Germany we have a  second wave of of deaths but sort of you see that perhaps there's a chance that Germany over a long  period of time um can get um many downloads but I think the best guess is more that we sort of end  up between sort of a range of 30 to 40 percent just looking at these these graphs from Singapore.  I want to talk a little bit about the um sort of about the tools and the methods and not just  about COVID so in particular when you compare this if you charted this graph for France and Italy  that have also released apps a little bit later than Germany they also have released apps the  installation numbers are much much smaller so in Italy and France about two to three million people  have downloaded the app so this is much lower Germany has about 13 million Germany's also a  little bit bigger but it's perhaps a quarter of these these numbers. When in our study we found  roughly similar amount of sort of similar values for Italy and France and and Germany so so how can  we explain that and now this is really sort of speculation um first of all did we measure willingness  to install correctly was something in our survey wrong or is something wrong with surveys in general  perhaps people just told us what they thought we wanted to hear um so say a social desirability  bias that uh our respondents didn't think about this at all they just told us what they thought we  wanted to hear and perhaps they thought we want to hear apps are great and and and so they told us  that even though they didn't think that's that that wasn't actually their true opinion. Perhaps our  explanation of the app was different from um what people actually saw our um our explanation was  neutral but we also highlighted the positive things um I think the the discussion since then  in particular in France has been quite negative as focused mainly on on privacy not so much on what  can it uh what can we sort of what's the positive side of this app rather about what could go wrong  and that's rightly so because it's a new um uh it's a it's a new technology with lots of risks  um but it's also with upsides so I think perhaps our explanation was more positive  than the than the information that people had over time between March and and um and now  but perhaps also we measured everything correctly it's just that the willingness to install has  changed so surveys that have been run very similar service to ours that have been run in Germany for  example find newer service over time find actually that the willingness to install goes down ever so  slightly over this period of time perhaps because the epidemic is receding similarly in in Italy in  end of March we were actually close to Armageddon in northern Italy the hospitals were sending  people away and to die at home was a really really very tough situation and that situation has been  has has been luckily has has improved very much but that obviously also reduced the urgency to to  do anything about it and then obviously and I mentioned this already before there's a clear  difference between what we can measure even in the perfect world what we can measure in these type  of services is the willingness to install and there's will always be difference between that  and the actual installation I think the biggest thing the biggest difference is the attention  in our survey we chucked all this information into people's face and they're there and they basically  have to make a decision I mean they can drop out but it's there and they have to they are aware that  this app exists and they have to press a button and there you go and then they press a button well in  real life you might see this on tv you might not you might never have heard about this you might not  have a tv so it might be that you're not aware of this app and even if you're aware there's also  300 000 things that you also have to do today you have to do the dishes you have to do this you have  your kids all kinds of reasons that will take your attention away from this so even if we asked  you again if this popped up on your phone you wouldn't say yes I'm happy to install this app  but you that might not be enough for you to go out to find the link and to do that  and obviously it's costly to do that takes a minute or five or ten if you're older  perhaps you don't even know how to do this you don't for me it's easy to say oh yeah you go to the  google play shop shop and then you search for it and then you install it my parents might have a hard  time with that um they might procrastinate and finally also I'm in my normal life I'm a I'm a  behavior economist and I see that the just the general concept of the app is actually pretty  complicated I think everybody can understand that if you stay at home during the lockdown if you  stay at home you don't meet anybody it's really hard to get infected I think there's a really easy  proposition to do and if the prime minister says they stay at home because if you stay home  then nobody can infect you and you don't die that's something that is really very straightforward  similar with face mask if I wear face mask I can't cuff it other people it's harder to infect others  that's relatively straightforward to understand with the app is much much harder if I install the app  nothing good is happening to me the only thing that can happen to me to happen is that if I already  got infected I will still infect my first degree contacts but if I have the app and my first  degree contacts have the app then they can go into quarantine early enough and I can  perhaps protect my second degree contacts you see that's really complicated nobody of you has already  heard of second degree contacts and so it's it's much much harder to understand there's lots of  trust involved again um and so that's much much much much much harder as a oh I fully understand  this and I'm I'm happy to do this honestly we are running a little bit out of time just yes yeah so  that's the last that's the that's the last slide I said I just want to say that the so in these  attitudinal surveys um I think they're very useful tool to get this early indication just what like  we use them but you shouldn't expect that you will be able to fully predict future behavior um and  that's what wasn't our intention um I think the data and motives should be more generalizable  so I lied to you there's not the last slide this is the last slide so in your spare time do download  the app they're available in almost all countries except in the UK and in the US um but there you  go thank you very much thank you so much Johannes for your very interesting talk and your insights  into the pitfalls of different data and we already have a question right on on the topic um Georg  is asking um whether the install number or also um covers reinstallations or deinstallations and I  would like to add um is it installations or is it really activation of the yeah because for the  for the German app you have to um clicks through some some slides and say okay I accept um that my  data will be used and etc uh no very good question so the the data I showed you is is the download  data so this is from the robot conference web page um so this download this doesn't this doesn't  doesn't correct for deinstallation doesn't correct for anything the data on from Singapore is from  the website traced together and one of my worries is that this sort of increase here means something  about the data quality which I can't track so I just say this is the this is the number on the  traced together website they this is the number they give me but what it means about um working app  installations um in in real life are to say and even it's even worse than that because even if you  have it on your app on your phone and you click yes to everything where you have to click yes to  you have to have the phone with you your battery has to be running and all kinds of other things  have to happen so it's uh uh this is um yeah so there's quite a lot of things I have to go right  thank you um another question um you mentioned that you were doing the survey especially from  ministries of health and and uh politicians did you get a lot of feedback did people in office  react to you um um well many countries installed these apps so I think that's I take that as  obviously totally driven by our survey uh no so this is sort of partly um so this this ferretti  at al study here um so full disclosure my wife is one of the co-authors on this that's how I got into  this um she's the stake in our family I'm just a side salad uh doing the social sciences uh  side of this they were working with NHS x the the department that developed the the app in Britain  so in that sense we had them in in that respect um we were in contact with pet quite a lot when was  you might remember um germany had a centralized app before they moved to the decentralized  a system so we were involved with them quite a lot early on we were also involved with the  the so we yeah so in that sense in the sense yes these data were taken up um yes we got some  feedback but then on the other hand we were not the main main game in town I don't think they  I think the data that I showed you is relatively self-explanatory um so um I um we were actually  quite happy um about them about the impact in that sense so you see I'm thinking about  academia we always have to think about impact and so I was quite happy about this  there's another question um uh deliverance is asking how can we  advocate to get the app in the US yeah good luck with that um so um so frankly now after three months  I think both in the US and in the UK I think having a an app that is based on voluntary downloads  I think is a very very difficult project now um because so many things have happened even in the  best of cases and I think in Germany if you follow the public debate there was basically  everybody saying this is an amazing thing and we totally need this is basically I've only found  one politician who said she wouldn't want to have this but it's across the spectrum everybody said  everybody said this is amazing it's the bonus leaguer whatever um while in the UK the debate has  been rightly so actually quite uh quite quite negative about privacy in the US obviously all  kinds of weird stuff is happening um so I think with this type of background um I think it's very  hard to get high numbers of um of voluntary downloads I think my only hope for the UK and the  US is the second phase of the Apple and Google uh installation I'm not entirely sure um whether this  is coming but when they launched their their API they said in seconds that we'll just do the contact  tracing for you it will just happen on your phone and then the the app the NHS app or the US whatever  or highway just has to do has to be downloaded once you're infected you don't have to have  an app penetration of 60 in the population you can download it as soon as you get infected so  that's a totally different decision and I think that's the best guess for for that but we also see  how difficult it is due to contact tracing you see it in Singapore you see it in sort of countries  that were actually very very successful it's really really really difficult to contain a local  outbreak you see it in Guter slow one of my favorite cities um is really difficult and this process  has to be super super efficient and it's really hard to do it without an app and we we're seeing  this and I think we will see this more over the next couple of months great thank you Hannes  thank you very much for um presenting our research and showing the importance of sort of  connecting these different datasets I was hoping we have enough time for Ellen and you to discuss  how these graphs can be made prettier because now of course we all have conscious or not having  beautiful ft graphs but we do have a graph here on the slide and I want to use the the closing  sessions here for a moment to invite everybody to take the annotation feature of zoom which is  either on the top row for you on the bottom in the menu and it's it's annotated and then you can  pick the the use your mouse to mark the country you are from and so I never tried this let's see  if this works with 60 people 70 people still on the call and I'll give you about two minutes and  then we would like to close this out with a group picture before I let everybody go so but before we  do that a big round of applause and everybody since no one is speaking feel free to like  turn your videos off already sorry on already so that we can see you and see your class either  with the yellow hands or in real time next time you Hannes we will get prepared to do a little  voting on the fly survey and hearing who installed the app but I didn't think of this ahead of time  so all right love to see I'm I'm still missing our Chilean participants and the Brazilians I hope  you can figure out how to annotate I've seen you in the participant list so hope to see you soon  on the screen here and and I do want to thank everybody who participated and because we had  yesterday women in data science I do want to say that in our full blown connect program we had  a lot of female speakers as well and workshop leaders and whatnot it just so happened that as  we pared this down we ended up with two males which didn't you know I'm locked these two talks I'm  happy we have them but I feel a little self-conscious on the imbalance that this created so while this  is not an excuse at least be aware that we are aware all right well I love the signals coming in  thank you everybody and I do also want to say that in November we will have mid-November sort of the  second half of this event hopefully live then in manheim and we hope that you can find your way  you are interested in coming admittedly November is not the best time to visit Germany weather-wise  but flights will be cheap so you know it's always a trade-off with the data we have all right  Annika I would love to hand over to you I know that you do want to guide people with the map  but also with the picture taking so I'm gonna mute myself now and hand it over to you thank you  everybody yeah before going into the break again we just wanted to take a group picture so maybe  everybody could turn on the camera now and maybe Falki you can unshare your screen so that yeah  perfect so I think you won't fit into one picture and I can say because we heard that data ethics  and privacy is so important in both of these talks that not post this with everybody's name  on them right so that just for this is also a privacy sensitive but in new york having you  all in manheim at the group picture we at least thought we do this here and I guess Annika you  go through three rounds of this with the three panels we have so yeah  and while Annika is doing that I do want to remind all the present students and those that are  incoming that at 6 p.m. after the break there will be a Q&A session with Karin and Kai and Olga  to answer all the questions you might have with respect to the program and going forward and  finishing up and changing and I do want to remind everybody that we have another info session on  June 30th for those that are now in ties to hear a little bit more  so I think we're done with the all right then I want to take one more big round of applause to  all the speakers and panelists thank you for joining us today and I look forward to seeing  all of you in person or virtually at one of the upcoming talks or in one of the courses many of  the courses can be taken at standalone and Ellen I think we're all ready to take an internship at  the FT including me so all right thank you all and have a lovely weekend
