You have to know not just whether the data is accurate but whether it’s significant. Companies measure profits because they’re concrete and easy to understand as a metric of success. But the long-term flourishing of a company is affected by things like customer relations, innovation, collaboration, and mentoring – all things that are critical even though they’re harder to generalize and quantify. You need a seasoned executive to assess them, not an algorithm. Another example of a metric fallacy is evaluating a nonprofit by how much it spends on administration as opposed to programs. Low overhead may be an accurate metric, but it isn’t actually a significant one. We assume that a high ratio of overhead-to-program costs indicates fraud or poor management, but most of the time low overhead doesn’t equate to high productivity. Just the opposite: low overhead deprives the nonprofit of the trained staff, functional offices, and program management tools it needs to get the job done. Finally, metrics lose their meaning when they are used to reward or punish performance rather than to diagnose and analyze. If there are rewards and punishments attached to a metric, people can and will game it. That can be a matter of life and death. In New York State, the mortality rates for heart surgery declined after the state began publishing metrics about individual doctors’ success rates – because the doctors became less willing to treat high-risk patients who might lower their performance scores by dying.