The Right Way to Make Data-Driven Decisions

This HBR IdeaCast episode interviews Professors Michael Luca and Amy Edmondson about their research on data-driven decision-making. The discussion explores common pitfalls in using data, such as misinterpreting correlation as causation and misjudging the generalizability of findings. The professors advocate for a more nuanced approach, emphasizing the need for thoughtful conversations and critical analysis of data, including understanding sample size and confidence intervals. They suggest a framework for interrogating data from both internal and external sources to improve decision-making. The ultimate goal is to foster a culture of learning and iterative decision-making, rather than relying on data as an infallible source of truth.