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All right. Welcome everybody to our 2020 Connect event, Connect at IPSDS. Some of you were part of Connect in previous years. We had around this time a lovely event last year in Mannheim and quite sad to not have you there in person. It's a beautiful summer day today here, light breeze. And you know, I'm glad that at least we get to meet online and I'm actually super excited that we get to invite and host people who indicated previously that they can't join us and now can because we are online. So unlike my wedding celebration that would have happened next week, which needs to be moved, this one has not moved. And so welcome everybody to our 2020 Connect event. I am excited to introduce all the speakers and panelists we have, but I realize you, not all of you know me. So let me say a few words about myself. So if you know who is greeting you here. My name is Frauker Kreuter. I'm the director and founder of the International Program in Survey and Data Science at the University of Mannheim in Germany. And also I still the director of the joint program in the survey methodology for another five months at the University of Maryland in the US. And I was interested, many of you know that we love to train folks that work in the federal statistical systems and around. I do have ties to the Institute for Informative Research heading the other statistical methods research departments. So just a little bit of context. If you want to reach me with questions about the program or what we do here afterwards, my email address is right here. More importantly though, and this is my cue that I tell everybody that we do love to hear from you in the chat. And for at this point in the Corona crisis, I assume everyone has been plenty on Zoom and knows how the chat works. But if not, press those three dots on the side or look for a menu item that says chat and you can type in questions to everybody. And we already getting a hello from Japan. Hi, Pina. Great to see you. Obviously, there are too many people to introduce everybody, but I do use the chat and post questions. We have Q&A and Karolina and I will be monitoring the chat window and will fold those into the Q&A that we have later on. So I also want to extend my thanks to the University of Mannheim and the Mannheim Business School. While they're not physically hosting us, they have been instrumental in helping us set the event up and help with registration and the like. And they all, well, both institutions, hope you will come visit in person when we continue this event in the fall with its onsite portion, but also in the next year when we have our usual summer event. So let me introduce here briefly the agenda. I will leave this up for a couple of moments so that you can see it. We have, oops, sorry about that. We are starting with the career panel where you will see one of our faculty members, Flora and Koish, hosting current students in the program, friendly collaborators and future collaborators for a career panel. We try to make quite diverse in terms of backgrounds and look forward to that discussion. This has always been part of our connect event and I'm thrilled to open this up to everybody. Well, we created a little buffer, you know, often the Q&As go so long that it's helpful to have these buffers, but after a short break, we will then have keynote talk by Alan Smith from the Financial Times and second keynote talk by Johannes Avala from Oxford University. So my special welcome and hello to both of you, Alan and Johannes. And we'll have a closing discussion and then after another break, we have at 6 p.m. German time, different time, wherever you are in the time zone, and information session for all those that are planning to enroll in our master in applied data science and measurement and that will be hosted by Karin Fursinger and our colleagues from the Mannheim Business School. And so, as I said, I'm delighted to have you here. Anika, I'm not sure, is there a second slide deck I should be showing here? I only have this one, if so, I'll stop sharing my screen and you can upload that. Let me pass here for a second. No, that was the right one. Okay, perfect. All right. Good. So, already ahead of time, Florin, I told you I will make the intro brief and so we have plenty of time for the career panel and get to the interactive part. I love to see all the... Sorry, there were a couple of additional slides on this deck, I think. Okay, see. Sorry. Let's check. Oh, yeah, there are. Okay, I did... I thought that was a separate... Okay, all right. There you go. I told you it's warm here, so it's messing around with my... I thought this was the ending side, but this is the transition to me explaining what this program is about. So, let me take five more minutes for... Paul's hope to explain the international program in serving data science as it started and now the master program in applied data science and measurement. We have been starting five years ago under the auspice of the Federal Ministry of Education and Research. They kindly sponsored five years of program development to develop courses in data science probably defined, but for us with the clear focus on data collection and angle to the survey world. And we were fortunate to learn the online education part, what works, what doesn't work way before the corona crisis hit and then have the opportunity to teach our colleagues here in Mannheim and in Maryland a few tricks on how to do that. And it's future the program will be under the label Mannheim Master of Applied Data and Measurement housed and hosted by the Mannheim Business School and at the University of Maryland it is hosted by the joint program in survey methodology continue with its name international program in survey and data science. While these two universities are sort of the pillar and the flagship, there are a lot of faculty involved from partnering universities and University of Manchester for example is a strong pillar. Increasingly we're recruiting faculty from the University of Michigan in the United States but also all the way down to Chile we have faculty member there who has been active almost from the start of the program. Our goal with these related faculty members and universities is to eventually have a partner university on each continent and the possibility to enroll through these other schools. We also have a lot of faculty in this program that comes from industry or industry related I mean private and public sector but non-academic and you see here a list of them. People change jobs so I feel like every time I look at this slide deck you know I feel like oh wait a second this is outdated but I think this is at this moment in time actually a correct representation of our participating faculty and you see the diversity reflected here as well we just recruited two more people from Facebook that will be joining us teaching in this program they have been helping us to develop and sponsor the program on the US side as well. What is survey and data science? Not all of you know directly what this keyword means and for those a bit of context in the data science discussion I mean you hear people talk about data being the new oil and this and that they often talk about data that evolve through digitalization so through organic digital footprints that we leave in our daily endeavors and last year at Connect we had we had Roberto Ragon Bonn speak here and he designed this slide and beautifully captured sort of the difference in these data sources. On the survey side on the more traditional side and you know our speakers both of them actually have used or are using surveys in their work we always have been collecting data but in a very different way right we designed a data collection with a different purpose we had experiments we had surveys and to some extent influencing what administrative data gets collected and so what you know this program tries to do is to combine these two sources with each other and take the best of both. And Roberto whom I just mentioned has a beautiful project that does this combination in real life and has become even more important that kind of spirit in the last couple of months during the corona crisis and I'm talking about the price that index that they developed at MIT. Roberto is from Venezuela he got agitated when you know two decades ago he got the impression that Argentina might not be reflecting the inflation scores correctly or inflation indices correctly and that it could be useful to create an alternative indicator for inflation indices for countries. And so what he thought was well wait a second there are a lot of prices online these days we could just web scrape these online prices and use the changes in time from daily changes to reflect changes in the pricing. And what's interesting here while not the whole market of goods is online the changes in prices online likely reflect the changes in prices offline as well yet the information is available much quicker and so that was sort of the spirit and the idea of his project but as I mentioned it's a blending of two data sources because while they get the prices online they still do it or use it in for most countries data collected from the national statistical offices on the basket of goods that people buy and purchase and they use sampling strategies to have a good representation of the different prices and so a combination of the different skills that you would learn as a survey statistician and as you know what you see as data scientist elsewhere. Likewise statistic Netherlands has made its mission to be a zero footprint agency meaning no surveys it's interesting what statistics agencies think of zero footprint but anyway for statistic Netherlands it means no survey data collection ideally everything measured in the background and hence our name you know data applied designs and measurement it's really about how do you best measure what's going on in society and certainly right now what's going on society you need measures fast and reliably. The statistic Netherlands has used Twitter and in general social media sentiment data to reflect consumer confidence scores and traditionally they did this with the survey the survey data are depicted in the upper right corner here the Twitter scores are depicted in the larger part of the graph. Not going to talk in detail about this but what unifies us in all the courses and the kind of framework that we do is a mindset that comes from the survey world if you think of the way a survey statistic is produced you can think of a representation side that's depicted here on the right when we worry about what is the population we can draw inference to how well does our sampling frame where we actually take data covers that population and how can we collect the sample what are the respondents do the respondents do and how do we do adjustment to these sample data in stressful times like right now it's very difficult to do population samples you know sometimes you have to collect data on the fly and then make them work or you have to do shortcuts you know all that but without knowing the framework to begin with it will be hard to get the inference right and unfortunately we do see a lot of surveys that just take conveniently every answer they can get and then post in major outlets that X number of percent of people are doing whatever measure in the corona crisis. Now similarly difficult on the measurement side defining construct deciding what's the right measurement and what's the right response is something that requires some skill and knowledge and we're trying to teach that. If you think of that other set of data the digital footprints you often don't have as structured of a process you don't draw a sample you don't know what the measurement is you confronted with what comes in from many different sources and you know you people extract the data clean them more or less although that is a big chunk of work that a data scientist does and create a pipeline to load and store the data and you know then decide on how to analyze them how to filter how to create features and all of that. What we found in talking to practitioners and the faculty member from these other organizations is that it helps to know about this other framework to get this process here right because you still want to know which data do you not have what is missing where could they be doing do you have the right measurement and so that's the kind of thing that we are trying to teach you. If this little briefing was fun there's a couple of literature freely available online that I posted here and I encourage you to download and read up a little bit and but this kind of framework might help you to understand what the folks later on on the panel that are in our class courses have learned and will take with them into the future. So who are these people who is the program designed for we have currently four cohorts that we call them test cohorts that was more proposal writing right I mean they are really our students and participants in the IPSDS and then an incoming cohort with the master of flight data science and measurement and they really spread all over the world and what's missing here from this graph are actually all those that are enrolled at the University of Maryland then you would see Australia and Japan and you know a couple of other spots in Asian Africa colored because they too join our online program. We have been fortunate to be pretty diverse both age wise, region wise and gender wise and that has greatly enhanced the discussions and I hope those students here on the call that see this would agree that this work well. In general just briefly for the course structure just in case you are interested maybe in enrolling in some of the courses or all of it or get a certificate you can you know stay on until 6 p.m. our time and learn more on how to do that but in any case we have five modules we extensively discuss how certain types of data fit a certain research question how to think about data generating processes what do you need to learn to understand can I really use these data are they of high quality enough is that something that works. You learn how to curate and store your data how to manage your data and to be honest right now you're doing this global COVID symptom survey with Facebook where people get invited on the Facebook platform to participate in a survey and I have deep respect for anyone who does sort of real-time millions of data points online I mean management not a small feed and something that traditionally statistician or social scientists probably don't learn enough what they do learn a lot is data analysis and that's of course part of the program and so is data output and access and for that reason we have this visualization talk later on here not going to bore you with the details of the different credit points and the like will come back to that I just want to say that we have three text data inside survey methodology and survey statistics depending on what is of interest to you and as many online programs these days we have asynchronous and synchronous components or actually unlike many we have also synchronous component there's prerecorded material their readings their discussion forums and weekly online meetings as a package so you can prepare and then we assume have always the chance to talk to these instructors directly and I love the online teaching because everyone always sits in the first row and that's what I think makes the program so much fun we make sure that there are small virtual classrooms that sit together in these 15 minute sessions so unlike here we really can talk with everybody directly and don't even need to use the chat so this set our kickoff event for the newest cohort and you know a special welcome to all of the new students here is our connect event normally a two-day event first day where we create a community of the fellow students second day where we have the audience interested in data science and the series of speakers the speakers we have here and will continue on with them in the career panel as I mentioned Alan Smith Johannes Abla both giving the talks but on the career panel I want to highlight Laura mirror in the show on from the University of Mannheim and Patrick and Rayman from SAP with whom we organize women and data science in the previous years and yesterday and is becoming more and more an integral part of the program and maybe will appear soon on the logo faculty members that you saw on the industry partner connection line okay so I will not go into detail with the admissions we talk about these pieces later on you can learn about the prerequisites for those we try to make it as welcoming but if you are interested in those details we have an online info session on June 30 years from 1 to 2 p.m. and you are also welcome to visit our website or write an email to info.mdm at Mannheim and business or comm all right so without further ado your questions to the panel now and over to Florian for the career panel thank you for okay I'm going to share sorry I'm going to share my screen in a second that always takes a little bit okay thank you so I mentioned I'm one of the faculty and the international program of survey and data science my name is Florian Koish I have been one of the first employees actually in probably in the startup lingo I would have been employee number one of IPSDS when Frauke started founding the program I've been on work since in different roles I'm now part of the executive faculty board and I'm also a professor at the University of Mannheim today my role is all right and there goes Florence role I he was warning us that he's in a place where the Wi-Fi might kill him and it just did so no more employee one number one you can this spot is now there to fill someone else wants to join us are you back yeah I'm back I'm very sorry yeah so you want to give it another try otherwise you can do without the slides and since we have the panel online and yeah so yeah this slide right so maybe I'm I stopped sharing the slide because okay so we have four great panelists today I'm happy to introduce them to you first and then they'll have the chance to introduce themselves a little bit more about their background and I want to encourage everybody who is here on the call to ask questions to do so please post your questions in the chat right if you have never used to chat that's the little bubble that the way you can can post a question and I'll then relate them to our panelists so the four speakers the four guests that we have here today is first and in no particular order Ellen Smith who is the head of visual and data journalism at Financial Times so he's a data visualization specialist who also writes the Financial Times popular chart doctor column he's