MIT Sloan Management Review Article on Framing Data Science Problems the Right Way From the Start

The failure rate of data science initiatives — often estimated at over 80% — is way too high. We have spent years researching the reasons contributing to companies’ low success rates and have identified one underappreciated issue: Too often, teams skip right to analyzing the data before agreeing on the problem to be solved. This lack of initial understanding guarantees that many projects are doomed to fail from the very beginning.

Of course, this issue is not a new one. Albert Einstein is often quoted as having said, “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute solving it.”

About the Author

Roger W. Hoerl (@rogerhoerl) teaches statistics at Union College in Schenectady, New York. Previously, he led the applied statistics lab at GE Global Research. Diego Kuonen (@diegokuonen) is head of Bern, Switzerland-based Statoo Consulting and a professor of data science at the Geneva School of Economics and Management at the University of Geneva. Thomas C. Redman (@thedatadoc1) is president of New Jersey-based consultancy Data Quality Solutions and coauthor of The Real Work of Data Science: Turning Data Into Information, Better Decisions, and Stronger Organizations (Wiley, 2019).

Learn more about MIT SMR.