Hi everyone!
This is a quick crash course video where we’ll
talk about business, data science, and how
the two go hand-in-hand.
Welcome!
So…
"Business basics for data scientists?
What do I need that for?"
Let me explain.
Imagine the following:
You’re a data scientist.
You thrive on maths and statistics, you’re
confident in using SQL and Python, and have
some experience in data cleaning and visualization.
Plus, you’re no stranger to machine and
deep learning, which, in your opinion, makes
you the perfect candidate for any high-paying
data scientist job.
Maybe you’re a seasoned data scientist trying
to break new ground.
Or you’re a novice who just completed an
online certificate course to land an internship
at a prestigious data science consultancy.
Either way, you go to the interview, feeling
like a winner.
You boast about all of your skills, explaining
how you know 19 programming languages and
want to use them all and how you can apply
the latest MFCC algorithm, with the enthusiasm
of a girl scout determined to sell all her
boxes of Samoas cookies right then and there.
Judging by the impressed look on the interviewer’s
face - you got the job!
But, in reality, here’s what the employer
is thinking: “Awesome, but I don’t have
a job for another run-of -the mill data scientist.
I need somebody who understands that data
is business, who knows how to solve complex
data problems, and share their insights with
the management.”
And that’s exactly why you should watch
this video.
We’re giving you 5 key business basics that
will show you how to work with data to reach
practical business solutions.
Because today, dealing well with data is table
stakes for any company to stay in the game.
It means innovation, productivity growth,
and richer customer insight.
And helping a company ensure these will make
you successful as a data scientist.
So, here they are!
Starting with Number 1: Understanding business
objectives.
Data scientists must understand the strategic
goals of the company and use them as guidance
for the whole data collection and interpretation
process.
This guarantees that the analytics you provide
will ensure the competitive edge of your company.
Nota bene - always keep in mind your audience.
Is the data information for internal use by
the board of directors or the sales managers?
Or is it for external use by capital markets
or suppliers?
Each audience has different needs, even if
the overall strategic objective is the same.
Once you’ve identified your audience, make
sure you provide the answers to their performance-related
problems.
Ok.
But how do I do that?
Well, make yourself familiar with the concepts
of Key Performance Questions (KPQs) and Key
Analytics Questions (KAQs).
Both allow you to contextualize performance
data and derive actionable knowledge form
it.
KPQs revolve around how well your company
is performing in achieving certain goals.
For example: “How well are we promoting
our services?”.
Or “To what extent are we attracting new
profitable customers?”.
KAQs, on the other hand, aim to narrow down
the strategic choices for achieving a goal.
For instance, “How do customers click through
our website?” or “Who are going to be
our most profitable customers?”
“What about business intelligence tools
and other IT systems?” the nay-sayers might
ask.
Unfortunately, in most companies, BI tools
are driven more by the information on hand,
than by the information that will actually
lead to the best business decisions.
This could put any company at a major disadvantage.
That’s why it’s important to discover
what knowledge the recipient needs first and
use the tools accordingly, as opposed to applying
the tools and then deciding on the information
needs they could possibly fulfill.
Alright!
Moving on to Number 2: Collecting the right
data.
A senior data scientist must ensure the team
under their lead collects and organizes relevant
and useful data.
So, it’s crucial to know if the necessary
data is already stored in the organization
and in what formats– numerical or non-numerical,
such as images, text, or sound.
That will help you establish the company’s
methodologies for collecting additional data
- quantitative for numerical data or qualitative
for non-numerical data.
Quantitative data is collected automatically
from operations, or via surveys and questionnaires.
It’s easy to analyze and represent visually.
However, to provide more richness and context,
a company can’t do without qualitative data.
Its analysis uncovers the factors influencing
certain behavior, like customer satisfaction
or customer churn.
Qualitative surveys, focus groups, and peer-to-peer
evaluation are some of the methods for collecting
qualitative data.
Other ways include analysis of click-through
rate and engagement in social media
So, you have the data.
Now it’s time to interpret and contextualize
it to extract valuable information.
Number 3: Analyzing the data.
Meaningful analysis is crucial for effective
decision-making.
As we already mentioned, BI tools are not
sufficient for a great analysis per se.
Still, they can play an important role in
various types of other analyses.
For example, Online Analytical processing,
a.k.a. OLAP, which provides numerous dimensions
to look at data.
Or data mining which correlates various factors.
And, of course, text mining, used to extract,
analyze, and summarize information from large
text datasets.
BI software also provides data scientists
with interactive drill-down and rich graphic
capabilities, and the ability to perform root-cause
analysis.
What if you need to view data from different
perspectives?
Then multidimensional technology comes into
play.
Using data models, it helps to make decisions
based on consolidated summarized business
information from various sources.
Basically, we can say that all is fair in
love, war, and data analysis.
So, don't shy away from taking advantage all
tools available, as long as you use them smart
to reach relevant and actionable insights.
Number 4: Communicating the data effectively.
To prepare a clear and compelling presentation
of your insights, you need to use different
types of charts and graphs, such as tally
charts, histograms, scatter plots, etc.
However, for truly informative and engaging
data storytelling, use graphs and narrative
together.
This will help your audience see the big picture
and derive business value from the collected
data.
How to make sure that the valuable insights
won’t be overlooked?
Bernard Marr, a renowned strategic performance
consultant”, suggests 4-steps to powerful
and strategically relevant reports:
• Frame the report with KAQs and KPQs;
• Support the KAQs and KPQs with suitable
and informative graphs and charts;
• Use headings to capture the key insights;
• and narratives to provide context for
the visuals.
If you opt for a dashboard representation,
be mindful of some common design mistakes,
such as supplying inadequate context for the
data, cluttering the display with useless
decorations, or arranging the data poorly.
You believe a data scientist’s job ends
with packaging the information and presenting
it to stakeholders?
Think again.
Truth is, if you’re serious about your career,
you should be aware of Number 5.
Understanding how evidence-based decisions
are made.
The best data scientists make sure that their
insights will become the basis for actionable
steps.
As a data scientist, you can have a strong
impact on the company’s desire to learn
and improve.
And, sometimes, it will be up to you to inspire
accommodation of analytical capabilities throughout
the organization.
Or initiate implementing an appropriate IT
infrastructure.
So, embrace your data science power and use
it for good!
That wraps up our list of 5 business basics
that will help you on your data science career
path.
You can use them as a stepping stone to build
up on your business know-how or expand your
knowledge with some relevant books.
Or take an online business course to improve
your skill set and make your resume stand
out.
Well, if you happen to be watching this video
while waiting to be called in your next interview,
don’t despair.
You still have a few minutes to repeat the
basics 5 times as a data science mantra, take
a deep breath, exhale, and enter with the
unshakeable confidence of a master data scientist
who truly knows their stuff.
Good luck!