also a highly sought after speaker he gives lectures have had a couple of or had a TEDx talk called why you should love statistics so write out write our LA and I also learned that he's an officer of the order of the British Empire which is the first thing to me I've never spoke to a spoke to an officer of the order of the British Empire so that's that's great Ellen thanks for thanks for joining us next we have as Frau can mentioned Laura Marie eating a chance she's a professor of corporate social responsibility at the University of Mannheim her research focus are on corporate social responsibility on the communication of CSR and management of CSR but also on innovation practice sorry innovation pricing mechanisms and business models she publishes widely in academic journals and has received a number of awards for her work third Catherine Redman Catherine is innovation lead Germany Austria and Switzerland and next-gen innovation hub lead Southwest Germany at SAP but she worked at SAP in diverse positions she's also highly involved and responsible for innovation entrepreneurship and teaching and actually will probably tell you innovation and innovation with purpose and encourage founders and innovators to incorporate the UN global goals is one of her mission and then last but not least Martin Coleman he's data manager at the Swiss National Education Panel survey called transition from education to employment with the acronym three he has a background in anthropology economics and sociology and he has been working for social science survey infrastructure for the past 10 years as a data manager for example but also as a survey researcher and as Frau can mentioned he's currently a third-year student in our IPSDS program so welcome to the four of you I invite you to unmute your microphone so that we can we can start a discussion and I would I would really briefly like you to all four of you briefly talk a little bit about your background maybe past jobs and how you ended up in the job that you are right now and what your responsibilities are in these jobs so that that your that our guests get a better understanding of what your maybe potentially your day-to-day business looks like what type of data you work with for example and these type of things so again we don't go in any particular order on my screen I see Ellen next so Ellen why don't you get started thank you Florian firstly just it's wonderful to be with you all today I'm very excited to hear what all of my colleagues are gonna on the panel have to say I am head of visual and data journalism at the Financial Times that's my official title that's a senior editorial post I'm an executive editor of the Financial Times and I guess in in in a career panel sense the thing that's interesting about that job title is that it did not exist 10 years ago you know it did not exist five years ago that this is a you know the idea that we now have data in the newsroom of newspapers is something that I'll talk about later in my main talk but the role is new it's focused on data in the newsroom and data for storytelling the my journey to that role has been very interesting because I was very interested hearing fraca talk about official statistics because that's my real background in in career space I joined the Financial Times about five years ago after more than 15 years at the UK office for national statistics and I think the observation on that is it's an unusual career trajectory to go from the civil service to the newsroom but that says a lot about how these career paths are converging in the field of data science my actual academic background is in geography my undergraduate is in geography my masters is in geographic information systems so I've always been very much interested in how information is presented visually which on fraca's five modules that she showed is obviously sitting proudly at the top of those five modules is a final what Hans Rosling would call the sort of final six centimeters of the journey just into people's eyes so I'll pause there thank you thanks Alan so why don't we go to the next person on my screen and that's Laura Marie great to have you yeah thank you so much for having me as a guest I have to say I'm a big fan of focus work and also of the women in data science events so happy to be here and my name is Laura as Florian said I'm a professor of corporate social responsibility at the University of Mannheim also position that did not exist before and that way my background is that I studied economics and business management and then I did a PhD in experimental economics and I collected primary data in Indonesia running experiments on ultimatum game bargaining and when I was traveling in Indonesia I witnessed a lot of really the horrific impacts of our globalized markets on developing countries including working conditions that are looking like modern slavery is severe pollution exploitation of the natural environment and so on and when I finished my PhD in 2011 I decided I really would like to pursue an academic career and look more into the topics of sustainability and corporate social responsibility and I plan to use the methods skills that I had learned in my PhD running experiments but applying that to this topic in management and my direct surrounding my supervisors they all said don't do that bad idea you're never going to get a job and at that time luckily I didn't listen I was really passionate about the topic my doctoral supervisor gave me the advice that if I really want to do it I needed three things passion perseverance and good data so I set out to find companies to work with me and to collect data in companies and one of the biggest projects that I did and I have been in contact about this project with Prog in the past was a data collection at IKEA Germany I did a panel data collection with 240,000 IKEA family customers over eight time points so one year and I was granted a lot of opportunities by IKEA for example to manipulate the IKEA newsletter between subjects to include subsequent surveys to interview the store managers and so on I got to media tracking data and so on so I also combined data sources from data sets from various different sources and started to look at them and started to learn a lot about new methods longitudinal analysis cross leg models and so on yeah and that data set actually really gave me the opportunity to publish in these leading top journals which then was my entrance ticket into the academic job market another thing that happened through this big data set I really got a lot of awareness in industry and a couple of other companies asked me to do similar things so since then in 2015 I got the offer to come to Mannheim and since then we've been working with a lot of companies to understand better the topic of sustainability expectations of stakeholders reactions of stakeholders we also have a lot of links to the social entrepreneurship and the innovation community I also met Katrin a couple of times before so yeah exciting job I'm super happy to have pursued this path but it's probably not the most obvious career path in academia great thanks Laura next on my screen is Katrin yes very warm welcome also from my side for the whole participants and also the panel companions here thank you very much for the invite Frauke I really love to be here I'm I think not the classical data scientist so I'm more the user for data data what can you do out of it at SAP I'm responsible for the university alliances so I'm very interesting interested in innovation in exactly such career paths that we listen to here in order to connect dots them so I'm doing this at the moment as a platform as a personal platform me myself but I'm very interested in how can you leverage networks founded on database how can you combine people projects future projects how can you involve trends and so on so we heard yesterday a couple of interesting new yeah also new proof new jobs like decision strategies and I'm very interested in in yeah developing the whole thing so I collect data on my own but I really should do that now because the quantity is huge and the quality also yeah leverage my network by database so if somebody has an idea here I'm very keen and hearing more from you in my main role at SAP I'm responsible for the global innovation portfolio my role just recently changed and I have another connection dot with with this panel here I'm studying since one year now at the Mannheim Business School beside my job and yeah it's quite stressful time but I learned a lot about analytics people analytics and all the things that Mannheim Business School is providing as tools and strategy supporting methods so I'm keen in applying them soon when I finished in November thank you great thanks Katrin great to have you and then last but of course not least Martin and I have to give a special thanks to Martin because he's joining us although he just became a father of twins so he's sharing parentable responsibilities here and so found time to join us so thanks Martin for coming yeah if you hear some screaming on the background it's not because I have somebody locked up in my closet that's my new born girl they'll be complaining but they have to be pretty good today so I'm hoping it will be fine yeah thank you for the invite and thank you all for joining I'm very happy to be here after three years of studying I finally made it on the career panel as the student representative so very happy I could say about myself I probably have a more kind of traditional job when you think about you know working for a survey infrastructure and I kind of rolled into this job but while studying anthropology because many of my my professors had ready wild ideas and plans but they were not so savvy on the technical side usually and I started to kind of help them out like turning some of those ideas into practice and you know doing kind of data management on the side as a student and I think that you know over the years I've worked for many different projects I worked for the European Union and run some of the surveys for them and the more the further I got the more complex the surveys have become and now the survey that I work for is so complex that you really need a really dedicated team to kind of like even manage the data like if we've very far away from having like one kind of data collection event they have several events over several modes and there's like a lot of like data addition that's being done with like secondary data of course so just just working for one survey can be quite complex and taxing and I am very happy that I found this program it has taught me a lot and yeah and hopefully I can continue learning from it it's been a great resource for me personally. Great thanks Martin. Again I want to remind everybody that please feel free to shoot questions at us and I'll forward them to the speakers but I've organized I've tried to organize this panel a little bit ahead of time of course and thought about like okay what could we talk about and one thing that I would ask our panelists to tell us a little bit more is kind of like how their day-to-day work looks like and also what type of skills they need to do this like and where they feel like where have they learned them is this something to have like self taught them maybe where the designated courses this is something they learned back at the university or did they learn something completely different and had to relearn everything once they were on the job so maybe a little bit about that and again I'll start with Elin here. Yes thanks Florian I mean it's fairly easy for me to answer the question of you know did I learn my skills at university or did I learn them since I left you know I am so old and ain't that when I left university the internet didn't really exist so as you can imagine there has been a lot of learning for me to do away from university and I guess actually for me that's a really important challenge for everyone you know I graduated in a recession at a time where our main technological vehicle at the moment didn't exist and similarly students graduating now are going to be graduating at a time of huge uncertainty and you know the most valuable skill that I've carried with me I think from university is this sense of wanting to keep going right like in terms of you never get to the end of a learning journey and if you think that your learning journey stops when you've hit the four or five modules and you've ticked those boxes then you know careers are never normally so neat. I also think that the thing I've learned since you know you asked me I think one of the discussions we had before the event was you know what skills did you have to learn after university and I couldn't really code when I left university not really I mean I did a little bit of turbopascal right but the problem with being taught turbopascal in an academic environment in the early 90s was what's the context what is the motivation you know like teaching turbopascal in the early 90s involved teaching people how to code an alarm or a currency converter and I could see no real practical application of that to what I wanted to do was make maps right like my cartography that I did at undergrad was all with pen and ink right like pure manual cartography. The big change for me came when I learned how I applied some of the amazing technology that was arriving with the internet to things that I love doing which is to code to make graphics and suddenly I had an incentive and a purpose right so abstract skill sets can sometimes difficult things to learn in a pure academic setting when you don't know how well they're going to be used in your onward journey. But the other thing as well is just I know we talk about these things called soft skills all the time right like everyone talks about soft skills that's not a very complimentary phrase soft skills it kind of sounds a bit optional right like the soft skills are actually some of the most important skills that I've learned in the last you know couple of decades and that includes it's not just how can you stand up and do a TEDx talk but it's also can you go and talk to an editor and convince him or her of a story idea and why you think it's so good you know can you can you get people enthusiastic about an idea can you know can you listen as well as talk that side of things and so I've tried to develop those you know soft skills I will call them but I reluctantly I will call them soft skills try to develop those as well as the technical because they will enhance each other so for me it's been you know the university side of what I learned was very good at understanding you know I think I left with a fairly good basic data literacy and a sense of my own domain knowledge in the field of geography but the world shaped so much more afterwards one one side left and when I did my masters I did my masters while I was working at the official statistics agency and the experience of doing a masters alongside work was transformative right that was so totally different to my undergrad because it was much more applied much more focused on particular questions that I was interested in exploring and allowing me to focus. So that's great because we actually got one of a couple of questions and one is directly to what you just said is how did you transition then from official statistics into into the world of media and and what basically is there anything you learned there that do you know I mean I guess there's a lot you can apply now in your job but also kind of like what are the differences maybe could you elaborate on that? Yeah I mean it's a really good question and I'm mindful that I'm being ordered and there are you know nearly a hundred people but I'm going to be quite candid in my response to that because it's a very good question right how did that happen and why did that happen? So I already realized that my work at the statistics office was I was working with data visualization at the statistics office and the reason that I was doing that work is we started I think in official statistics to start to there was a fundamental change in the role of official statistics agencies happening and I think it's very relevant to data science which is historically official statistics agencies thought of themselves as number factories their job was to produce numbers in a factory sort of kind of workflow and then to just publish the numbers to leave the numbers outside the factory for other people to then you know to collect the numbers and go collect. When the when the web came along I think it started to question some fundamental principles of official statistics and you know official statistics started to rise oh hang on we need to use the internet to communicate and very quickly this phrase dissemination this word dissemination appeared official statistics agencies started saying oh we're going to disseminate our information again I don't think that's a very good word for data because it's an agricultural term and it's the one where basically a farmer can throw seeds into the field and not particularly care about where they land some of them will grow some of them won't. What we increasingly started to realize is that we actually needed to turn