I want to explore a new term that I heard at meetup hosted by Inspire11 called “COVID-19 PT. 8 — Data Science in the Midst of a Pandemic”. While discussing what’s needed in communication with data scientists and business partners, a director named Steph Kopa mentioned a term “Decision Science” while we were in a break out room together. It’s not a term I’ve heard before, not coming from a business background. I want to explore what that means.

According to this post by Chris Dowsett, decision science is the skill of using data as a tool to make a decision. He goes on to explain that the role of a data scientist, at least at Instagram, is to evaluate independent projects or features. The decision scientist takes those individuals analyses and compiles them, incorporating what they know of business and of the current environment in which those projects would be rolled out to develop a model that will assist in making a decisions for the company.

My education at Flatiron School focused on incorporating a business case for each project assigned. Sometimes this business case would make developing features for modeling easier, but sometimes when revisiting the business case after a model was developed, I was left with a gap between what my model could do and the original business case. This experience added some color to my conversation with Steph. My background in science allows for the exploration of ideas simply for the sake of exploring a theory. When I mentioned this, Steph shared that when she’s working with her data scientists she often pushes into the question why, asking it until there’s nothing left to answer. I think this might be a good approach to developing the skill of decision science while working as a data scientist. Not to mention, this approach will empower better features and better models by forcing the analyst to think critically about each incorporated component of the model.

In this article, the approach to decision science is mentioned to be different from other analytical approaches. It takes in available information and makes the best choice for the circumstances. This almost reminds me of a project that I did in determining the best product formulation. In this project, first I had to preform statistical analysis for each formulation, exposure, and method of use. Once this testing was done, I laid out a table on paper with my boss that had the results from over 60 statistical tests. From each independent conclusion, we were able to see a trend of which product performed higher with more consistency. The work that I did was in the vein of data science. If my boss and I had extended those same skills to the table that we drew up together, that would probably generate a decision science model.

To conclude, it seems like data science and decision science apply the same skill of analysis but with different frames. Also, it seems like the work that a decision scientist does is an extension of the work generated by a data scientist. It seems like the two roles could work together to generate truly data driven decisions.