97 Things About Ethics Everyone in Data Science Should Know

Chapter 82. Ethics and Reflection at the Core of Successful Data Science

Mike McGuirk

Faculty Member, Babson College

I often think back to some very sound advice I received from my mentor early in my analytics career, when I did not yet have a significant client-facing role. His advice to me as I worked on analytic projects was that I make sure I could always explain, justify, and defend each and every decision and recommendation I made as I progressed through the analysis. I should put myself in the shoes of the client and fully anticipate and understand their needs, and then exceed their expectations. That left a lasting impression on me and conditioned me to always be thoughtful and thorough across all stages of the analytics process: the analysis design, the use of consumer data, the recommended insight-driven business actions, and the measures of success. That approach worked extremely well in a business-centric operating model.

Fast-forward to today’s business environment, where customer-centric operating principles rule the day, and it becomes clear that business-centric analytic and data science processes are no longer sufficient. Companies have become obsessed with using consumer data to find a competitive advantage. In fact, Forrester Research explains in its Predictions 2020: Customer Insights report that 56% of the businesses surveyed will be launching initiatives ...