into much better communicators in the world of official statistics like you know the insight that you collect as part of official statistics methodology that's valuable insight that should be shared with the world and we wanted to do as much as we could to not just connect with the traditional policy makers and business decision makers but with a much wider group of people you know interested citizens and so that's where that work started from and that changed our view of what success historically success for statistics official statistics was nobody complains that was a great release because nobody complained now the easiest way to make sure that nobody complains is to make sure that nobody looks right to not advertise yourself particularly well but actually we changed our mindset and we wanted people to look we wanted to generate debate and discussion and so therefore I started to talk much much more with people who were working in the media and we started to syndicate our content to the media which was an interesting thing and eventually talking to some of those people in the media led to them saying well look these skills are very valuable that you've learned at the statistics office would you like to come and work in a newsroom doing them and so that's how the journey happened and the biggest difference between those two environments is simply speed so rather than ten years to do a census I usually have 24 hours to produce you know quarter of a million newspapers but you get the idea that this the processes are very similar but the speed is slightly different. Yeah great thanks that's that's very insightful I want to move on to Laura you already mentioned a lot of the skills you have learned as part of your PhD and working with this type of data any other things that you feel like well in the past couple of years this is something that I can't live without anymore these days in terms of the skills and also maybe you mentioned this already can you talk a little bit about this intersection between academia and industry and how you made this decision of pursuing a career in academia but it sounds like you still have a lot of like connections with industry. Right I also just wanted to pick up the question from Annika from the chat how did you choose your industry academia versus corporate and for me this was actually a tough decision because my main motivation was to change something in the business world and I had exactly that question where can I be more effective going into companies or staying in academia and my decision actually was based on the rationale that in academia I was I would have the maximum freedom and the independence right so if I would be in one company I would actually have to work along the lines of the agenda of one company and if I'm in academia I'm independent and I can freely choose the topics and my position on the topics but I also had the idea that if I go to academia I will really want to build strong bridges to the business world and actually engage with the real world out there and not just be an academic that's you know running data analysis and publishing papers behind closed doors. In the last five years in Monheim I've actually spent a lot of my time building these bridges trying to collaborate with companies and also talk to companies not only the ones that are super sustainable but also trying to understand you know the processes the hindrances and motivations of companies that are not really transitioning so really trying to understand the struggles and go into a dialogue there. And yeah you said so what are the things that I needed on the way I also like Alan I come from the past where in university we learned toro Pascal we actually programmed with pencil on paper in the exam and I had to learn everything basically that I need today by myself and as Alan said it's a continuous journey right you always learn new things along the way and I think the best thing is just to have a problem and then learn by trying to find a solution right so really working on the stuff and by working on it learning how to do it when I did my master thesis I started to do structural equation modeling then during my PhD I used experiments and later on longitudinal analysis and when I started to learn about data analysis there was actually nothing like YouTube tutorials or anything right so I really had to go to the library get a book from the shelf and read a book so I mean all of you guys out there that are now studying these things I think you are in such a luxury situation that you can just use online tutorials and videos and MOOCs and everything like that so I think that's such a new world of learning and I totally enjoy it now when I want to actually acquire new skills and about the soft skill question so I think as an academic that is trying to build these bridges to industry for me it was super important to actually have these soft skills of trying to understand both worlds right because academia and the business world are usually very separate and there are not so many links and if you ask practitioners why don't you work with academics they say oh you know they are they just interested in theories and not in the real problems and on the other hand if you ask the the academics why don't you work with a business world they also have a lot of stereotypes right and say you know the practitioners are not interested in gaining knowledge they just want to solve a talk problems they want to increase profits so I think there is a lot of potential but you need these soft skills of really understanding the different stakeholders and trying to bring different parties together to to build these platforms and that's I think the strongest skill that I had to learn and again there I think I'm still on the journey of learning from university I have to say I don't I didn't get so much that I use today I also got the maybe the skill of perseverance having big lectures of 1000 participants sitting there trying to figure out how to solve the exam but everything basically that I need on that I need on the job today I had to learn by myself but I have to say that I had a very passionate doctoral supervisor and he really taught me this passion for research and I think if you have that then you know you have the right intrinsic motivation to also go this way right and find the necessary information teach yourself how to solve these problems and I think this really needs a lot of passion for problem solving and just you know not being scared of starting just getting started and just trying to do it. Yeah and today what are the things that I'm trying to learn I if I have problems today that I want to solve I use YouTube videos tutorials MOOCs and so on for this summer I have a long learning list again things I would like to know more about and I actually want to use a couple of these really cool upcoming new online courses for example to learn more about Python and I'm also very lucky to be married to a computer scientist who's also teaching me a couple of things like programming machine learning skills and so on so I think it's really a continuous journey and I for me the biggest learning was that I need a problem and then I you know if I have the passion to solve the problem then I will find the solution and the resources are getting better and better to solve these problems. Thanks Laura I'll move on to Catherine because you mentioned already a couple of things and especially I think it's a great transition because Catherine as you told us you're in charge of the university alliance so kind of like this this this intersection again between academia and industry so maybe we can tell a little bit more about that but also from a personal perspective what made you come back to now the modern business school and make this decision to after potentially being out from the university for a couple of years pursue kind of like another degree or learn something new. Yeah it's quite a couple of years you're right so the first two days were a really interesting journey for me I thought I will never stand sitting nine hours here in the audience lecture hall but yeah finally I was perceived variant enough to do it for one year now already and soon I hope that I will have finished it because it's quite the double with family it's three columns that you are at the moment serving us three pillars and that's quite exhausting I must say so I really need the vacation afterwards because you don't have any weekend it's really beside your full-time job so I think that's exactly what what you said Laura it's lifelong learning I think that's the most important thing and yeah so find your I think the most important item you have to find out is find your your strengths and and your and stick to it yeah don't let you be influenced by people saying you will never learn that you will never be able to do that if you have the impression that it's the right journey just go for it because you only can't be you only can be the best the most the best person in in something if you really have your your own confidence in yourself and and know that you can do it so I think lifelong learning find your strength and safeguard your your values and energy after afterwards when you when you find something that gives you energy that gives you success then try to to elaborate on this and on this area and try to be an expert in it so that's something yeah also to have a like a break and not only run and go forward forward forward but also take your time and if you have the impression you can serve to something or you can learn a lot in one area then stick to that and try to be even better in it so then comes I think because I have a quite long experience also now a professional wise there comes times where you have to reinvent yourself that can be if you give birth to a child also Martin I think he will recognize now that there are things becoming more important suddenly than your professional career it can also be negative things and now in corona times I think there can be more and more people and families also affected by things they never would have thought that happened to their families so it's something like resilience try to to take life and also of course professional challenges like they come to you and not try to to to fight against that sometimes you have to stay with a flow and and make the best out of it even if in the first glance it's really you think I know why does that happen to me now there's always some sense behind I think and that's why I think why you need to be courageous innovative yeah make like resumes about yourself how went the year and what do I want to to go further I always always do mind maps about myself not because I'm a narcissist or something and it's only to to have a point in time where you can where you see okay that's my that's my balance sheet at the moment and I have these goals how can I develop further myself also and if you have children you know that you have to do it in a dual way you do it for yourself to get forward and to learn more but also for your children how can you motivate them to think about themselves also and not only to stick to school grades and and learn the stuff in school but also to learn things around the school and the education items and that's that thing I always learned is what I live and that is something that combines a lot of people here in the audience and in the panel so shaped your future because it's your future yeah that's why I love now to to teach and I wanted to become a scholar professor when I left school and my parents said no no there's no chance to be a professor there are too many and not enough jobs don't do that so I choose another thing I became a bank clerk first what was completely wrong for me afterwards I discovered okay even something that you didn't like like a bank formation can help you later because now when I counsel startups or mentor people there's always the balance on the bottom between cost and revenue is always filled in because I know how important that is to convince investors or people who can help you so shape the future in a co innovative way and mix teams that's because I'm a design thinking coach since 10 years and I discovered that linear planning you can do it but so you have to revise your planning often so I choose now the way to be iteratively planning and look what comes to me and then try to integrate a new project and less than you have of course drop other projects that are less vivid so in the iteratively planning and not only linear that's very important you can't apply that to everything that you need to play both methods of planning in my view and to the intersection of industry and academia I'm also glad that I got this job in six years because it's exactly what I always wanted to do combine the bridges like Laura also said and focus also doing this every day so not only staying stick to your world where your comfort comfortable but also look 360 degrees around yourself your world and try to combine other areas in order to get innovation disruptive ideas and teaching at universities that gives a lot to me because I learned from you from the younger generation how you solve problems how you think how you capture your we have that already how can you gain new knowledge and it's really completely changed from the way I learned at university so I'm keen now at the Mannheim Business School to learn how my peers there which methods they have to capture knowledge also and to gain more insights and at SAP I can really say I'm an entrepreneur I have like my little own business insight SAP university life so it's also possible to free yourself with a couple of years that you have experience and know people and yeah be your your own entrepreneur even at a big corporate so I'm looking forward to your questions thank you so thanks Catherine I also want to before we we already got a couple of questions that I want to for the right way I also want to briefly give Mark Martin a chance to also tell us a little bit about his story why he decided to for example start at IPSDS and and what were the motivations there what were the why did you feel you needed something to learn more like what what what was missing more or less in your your life that made you go through that right I had too much time I needed something to film my time and now I have twins though I mean I should stop now funny enough it's it's has something to do with what Ellen called soft skills which I also I don't like that term either but I think I was hired in my team to be kind of a conduit between our IT staff and our academic staff and I during my time there I realized there is a ton of miscommunication going on all the time between and if they go back and forth between design and implementation and then you know you lose so much time fixing for mistakes and miscommunications and I saw this program and thought that it really fitted that bill you know it's it's it's it's kind of like in in the middle layer although I still think that maybe it could be a little bit more heavy on on information science for my taste but it's it really has a useful resource for me to think about also developing those soft skills in terms of how do you communicate between those different aspects of my work and you know it another thing that I think is really great that I want to impress on young people thinking about taking this program I mean I I I the resource is not only very very broad and very interesting in a interdisciplinary aspect but it's also extremely flexible which I now extremely appreciate I have like little hours in between the day like at night or whatever and I can just always now take some device somewhere in my house and do you watch a lecture video or do some homework or whatever it's it's great to for that as well so it's easy to fit in my in my in my day but yeah I I think just this kind of like interdisciplinary kind of aspect of it was really kind of key for me because I was looking at other programs too of course you can go deeper into statistics or you can go hardcore information science or something like that but I feel like this program has a good balance between the two thanks Martin so there's already more questions coming in so I'm ask our our panelists to answer them so one one question came for Laura asking how much interest is there actually in the business sector and using accurate and reliable data for decision-making on strategic priorities such as the sustainable development goals so you have worked with a number of companies as I take it so maybe you can stay a little bit about that yeah I think that's a very interesting question so and I'm a little bit ambivalent so I can explain what my experiences are at the beginning of course there is a lot of interest from the business world because they know that they on the one hand they know that data analytics are important on the other hand they have to start to report about sustainability since 2017 we have a EU regulation that tells companies that they have to report on non-financial performance so everybody wants to explore ways that they can use data on understanding their social impacts and environmental impacts better on the other hand I have to say that