Why so much talk about Decision Science?
When I first came across this term and tried to learn about it, I was overwhelmed by the information. Everyone has picked it up and have written pages on end about it. There was one thing in common though.

We all have read many articles, blogs and seen posts of ‘decision science vs data science’. We haven’t actually tried to think why do we compare the two. There is more congruence between the two. I would say that the only main difference is that data science delivers the results and decision science helps us take calculative steps based on the results.

What if I asked you to compare the sports, chess and boxing. Many would argue chess as an activity rather than a sport but that is a discussion for some other time. Both require attention and patience and practice and pattern recognition in the opponent. Yet, they are quite different and does not require to set up a comparison between the two. The missing piece that we failed to notice was that they did not affect each other. Whereas, decision science depends a lot on data science.

Imagine this, you are a data scientist dumped with a huge database containing all sorts of data. Your superior asks you to comb through the data and provide him with your findings. We have all been there where based on certain results we can easily decide what to do. That is where the decision scientist comes in. It is not as easy as it sounds.

A decision scientist doesn’t just look at the data provided. There are factors like ‘past-experiences, variety of cognitive biases, individual differences, commitment and some more’. Data Science is a tool by which correct decisions can be made. Read more here.

Whoever said that data science is for the math majors or the computer genius in the class. Basic analysis is required in every field. You do not have to be Alan Turing to go about it. Psychology students learn distributions in their bachelors, biology majors have to understand the normal curve too. In short if data science is everywhere, so is decision science and has been so always.

Photo by Frank Vessia on Unsplash

Since we are just venturing into this field, decision science can quite easily get misunderstood for analytical fields or areas using machine learning, but it is very much a separate area.

“While data science is perhaps the most broadly used term, ‘decision science’ seems like the more fitting description of how machines are assisting business leaders in solving problems that have traditionally relied on human judgment, intuition and experience,” according to K.V. Rao, founder and CEO of sales forecasting software company Aviso, in TechCrunch. “It may not be the sexiest phrase in the world — I’ve never seen it in any marketing materials — but ‘decision science’ aptly encapsulates how computers are helping to systematically identify risks and rewards pertinent to making a business decision.”

As I have mentioned above, according to Data Science Central, “Data Scientist is a specialist involved in finding insights from data after this data has been collected, processed, and structured by data engineer. Decision scientist considers data as a tool to make decisions and solve business problems.”

Photo by Emily Morter on Unsplash

By now, we’re accustomed to the what a great deal being a Data Scientist is. The demand for Data Scientists is only skyrocketing and will be go on maybe till aliens take over. The overlooked champion of the business and technology world have just been recognized. Little do we know about them.

If people were rational, behavioral economics would be a myth. From Economics to Marketing to Psychology, all require a Data Scientist and a Decision Scientist. Intellectuals believe that the combination of two can take the business to great heights.

A data scientist just has their data in hand. They have no knowledge of client needs or promises made by the company or whether the management has some reservations. In short, they do not have a 360-degree view of the problem at hand. Regardless decision scientists have an analytical mind which helps them picture the problem and find the best solution for the company and client/customer. Hence, they are prepared for all kinds of situations.

Being rarer than data scientists, the demand for decision scientists are only to increase. The craze for data science had just started when educational institutions started providing courses. The trend is still increasing. Everyone is gearing up with the latest tools. We do not want to be left behind. Universities have already started courses for students to become decision scientists. It’s a matter of time before schools adopt some concepts.

I have made a list of as many universities as I could find which were offering the course in major countries. Now, most of these are PhD courses as decision science requires a some taste of how things run in the real world. It is a great opportunity as research is still underway.

ASIA:

IIM Bangalore, India

IIM Lucknow, India

INSEAD, Singapore(in all campuses)

CUHK Business School, China

Asia and Pacific decision science institute

EUROPE:

European decision sciences institute

University of Konstanz, Germany

IESE Business School, Spain

Rwth-aachen University, Germany

HEC, Paris

AMERICA:

Decision Science institute

The College of Penn

Carnegie Mellon University

Harvard University

Kellogg School of Management

Thank you for reading!

Do tell me your remarks.
['Ada Johnson']
['Technology', 'Data Science', 'Decision Science']