Finding What You Wanted to Find (Even When It Wasn't There)

CEO wants to prove the new pricing strategy is working. Asks analytics for a report. Analyst finds data showing revenue is up in two regions—ignores the three regions where it's flat or down. Presents the good news. CEO is thrilled. Strategy continues. Six months later, overall revenue is down 8%. What happened? Confirmation bias. We see what we want to see. We cherry-pick data that supports our hypothesis and ignore data that doesn't. It's human nature. But it's deadly in analytics. You end up making decisions based on a partial, distorted view of reality. It's like a factory manager only checking the machines that are working and ignoring the broken ones. Eventually, production grinds to a halt.