if you start talking to the companies about the specific processes and the necessary steps then you have to do a lot of explanation work right because there is this diffuse idea of what you know a scientific study is going to look like but if you then go into really working on it of course you have to yeah you know go along a lot of ways that take time good science takes time you have to do a lot of explanation why you need different data sources why you need a good measurement for example why you cannot go just with a single item or in some cases why you need objectively observed behavioral data and so on and I think this explanation work is really important and understanding the other side and telling them you know this would help us to understand that in a lot of my collaborations I had to invest a lot at the beginning to do this explanation work but then if the companies see what they can get out of it they really get interested and they are willing to go this long away and doing the work for you know good science and I think that sometimes it makes sense to go step by step right because what the practitioners often need is some quick wins that they can show to the management so they need something like numbers to show okay look at this this is great we need to go further and as a scientist maybe sometimes we think that you know we have to produce the perfect results first and then after one year or two years we can publish that in a scientific journal and I think for the collaborations with businesses in many cases it's important to really give them these quick wins and to do a little study first and show them you know look at this this will help you to explain to your management that you have a positive social impact one example in our field is really social impact measurement and a lot of companies don't even believe that this is possible they know that they have a lot of data but they say you know can you actually measure social impact and then when you start to work with them and you give them little quick wins and say look at this data you can go to your you know to your manager and show this a couple of slides then they are really motivated and say okay how can we do more let's sit down and I think that's that's important in the process that you understand that the other side needs to learn what what it takes to produce valid results right and that's the explanatory work that we need to do. Yeah great thanks so there's there's somewhat a related question came in for for Ellen on this relationship or the role of the new or big data sources comparing that between what's collected in the statistical agencies and in the national statistical offices and then now on the other side for for the financial times right and in the media so do you see a shift happening there also from from the most traditional players such as the national statistical offices using them or do you think there's still like a big difference in how and what type of data the different places are using? Yeah I mean wow there's a lot to unpack in that set of questions I mean the first thing that I'd say is that I left the world of official statistics just as just as this real interest in alternative data sources started to become not just not just an opportunity but also a pressure because there's an assumed knowledge now I think a public knowledge that there is data out there and the longer that the world of official statistics didn't use it the more questions there would be and but of course it does bring with it a whole question of the legal framework for doing so how do you prevent this becoming like the wild west of you know having spent years trying to get survey methodology workflows in official statistics organizations you know as as well drilled and as efficient as possible how do you introduce this chaos of data that was not collected for the purpose of your survey to be used for your survey you know that kind of thing and I think it's been really interesting for me personally to watch that develop so much in the last few years from a slight distance so you know if you go to my old UK official statistics office you know they now have a very well established data science center that did not exist five years ago and so to see this investment in official statistics being able to leverage these sources of data I think is tremendously tremendously useful and in fact you only need to look at the current crisis to see how valuable that has been because you know the UK public official statistics have been super responsive during coronavirus to be doing all sorts of really interesting things on on much tighter timescales than they are used to do to doing and I think the really interesting thing in the slide that Frauka showed earlier was this this complementary set of sources allowing you to use two very different techniques to get two very different angles on data you know for from using sources like big data that are maybe less good at answering some of the absolute questions but very quick and responsive and relative right and positioning those together is quite a powerful proposition I think the one thing I would say from the Financial Times perspective is obviously I'm working in a sector now where the commodity the end product is not the statistics the end product is something slightly different and we are obviously in a world where we are trying to play our role in the reporting that we do about kind of responsible capitalism about about making sure that the direction of business is is sustainable and responsible and you know things like the ethics of data is an absolute story that we are you know partly involved in in telling you know we we have writers who come in and do pieces that I mean people like Shoshana Zuboff and that the surveillance capitalism space these are things that our readers are tremendously interested in you know the ethics of data and the which data is starting to become you know it's it's a bit of a cliche to say it's the new oil because there's lots of reasons why it's not it's something very different but we're working in a very very different space to the official statistics agencies and my biggest my big lesson when I left the world of official statistics and arrived in the newsroom was to realize how lucky I was for the previous 15 years working with teams who had such who always gave me very high quality data to work with that was well formatted that was well documented that had a methodology that was explained because the use of data out in the world where I work now it's just generally messy from the start right like and it's just half of the job is working out what you can use and what you can't and that kind of evaluation of data quality particularly when it's data that's not come from official sources is a huge challenge for us I'll just finish with one quick example I had it's not uncommon for this sort of thing to happen in my job right you will get an email from a PR rep who will say we've got some exclusive analysis on property prices in the UK and we've got some shock results about which areas have gone up the most and it's not where you think now my you know my my sort of first guess was it's probably London where the prices have gone up the most and I looked at the data that they sent as part of this press release and there were no London places in it at all and I thought well this is really crazy so I better ask what they're doing here and all this agency had done had been to take an official statistics data set on property prices and remove the London locations in the data to make something more interesting and then repackage that data and push it on and so like here in things like this you start to see how misinformation spreads that there's this kind of a very badly policed area out there where I think actually you know the world of official statistics is obviously a great reference point for best practice and for the right way of doing things but it shouldn't stop there right like we've got to make sure that our society is functioning along data literate lines and I think that's a combined effort I'd like to think that both official statistics agencies and organizations like mine would all see a part in in resolving that yeah so yeah thanks thanks Alan so I see I see more questions coming in especially for Alan but we will hear more from him very soon so at this point looking at the time I would really want to thank all our participants here the panelists Alan Smith, Laura Marie, Eding Ashons, Katrin Riedmann and Martin Komen, thanks for taking the time to talking to us let's give if you can like a virtual round of applause and I believe the next time point is that we take a short break and we will be back at 4.30 for the talks I believe Frika anything else we need to say at this point? No that's it Anika will advise us if there's anything she wants people to do here but mostly we wanted to give you the chance to go to the bathroom without your cell phone while you're listening to us or make yourself a cup of coffee and then be ready for the talks and questions and discussions afterwards and my two heartfelt thank you to the panelists I always love the career panel and every time I'm learning so it's never end thank you all thank you Anika can you say a few words say you're on here yeah we'll share the program during the break so if new people come in they know where we are and yeah but that's it I think everyone else can leave their videos on or turn them off whatever we'll see you back here at 4.30 thank you all it's good to have you all here and it's wonderful to have Ellen Smith here who will do one of the two keynote talks now the first one is by Ellen and the second one will be by Johannes Abelo and we will have a quick Q&A session after both of the talks and I will moderate this with together with my colleague Markus Heckerlotz from IPSDS and it's wonderful that you're here and it was already interesting to hear all your your insights into your career path and into your career at the financial times and your talk today will be about data visualization and journalism in the age of coronavirus and I do not want to steal any time from you so without further ado the zoom floor is yours thank you Caroline and let me just share my screen so that you will stop seeing me in my spare room and hopefully see something a little bit more interesting which is a presentation here so I hope you can see my screen okay and you might be thinking that with a talk title of data visualization and journalism in the age of coronavirus why would I start with a picture of a cat sitting in a tree well this kind of takes me back to my early discussions about data journalism when I was still working in official statistics and I was very skeptical of the field of data journalism I was at a conference in Australia with an economics commentator from the Sydney Morning Herald Ross Gittins and I said to him Ross do you have data journalism in Australia and he said oh yes yes we have data journalism in Australia now Alan he said because rather than report that the cat got stuck up the tree we can now confidently report how far the cat got stuck up the tree and so journal data journalism for me this was very much my impression of what we were talking about with data journalism is that not much of it was really data science that a lot of what we were seeing was the spurious use of numbers to try and kind of inject some sense of analytical work that maybe wasn't necessarily relevant and the reason why I kind of start with that is that even with the coronavirus and even at the FT the very early reporting on coronavirus was of that sort you know these are not quite the cats in the tree but this is what tends to happen when the news cycle is happening very very quickly and information reliable information is difficult to to come by the the little information that you have and in this case it's the number the daily changing cases inside China this was from very early in the story in January was almost the best that you could do in very very sort of quick turnaround circumstances and unlike a lot of other news agencies I think we're very aware that when we do something like this it has a very much a limit in terms of how long that sort of reporting is going to be able to be uh carrying a story so you know I think throughout the coronavirus we were seeing a lot of news agencies just throwing data onto maps and charts in the hope that some of it would stick what we realized very early on with coronavirus was that actually this is an opportunity for data journalism to really come into its own in the Financial Times newsroom and so we set up a page at the FT specifically to track the data that we started to collect from places like Johns Hopkins University from the European Center for Disease Disease Prevention and Control and lots of data sources that as we were starting to work with them we were assessing are these reliable sources of data and if if one source of data on its own is not reliable enough for everything how are we pulling together data from different sources to make it so this page became it was set up very early in in in the story and we started to put content onto this page that went beyond just the simple maps of cases and deaths I also thought I'd just draw a point that when this story first broke of course it was a story that broke in Asia and you can tell actually even just from the photos on the page that at this time this had a very strict geographical focus to the story as the story has developed we have changed you know as the epicenter moved to Europe as it moved to the US and as it has now moved indeed to Latin America the use of things like photographs is actually an incredibly important part of our visual journalism which is to to carry empathy and understanding of the human conditions that data alone maybe can't you know so these are composite pages that contain not just data visualizations but also some explanation some links to other kinds of content that are related and as we started to add content to this page one chart type more than any other started to become to resonate with readers and it was this chart which was created by my colleague John Byrne Murdoch and it was a chart that's been you know a long time in the making and refining which was pulling data together from lots of different sources to try and come up with some sort of form of meaning for international comparison about what was going on and working out how to present that now so that's a point to stress here is that the way that these get created is not that somebody goes away and does traditional academic research on a deadline and comes back just having worked you know it's not just a case of accelerate in the normal processes the process is the generating these sort of visuals are generally very iterative you know post something look at it see how people are looking at it you know look at the responses and evolve the contents of these graphics evolved a lot over time and we've paid a lot of attention to things like for example if you can notice that there's a log scale on the on the it's like a semi log chart because the the y-axis is a log scale and you know we spent a lot of time thinking you know is this the right kind of scale for this chart will our readers be able to understand what's going on will they be able to recognize that this is a chart of acceleration not absolute levels you know and and so on and the chart certainly was popular enough I mean it went completely viral and it's a chart that's certainly was popular enough I mean it went completely viral I think John had to stop checking his Twitter account at one because it was just so amazingly popular on Twitter but it one of the things that was really fascinating for me was that LSE went on to do a piece of analysis where they said well you know actually the public do not understand logarithmic graphs on these COVID charts they said not only do they not understand them they're better at reading the data if you just give them data in a linear scale format which I think this is really interesting because this is the sort of dynamic that has been rated by the crisis you would not get this action and response in normal times though so this has been very very interesting and I still have some issues here about making hard and fast statements about the public do not understand logged graphs you know like that this is definitely a period where we can start to push people's data literacy and understand allow them to understand why we're presenting data in a certain way I've written articles about what in the FT about why log scales are a good thing and why you should learn how to read them so this is a log scale of the Dow Jones industrial average right and when it's on a log scale you can clearly see that it took 25 years for the index to recover from the Wall Street right on a linear scale it looks like that and I'd like to give that to the LSE authors and say yes the linear scale should be used in all circumstances you can brush the Wall Street crash under the carpet very neatly with it and so I've started to write some articles in our work and career section of the financial times I've had a pause for