In developing a consulting practice around data and analytics, I am increasingly drawn to the nontechnical aspects of how real people can and will use the information predictive models tell us, and how principles of psychology and behavioral economics can be brought to bear in business decision making. James Guszcza, in his recent article from the Deloitte University Press entitled “The last-mile problem: How data science and behavioral science can work together,” argues that the full value of predictive analytics can in many cases be improved by applying complementary techniques from behavioral science. By combining concepts promoted in both Moneyball, by Michael Lewis, and Nudge, by Richard Thaler and Cass Sunstein, Guszcza makes a compelling case for approaching solutions to problems that combine the best of data science and behavioral science. It’s a thought-provoking article I would recommend to anyone interested in learning how to create a context for the effective use of predictive analytics beyond the generation of a probability estimation or numeric score.
You can find his article here, from the freely available Deloitte Review, Issue 16, Deloitte University Press online.
And if you want better understand Moneyball as a predictive analytics story, drop me a line at firstname.lastname@example.org. It’s one of my favorite books (I love both predictive analytics AND baseball) and favorite topics to discuss.