Why Data Scientists need to learn about Design Thinking?… Why NOT?
Data Scientist as profession actually is not entirely new. But the job title now has been vastly growing as one of the most sought career in the world of digital and Big Data. Even so, it’s been said (or rumored) that working as Data Scientist is really paid off! I am not talking about monetary here, but more to jobs satisfcation :).
Well, let’s focus again to why a Data Scientist needs to learn Design Thinking? We can start from how we get into the shoes of Data Scientist. In general, they manage end to end data processing from collecting, cleaning to sorting data, then to be analyzed further into presentable information for any purposes like decision making, business presentation, customers aggreation etc.
Then we know a Data Scientist must present their analytical result to end user, which can be their bossses, policy maker, board of directors or else. At this point we come up with what needs that the end users wanted to fill in? Do they need to make decision? To seek any correlation of events? To find alternative solutions or what? It depends.
That’s why when Data Science Indonesia, the biggest association of Data Scientist, Market Researcher, and Data Analysts in Indonesia, invited me to deliver one course series on Design Thinking, they really know what the deliverables for their 1st Data Science Camp in April to August 2016. They wanted the participants (consists of diverse background, experience and industry) to really understand what the customer’s needs in delivering their analytical result. I found it very interesting and I gave my credit to them for being open to learn more, beyond data and application.
So I delivered the full Design Thinking method series using the module in Stanford’s d.school: Empathy – Define – Ideate – Prototype – Test. I held two classes, and 1st class was heavily composed on how we should be understanding and comprehending customers needs as part of Empathy. A Data Scientist should be empathetic to their users’ needs and wants. It’s not easy but we can learn if only we open ourselves to learn more and more. The 2nd part, I taught about how to generate ideas and get into prototyping to visualize their best ideas to tackle the user’s definitive problem.