about a year now but we're going to start writing these again soon about the decisions that we make when we're presenting information and this is an area which I feel has traditionally underrepresented in academic space both on the arts and the sciences side for very different reasons and one of the things that's lovely is that this is an area that's right for huge amount of academic exploration and I do you know this was one of my favorite projects from last year which was sonification of economic data 40 years of the US economic data on the marimba you know some really wonderful opportunities to rethink how are we presenting data to people and should we be and this was quite an interesting process because what started off as a little bit of a sort of pet experimental project turned into something very real when I realized there were huge accessibility opportunities from presenting data in sonic form and working with you know I had somebody who was blind drew this US yield curve for me from listening to this music and it was a fascinating sort of project to work on but anyway back to the chart our solution to improving this so that people could not complain that it was log or linear was to make it completely interactive okay and so if I can just I'll just I'll just come out of this presentation mode so that you can see this in action so this is this is the interactive version of the coronavirus tracker so you can select your own countries that you may you want to make a comparison between so we're looking at the rolling out just seven-day rolling average of the deaths in Germany attributed to COVID-19 and we can compare that to other countries so you know let's put that in here and you know this is a simple sort of form of interactivity we can flick between death and cases we can switch between cumulative totals and daily cases we can also do the famous shift between linear and log there we go as the United States do very very well on the linear scale and we can switch between the raw numbers and also the per million because actually that dynamic changed quite a lot as the story developed because a lot of outbreaks start in localized instances actually the raw numbers were enough in early on but as the virus said it made more and more sense to standardize the data using per million so we tend to use interactivity when it's important to personalize the content around the reader and what we see generally is that interactivity is very often used very lazily like if your only use of interactivity is for someone to mouse over something and for something to pop up that's not enough it needs to be deeply personalizable and indeed the personalization that I make on this chart if you can see at the bottom of the screen if the url becomes fully customizable so as you make amendments to the chart you have a unique url to your view of the chart and you can share that with reporters with readers with people on social media you can use it to start debates hey look at this log scale chart of new deaths between UK and Germany and people can deep link to those sort of visualizations so that for us has been incredibly important and we've been using a full suite of data visualization techniques try and visualize for people how this crisis is unfolding with all of the graphics designed to try and look at some sort of angle on on the the evolution of the pandemic and so for example this chart here which is a something called a stream graph was our attempt to de-emphasize the map right like we were seeing a lot of maps where people were just putting bubbles on maps everywhere right that's how you visualize a pandemic but actually yes this is geographical data but actually it can be quite useful to see the geographical data in a different form and so this stream graph allows us to see this sort of onion skin style evolution of the crisis and this kind of shift in emphasis from Europe to North America to America becomes much more evident than it would do on a on a map and actually the metrics that we're using for describing the crisis have evolved over time as well it started off with cases and we know that there are problems with testing and you know international comparisons based on different testing methodology COVID it's incredibly difficult as is the reporting of deaths because every country has slightly different methodologies for for the techniques that they use for births and deaths and so this becomes difficult and so instead what happened was the team collated excess deaths data internationally so scouring lots of different official statistics websites and official sources of deaths data and making comparisons between deaths now and the equivalent deaths in recent years and one of the things that's been important on that to make this honestly tell you if you work in one statistics agent that's very different from trying to be a user of your 40 statistics agency websites so the pain in assembling all this data together we ended up sharing this data publicly on the code sharing site github so that's all of this combined csv data for excess deaths publicly available for people to take the data and use it which i think is a really interesting that conversation we had before on the balance between official statistics agencies and news organizations that for me this is quite a sophisticated approach for news organization to be doing with to be actually supplying some of the underlying data there is traditionally no expectation that we would be doing that and then what we realized in the process of this evolving story is that our role as journalists was changing produced on off stores you know like the newspaper story that you know is front page news one and then is like in the in the in the rubbish bin or the recycling bin the next day we were actually moving from kind of storytelling individual stories to managing products managing data products so that involves as soon as you have pulled all of this information together of course you get increased demand from other journalists for for our help because data is at the heart of so many stories at the moment and journalists who are not who don't feel themselves data journalists are coming to our team for advice and for help and we also have increased scrutiny from readers and what do i mean by that what i mean is the page that i just showed you our coronavirus tracker is the most viewed page in the history of the financial times website ever by a magnitude of about five right so data journalism has absolutely dominated this crisis and with that we put data in front of a huge audience relative to our normal audience which means everyone is asking us questions about the data and the techniques that we're using for visualizing it where we're sourcing the data from and so on and in fact that data sourcing and updating is become a huge part of product management for for the stories so what we did was we set up a dedicated email address on the page saying help us to improve this if you if you know of some sources of data that we are unaware of please tell us and we'll try and incorporate this into what we're doing and just to give you a preview that's the sort of response that we were getting in the inbox on the back of that request you know everybody from sort of talking about that i've tracked down some all-cause mortality data for Serbia for the last five years these are the sort of emails that are precious to us because it's allowed the team to respond to reader feedback in huge numbers and you can see on some of the tags there some of those tags are academic right which is again a dynamic of the crisis right that is definitely something that's new and should be encouraged um we of course have then been using kind of techniques for visual journalism to take the data that we've sourced and bring it into the storytelling space and for me when we were talking about the skills that I didn't learn at university earlier this is one of them because at the time no one was teaching people about how to present data and I think if that was one of the things that I'm most happy about the evolution of data science compared to statistics as a as an academic field it's the emphasis that data science places on being able to communicate what you've done at the end right like that's tremendously important so this is the financial times visual vocabulary this is our own resource that we've created at the financial times we have it big on our newsroom wall it's designed to allow people to to visualize stories based on the relationships in the data that they're working with and you can get hold of your own copy of this just if you go to ft.com forward slash vocabulary um and just to kind of round this off we've made it multilingual because guess what presenting data is a multilingual international language so this chart's available in chinese and japanese and spanish and french I loved learning that a pie chart is a camembert chart in in France um you know this is all good stuff um how does that what does that look like it's the thing that allows you to decide should I show the changing lockdowns of the world on a map or on a chart you know how different is how important is it to show changing patterns as opposed to spatial patterns you know and the answer in this case was to do both because they were both offering us something very very different about what was happening with the the government responses around the world um diagrams that have come out of biomedical science are pretty good for showing how airlines have been affected with their origin and destination routes um charts that look like petri dishes are pretty good at showing the difference in financial impact on the share prices and stock value stock market value of different kinds of company um a whole range of chart types that our readers probably have not seen four or five years ago have become a default method of reporting on stories with kind of a much broader range of chart types based on the underlying flows of data so this is this is this is trade data that is core data this to an ft reader understanding the world of trade but it's actually also quite complex data because trade flows are matrices right like so you need techniques for visualizing data in matrices of which this is one um you know we had a great design decision about this one where we were trying to show the fact that if you could believe the chinese numbers that they had managed to contain the disease to one province in general terms and that that was very different to the us and we were tempted to do that using a map but nothing seemed as effective as doing it like this you know just to use a slightly different technique um and a lot of our charts are actually relatively straightforward but the the the thing to to note with the design of these charts well there's a couple of things these charts are all what I would call um they are like visual rhetoric okay they are meant to make an argument to inform some um interpretation or view of the data so this is different to the world of official statistics sometimes where you're just saying here are the numbers so if you look all three of these charts on screen have a narrative title they're actually trying to guide the reader on how to interpret um the data and that it's yes it's important to show what the data is and that can happen in the subtitle right like in in the footnotes um but also we're very keen to try and draw people's eye to the data we want people to look at which is in this case the highlighted colors you know and the other colors here are for context um alternative data sources have become incredibly important to us energy usage google maps football data these are all things that allow us to understand how is the world changing um some data was more difficult to collect than others this is the we did a huge amount of work uh Steve Bernhard and David Blood analyzing how much flight patterns have changed between April and between January and April extraordinary stuff um and so our language of visual journalism I think has evolved through the crisis uh we had done some of this stuff before but the thing that's been really fascinating has been to apply this kind of thinking on deadline in a rapidly changing story space and and the results of that I think have been very warmly received by our readers um I ought to say not everything is a chart right as well as the visuals visual storytelling can take all sorts of different routes and actually illustrative journalism that explains complex technical uh and complex science to people who are not scientific specialists is also a hugely difficult challenge and we're lucky to have a couple of visual journalists who specialize in this type of visual journalism which has been again tremendously useful as our understanding of uh COVID team has developed um and sometimes these things can combine this is kind like a chart in illustration you know how far does a sneeze travel and how a cough these are sort of very interesting sort of and real world examples um the thing that I'll just finish with is to show you that one of the things that we can do on the ft website now is people can actually if you can see in the top menu here we have graphics is its own thing right and we have a searchable database of all of our visual journalism so you can search for coronavirus and it will allow you to see all of the coronavirus graphics that we're producing in a chronological thing or if you want to go to donald trump you can search on that you know um that has also been a really interesting thing to do because using graphics as the way of discovering stories has become an important part of what we're doing as well so um you know data journalism is definitely not new it's definitely more about than about how high the cat is in the tree um but I think if there's one positive that people are looking for in the world of data science for their careers is um here it is right like organizations are embracing this sort of work at the heart of their core business model um so I'll stop there and hopefully you've got time for a few uh questions thank you thank you so much and my my head is spinning from all these beautiful graphs and visualizations and I don't know which one I want to first look at in detail um after today so thank you very much um we already have some questions uh from frouker uh and she's really interested in how many people are there on your team how many people are producing this multitude of graphs of visualizations and how do you get to those designs so I'll talk about the team first because I'm very proud of them they're all brilliant um we have got a team so overall the FT's got around about 600 journalists and my team is about 25 people um and that's about 20 or so in our nominally based in London or certainly UK based now um and we have two in New York or one in Hong Kong um that covers a real range of disciplines though we've got everything from sort of graphic artists and visual journalists to data journalists and statistics experts and so um the way that we organize ourselves is to try and take advantage of that overlap in skills and for me this is a big difference so like 20 years ago if you were graduating in statistics you were probably anticipating if you're going to work with other statisticians right like that you you cover less with groups of people who have the same skill set and interdisciplinary skill sets are actually in my view much more challenging and rewarding and equal measure right that you learn and value everybody else's skills and try and integrate them um in the context of how we do things there's one thing that will determine how what does the process look like and that is time right like if I've got if I've got two hours it's not unusual for somebody to say hey Alan somebody just said something in parliament at 3 p.m. and we need a we need a chart of it oh by the way we're going to print it a quarter of a million times at six o'clock right so you've got two hours or three hours to just from what is effectively a blank page to come up with something that is ready and finished and obviously you're going to do that in a different way to if someone says hey Alan next month we're you know we're doing an investigative piece so we've got one month to run at something well we're going to take something for three months um so we have a we what we do is we assign people so that sometimes we have a group of people who are we always have a group of people who are covering the news cycle and their job when they arrive on those days is just to cover rapidly changing news events you know it could be an earthquake somewhere you know obviously in the world of covid it will be something somewhere right like but something that we didn't know about yes but that we need to know about today um and so people uh spend some of their time in that space and some of their other time on deeper projects like something that you know is going to happen like the us election well you know you're going to have six months to plan for that for example and you can approach that differently and i like to make sure that everybody has some time in both contexts because if you have three hours to do something it certainly helps with your decision-making skill set right like you know it focuses the mind and but then again you know it's very different when you have a longer term project and you need to manage your time and understand what's possible within a longer time period so it's it's challenging and rewarding but that's kind of how we do it yeah um people have been asking about your tools like do you autograph visualizations with pin and paper and then when you move to um to the computer which which kind of tools do you use yeah so apple pencil is the best of both worlds right like i use an apple pencil all the time for sketching and for doing things and it it feels organic but it takes me into digital space um we we we use a range of tools um if you're a data scientist most data scientists will be championing python or r or something like that you know those tools are used in our team and that's great for people who are learning those skills um but some people get by on excel because they're doing something slightly different you know um we use a lot of web programming skills for our visualizations things like d3 we use a lot um and what we do is we have a big library of those visualizations pre-created and pre-formatted for print online social media so that we can generate them very very rapidly if we use something like our visual vocabulary as a decision-making framework that we can go very very quickly from sketch to finished graphic we have another question um for the music project that you did and how did you get the idea to do that well that i that was one of my favorite things because music is a sort of slightly uh separate in the keyboard in the back there right like there you go um i have been doing some work which involved animating some us economic data that was how the visualization started we were we were animating some us economic data and then the video team came to talk to me about making a video about that action and the guy doing the video tom said you know what the big problem here is he said it's very just just watching this animation he said i bet you can't you know can you do a sound track um so i kind of experimented with could we use the data to not only drive the visualization but connect it to a synthesizer to drive some sounds as well i mean that we made that connection i was super excited because that's just classic serendipity you know i had absolutely no concept of what we were doing until we just got to a certain point with it we have a lot of questions rolling in and we have very little time but i want to ask one more questions and maybe and you can just look in the chat afterwards and maybe answer one or two more questions that would be wonderful um christoph is asking uh at the financial times do you differentiate between decision making graphics for politicians and informative graphics for a broad audience or don't do you do not make a difference between those two i think i think i mean we produce a lot of graphics i mean i did a i did an audit of this a little while ago and i think i came up within one month we made 800 graphics um and that even that doesn't include the newspaper or social media but just on the website 800 graphics and i think that's probably almost too many right like i think i think where we want to focus our graphics the distinction that we make is i want people to be slightly surprised by what we show them that's the big thing because i think that's a sign that if you want to inform decision making that you might be correcting some preconceptions for example right and that doesn't matter if you're a politician or just a reader trying to understand where the virus is spreading right like that you want to be at least partially surprised by the data and if all you're doing is showing people charts of things that they already kind of know um that's maybe those are maybe the charts to not make thank you thank you so much for your participation in the career panel and for your keynote and also thank you for asking all those questions that are still coming so a lot of interested here thank you very much thanks carolina and i'll do my best to answer any other questions people might have in the chat thank you so a round of applause for elin and our next speaker will be johannes arvila johannes arvila is an associate professor at the university of oxford and he currently focuses on the role of tracking apps in the corona pandemic and his talk will be on this today and we are very happy to have you here johannes and the floor is yours thank you very much um let me share my screen um so before i start i have to say i'm a big fan of of of graphics not just for sort of in in my newspaper but also in my in my work so i can already tell you that obviously academia has always been very graph heavy but this is going so much stronger recently and it's perhaps sort of it's a cycle that sort of people read it in the newspaper and they also want to read it in the academic papers but when i now see papers that used to have basically just tables now you often have the tables basically just in the appendix and the main story is told in graphs so i can totally see that i'm a i'm a i'm a very big fan of of of graphs and figures um to the dismay of my default students who always have to redo their graphs again and again um so today i i i want to talk about the step before the graph mostly in terms of tools and in terms of methods i want to talk about how we get the data and then also talk about the data we got um i have to warn you that uh today uh the internet at my house is sort of a bit dodgy so if i'm if i'm if i'm leaving the presentation it's not about you um and don't you worry i'll be hopefully back in a minute um but i hope it lasts for 20 minutes at least so um i want to show you some data about the acceptability of app-based contact tracing which we started in uh this project we started in march um and then at some point some some names that you that you might know frederick frauke and ruben uh joined us so it's sort of a it's a big international cooperation now um so when we started this in march nothing of the things that we've seen in the last two two months were sort of very much possible we knew back then that or sort of there were the first ideas that contact tracing apps would be useful or could be useful uh to help um ease the lockdown particularly if you combine it with a large testing program you could automate and and and make the um the contact tracing programs are more effective and if these programs are effective enough you could actually end the lockdown or at least loosen the lockdown obviously if you have a vaccine that's even better but that's sort of still in the future the problem was that that app-based contact tracing it was actually a totally new idea um it has never been used before at this type of scale it wasn't has been used a little bit on smaller scale but sort of on this really population-wide level had never been used before and it was clear from the get-go that the app take-up was very crucial so and i think we've seen one of the many graphs that ellen show was actually a graph that exactly came from this variety wyman an out paper from the Fraser Group in Oxford that showed that if lots of people use the app then it's really useful but if if nobody used the app then obviously it's not very useful and in in March nobody knew how how much people would actually download this whether people would like this type of idea or or not it's very it's relatively straightforward it's not easy as the UK has just found out it's not easy to program such an app but the harder part is then to convince 60 million people to spend one minute each to download download this app and to decide to download and to notice it at all so when we started our surveys we ran the surveys in at the end of March um there was no public debate about that and there was really no idea about what people would think about it it's it's potentially it's a it's a tremendous um privacy or threat to privacy um run by the government a government surveillance app on your phone something like that has never happened before in at least in western countries so it was really unclear what what people would think about uh what people would think about this idea so our target group basically and someone mentioned the target group our target group initially was really political and public health decision makers they'll we try to inform not so much the academic debate and not so much the sort of the public debate but really sort of the technical debate within the health ministries but then obviously because it's such an important topic and nobody everybody sitting at home and wants to know about covid we realized that this was spillover into sort of into a broader broader audience that's why we already started as soon as we had all the data we made them available you can find them on this on this website all the reports everything is there and you're happy to reuse them and build nicer graphs for example then um then we uh then we were able to do so the questions in our surveys was mostly about the future willingness to install such an app would you install such an app but the problem was that this was at a point in time when nobody had ever heard of this type of app so um it could only give a very rough indication of acceptability because um we could describe it in only in in in one way but really our sort of our aim was to see whether there's any chance at all I don't think we aim to get exactly the right number of downloads right sort of to really sort of predict what was going to happen but try to understand what people like uh or not like about the app think about the motives so that's I think that's the advantage of these type of of surveys or early on that you get a rough but a very early indication of of acceptability before any of this has been sort of programmed or or released the disadvantage obviously is if you do these surveys in March and now we look at this in June um the things will change over time uh the the stuff that we would measure if we ran the same survey today we would see different things probably we would see different things but also that ultimately decision to install is very different from what we could ask about which is something like the general willingness to install um but in the end you want to know what how many people will actually have it on their on their phone so that's something we we couldn't ask back then and you will I'll show you some extra data later on so comparing our survey data to the actual download data so this is what we find we run these first surveys in five country in the uk germany italy france in the us and um so you see on the y-axis this this is basically stacked histograms the the share of people uh let's say they would definitely install or probably install in green or then in red probably not install or definitely not install you see across these five countries it's actually pretty similar that about 70 to 80 60 to 80 percent uh say they would install such an app that's quite encouraging um in particular if you interpret this general willingness to install the app as an upper bound for the actual installation in in the end because there's quite a lot of things that even if you're willing to install they come sort of between the app and and so between your willingness to install and actually your the the app showing up on your on your phone we also find that people that trust the um trust the government less are less willing to install the app or they say they're less willing to install the app so on the left hand side so we had a question about how much do you trust government from uh fully agree to so do you trust the go sentenced I trust the government to do what is right most of the time people could fully agree to fully disagree to that we see that the people on the left hand side who say they fully agree with that are much more likely to say they would also install the app compared to the ones that fully disagree but you might remember from the last page that um we have us data in here and you might say well trust in government uh in the u.s is quite a different beast than say in Germany but we find the same pattern of behavior across all five countries obviously the level of trust is different um and also the UK and obviously the trust in government is also changing over time um but at that point in time the same we see the same pattern in all five countries so it looks very very similar this graph looks very similar across all five countries we also ask for motives um because this was still in the design phase of the of the app we also wanted to give uh information to to decision makers how to how they could design the app um so we ask for reasons for installing the app and against installing the app and people could click from a list of potential reasons they could click or they could also nominate other reasons if they wanted to and we see that first of all many more positive reasons are chosen and I don't show you that graph what I show you is the graph uh for the main reasons against installing the app and we see that the main reasons against installing are really to do with trust a bit like in the in the previous page the concerns about government surveillance after the epidemic um people 42 percent of our respondents say that would be that's one of their reasons against installing the app this is often people who will say they would install the app but it's still a reason against and that might be overwritten by other things but it's still a concern people have if you are worried about their phone getting hacked people might feel more anxious um uh that was actually quite surprising for me because I would I thought that um uh the app could give you a piece of mind if I know I haven't been in contact with someone and I can I can check every day um that that would be quite good but it seems that many people are actually quite anxious about or say they would be more anxious about this um in one of the servers in the US we also ask um we offer as one of the reasons um I believe that not enough people will download and in when we offer that but also people also say that that's an important reason if they think this doesn't work and they don't bother um installing the app or they might that might be a reason against it I think sort of I'm in the UK um you might have heard that the UK doesn't have such an app at the moment um you might have heard that the UK is struggling with responding to the pandemic in in general um uh so uh someone on Twitter said the other day because um so the UK had an app almost ready but then they postponed it and they say oh it's not coming first of June but it might come in winter but who knows someone on Twitter said um the government isn't able to organize a piss app in the brewery um so it's uh uh so which I found quite funny but sort of there's sort of a generalized sort of trust issue here coming again sort of trust not in will the government partly will the government um misuse my data down the line but also is the government able to pull this off in a in a in a good way and if if they're not um then uh I think that's bad news it gives sort of our our results would suggest there's bad news for the number of people who download the app now I said all these data I showed you are really from end of March it's really almost historic evidence I'm showing you today so how does that compare to actual behavior so you you surely know that Germany has um introduced an app um the Corona Van app about a week ago so these are the extra download data from uh from the Robert Koch Institute which I plotted as the installations as a percent of the eligible population so in Germany you'll have to be 17 or at least that says it has to be you have to be over 17 to download the app and you have to have a smartphone that has to have an iOS or uh Android um operating system and it has to be new enough so this is dividing the number of downloads by the number of people who could download it at all and that is actually also most comparable to the data I showed you before because we only surveyed people who have a smartphone and who are older than 18 so that's sort of the closest comparison and you see that in Germany over the last nine days almost 30% of the eligible of the eligible population have downloaded the app um we said in our survey sort of this very rough estimate that's about so going back to Germany is sort of um uh roughly 70% um so now you well there's only after nine days how can we predict the future really difficult we can look at Singapore because Singapore um introduced a similar app already in March so we have longer data for the for the download statistics for Singapore so this is the same data now sort of just on a longer scale because Singapore um has been out for longer so blue here is Germany and orange is Singapore but on the xx is still dates and since launch so sort of the the orange curve starts in March while the the blue curve starts in starts in June but you see where we have overlap it's it's almost bang on it's like the same people are downloading uh these apps so it's very very similar um now that this graph sort of waves its head really strongly at you to say that obviously what happened in Singapore will happen in Germany and I will be a little bit more careful about this um but you see that in in Singapore downloads have gone up it's really funny here that you would expect that there's plateaus all the people who are really into this all the people that read the financial times every day or really informed they downloaded early on and then sort of it sort of peat us out and you would think oh it doesn't go about 40 percent something happened in Singapore there's something that happened we think is really difficult to establish causality is that they uh loosened the lockdown and had a new wave of of cases and so suddenly hey you got went out to meet actually other people so suddenly the app is actually much more useful compared to if you just sit at home you don't don't meet anybody anyway and on top people started dying again and so people dying is really a good motivator it's obviously a very sad event um but to motivate people to do something uh that's really useful so um I'm not hoping that in Germany we have a second wave of of deaths but sort of you see that perhaps there's a chance that Germany over a long period of time um can get um many downloads but I think the best guess is more that we sort of end up between sort of a range of 30 to 40 percent just looking at these these graphs from Singapore. I want to talk a little bit about the um sort of about the tools and the methods and not just about COVID so in particular when you compare this if you charted this graph for France and Italy that have also released apps a little bit later than Germany they also have released apps the installation numbers are much much smaller so in Italy and France about two to three million people have downloaded the app so this is much lower Germany has about 13 million Germany's also a little bit bigger but it's perhaps a quarter of these these numbers. When in our study we found roughly similar amount of sort of similar values for Italy and France and and Germany so so how can we explain that and now this is really sort of speculation um first of all did we measure willingness to install correctly was something in our survey wrong or is something wrong with surveys in general perhaps people just told us what they thought we wanted to hear um so say a social desirability bias that uh our respondents didn't think about this at all they just told us what they thought we wanted to hear and perhaps they thought we want to hear apps are great and and and so they told us that even though they didn't think that's that that wasn't actually their true opinion. Perhaps our explanation of the app was different from um what people actually saw our um our explanation was neutral but we also highlighted the positive things um I think the the discussion since then in particular in France has been quite negative as focused mainly on on privacy not so much on what can it uh what can we sort of what's the positive side of this app rather about what could go wrong and that's rightly so because it's a new um uh it's a it's a new technology with lots of risks um but it's also with upsides so I think perhaps our explanation was more positive than the than the information that people had over time between March and and um and now but perhaps also we measured everything correctly it's just that the willingness to install has changed so surveys that have been run very similar service to ours that have been run in Germany for example find newer service over time find actually that the willingness to install goes down ever so slightly over this period of time perhaps because the epidemic is receding similarly in in Italy in end of March we were actually close to Armageddon in northern Italy the hospitals were sending people away and to die at home was a really really very tough situation and that situation has been has has been luckily has has improved very much but that obviously also reduced the urgency to to do anything about it and then obviously and I mentioned this already before there's a clear difference between what we can measure even in the perfect world what we can measure in these type of services is the willingness to install and there's will always be difference between that and the actual installation I think the biggest thing the biggest difference is the attention in our survey we chucked all this information into people's face and they're there and they basically have to make a decision I mean they can drop out but it's there and they have to they are aware that this app exists and they have to press a button and there you go and then they press a button well in real life you might see this on tv you might not you might never have heard about this you might not have a tv so it might be that you're not aware of this app and even if you're aware there's also 300 000 things that you also have to do today you have to do the dishes you have to do this you have your kids all kinds of reasons that will take your attention away from this so even if we asked you again if this popped up on your phone you wouldn't say yes I'm happy to install this app but you that might not be enough for you to go out to find the link and to do that and obviously it's costly to do that takes a minute or five or ten if you're older perhaps you don't even know how to do this you don't for me it's easy to say oh yeah you go to the google play shop shop and then you search for it and then you install it my parents might have a hard time with that um they might procrastinate and finally also I'm in my normal life I'm a I'm a behavior economist and I see that the just the general concept of the app is actually pretty complicated I think everybody can understand that if you stay at home during the lockdown if you stay at home you don't meet anybody it's really hard to get infected I think there's a really easy proposition to do and if the prime minister says they stay at home because if you stay home then nobody can infect you and you don't die that's something that is really very straightforward similar with face mask if I wear face mask I can't cuff it other people it's harder to infect others that's relatively straightforward to understand with the app is much much harder if I install the app nothing good is happening to me the only thing that can happen to me to happen is that if I already got infected I will still infect my first degree contacts but if I have the app and my first degree contacts have the app then they can go into quarantine early enough and I can perhaps protect my second degree contacts you see that's really complicated nobody of you has already heard of second degree contacts and so it's it's much much harder to understand there's lots of trust involved again um and so that's much much much much much harder as a oh I fully understand this and I'm I'm happy to do this honestly we are running a little bit out of time just yes yeah so that's the last that's the that's the last slide I said I just want to say that the so in these attitudinal surveys um I think they're very useful tool to get this early indication just what like we use them but you shouldn't expect that you will be able to fully predict future behavior um and that's what wasn't our intention um I think the data and motives should be more generalizable so I lied to you there's not the last slide this is the last slide so in your spare time do download the app they're available in almost all countries except in the UK and in the US um but there you go thank you very much thank you so much Johannes for your very interesting talk and your insights into the pitfalls of different data and we already have a question right on on the topic um Georg is asking um whether the install number or also um covers reinstallations or deinstallations and I would like to add um is it installations or is it really activation of the yeah because for the for the German app you have to um clicks through some some slides and say okay I accept um that my data will be used and etc uh no very good question so the the data I showed you is is the download data so this is from the robot conference web page um so this download this doesn't this doesn't doesn't correct for deinstallation doesn't correct for anything the data on from Singapore is from the website traced together and one of my worries is that this sort of increase here means something about the data quality which I can't track so I just say this is the this is the number on the traced together website they this is the number they give me but what it means about um working app installations um in in real life are to say and even it's even worse than that because even if you have it on your app on your phone and you click yes to everything where you have to click yes to you have to have the phone with you your battery has to be running and all kinds of other things have to happen so it's uh uh this is um yeah so there's quite a lot of things I have to go right thank you um another question um you mentioned that you were doing the survey especially from ministries of health and and uh politicians did you get a lot of feedback did people in office react to you um um well many countries installed these apps so I think that's I take that as obviously totally driven by our survey uh no so this is sort of partly um so this this ferretti at al study here um so full disclosure my wife is one of the co-authors on this that's how I got into this um she's the stake in our family I'm just a side salad uh doing the social sciences uh side of this they were working with NHS x the the department that developed the the app in Britain so in that sense we had them in in that respect um we were in contact with pet quite a lot when was you might remember um germany had a centralized app before they moved to the decentralized a system so we were involved with them quite a lot early on we were also involved with the the so we yeah so in that sense in the sense yes these data were taken up um yes we got some feedback but then on the other hand we were not the main main game in town I don't think they I think the data that I showed you is relatively self-explanatory um so um I um we were actually quite happy um about them about the impact in that sense so you see I'm thinking about academia we always have to think about impact and so I was quite happy about this there's another question um uh deliverance is asking how can we advocate to get the app in the US yeah good luck with that um so um so frankly now after three months I think both in the US and in the UK I think having a an app that is based on voluntary downloads I think is a very very difficult project now um because so many things have happened even in the best of cases and I think in Germany if you follow the public debate there was basically everybody saying this is an amazing thing and we totally need this is basically I've only found one politician who said she wouldn't want to have this but it's across the spectrum everybody said everybody said this is amazing it's the bonus leaguer whatever um while in the UK the debate has been rightly so actually quite uh quite quite negative about privacy in the US obviously all kinds of weird stuff is happening um so I think with this type of background um I think it's very hard to get high numbers of um of voluntary downloads I think my only hope for the UK and the US is the second phase of the Apple and Google uh installation I'm not entirely sure um whether this is coming but when they launched their their API they said in seconds that we'll just do the contact tracing for you it will just happen on your phone and then the the app the NHS app or the US whatever or highway just has to do has to be downloaded once you're infected you don't have to have an app penetration of 60 in the population you can download it as soon as you get infected so that's a totally different decision and I think that's the best guess for for that but we also see how difficult it is due to contact tracing you see it in Singapore you see it in sort of countries that were actually very very successful it's really really really difficult to contain a local outbreak you see it in Guter slow one of my favorite cities um is really difficult and this process has to be super super efficient and it's really hard to do it without an app and we we're seeing this and I think we will see this more over the next couple of months great thank you Hannes thank you very much for um presenting our research and showing the importance of sort of connecting these different datasets I was hoping we have enough time for Ellen and you to discuss how these graphs can be made prettier because now of course we all have conscious or not having beautiful ft graphs but we do have a graph here on the slide and I want to use the the closing sessions here for a moment to invite everybody to take the annotation feature of zoom which is either on the top row for you on the bottom in the menu and it's it's annotated and then you can pick the the use your mouse to mark the country you are from and so I never tried this let's see if this works with 60 people 70 people still on the call and I'll give you about two minutes and then we would like to close this out with a group picture before I let everybody go so but before we do that a big round of applause and everybody since no one is speaking feel free to like turn your videos off already sorry on already so that we can see you and see your class either with the yellow hands or in real time next time you Hannes we will get prepared to do a little voting on the fly survey and hearing who installed the app but I didn't think of this ahead of time so all right love to see I'm I'm still missing our Chilean participants and the Brazilians I hope you can figure out how to annotate I've seen you in the participant list so hope to see you soon on the screen here and and I do want to thank everybody who participated and because we had yesterday women in data science I do want to say that in our full blown connect program we had a lot of female speakers as well and workshop leaders and whatnot it just so happened that as we pared this down we ended up with two males which didn't you know I'm locked these two talks I'm happy we have them but I feel a little self-conscious on the imbalance that this created so while this is not an excuse at least be aware that we are aware all right well I love the signals coming in thank you everybody and I do also want to say that in November we will have mid-November sort of the second half of this event hopefully live then in manheim and we hope that you can find your way you are interested in coming admittedly November is not the best time to visit Germany weather-wise but flights will be cheap so you know it's always a trade-off with the data we have all right Annika I would love to hand over to you I know that you do want to guide people with the map but also with the picture taking so I'm gonna mute myself now and hand it over to you thank you everybody yeah before going into the break again we just wanted to take a group picture so maybe everybody could turn on the camera now and maybe Falki you can unshare your screen so that yeah perfect so I think you won't fit into one picture and I can say because we heard that data ethics and privacy is so important in both of these talks that not post this with everybody's name on them right so that just for this is also a privacy sensitive but in new york having you all in manheim at the group picture we at least thought we do this here and I guess Annika you go through three rounds of this with the three panels we have so yeah and while Annika is doing that I do want to remind all the present students and those that are incoming that at 6 p.m. after the break there will be a Q&A session with Karin and Kai and Olga to answer all the questions you might have with respect to the program and going forward and finishing up and changing and I do want to remind everybody that we have another info session on June 30th for those that are now in ties to hear a little bit more so I think we're done with the all right then I want to take one more big round of applause to all the speakers and panelists thank you for joining us today and I look forward to seeing all of you in person or virtually at one of the upcoming talks or in one of the courses many of the courses can be taken at standalone and Ellen I think we're all ready to take an internship at the FT including me so all right thank you all and have a lovely weekend
founder of the International Program in Survey and Data Science at the University of Mannheim in thanks to the University of Mannheim and the participants in the IPSDS and then an incoming is becoming more and more an integral part of the international program of survey and data science information is presented visually which on data sources from data sets from various way to make sure that nobody complains is to make intersection of industry and academia I'm also visualization and journalism in the age of the coronavirus we were seeing a lot of news about the evolution of data science compared to in will the government partly will the government heard that data ethics and privacy is so