Big data’s popularity isn’t difficult to understand. Busy executives are confronted with more raw information than ever before and are expected to make sense of it all and put it to use. Without sophisticated data mining techniques and decision analytic models they would be setting themselves up to fail. But Phil Rosenzweig, a strategy and international business professor at the International Institute for Management Development (IMD) writing for McKinsey & Co., worries that some executives may be too reliant on predictive models.

Big Data

Big Data: Players don’t predict, they have to achieve

Big Data was popularized by Moneyball and the success of the Oakland A’s under general manager Billy Beane, but not everyone has been sold on the idea that statistics drive the game of baseball. Two-time MVP Joe Morgan insisted that “players win games, not theories.”

“Proponents of statistical analysis dismissed Joe Morgan as unwilling to accept the truth, but in fact he wasn’t entirely wrong,” writes Rosenzweig. “Models are useful in predicting things we cannot control, but for players—on the field and in the midst of a game—the reality is different. Players don’t predict performance; they have to achieve it.”

Rosenzweig argues that models are at their best when predicting external factors that decision makers have no control over. By showing executives the lay of the land, models help them determine the best course forward. But when models are used to predict something that is within the executive’s control, it threatens to become a mental trap. Models might accurately predict expected performance, but management should be striving to outperform.

Big data: Leaders should try to influence outcomes, not just predict them

“Decision models are often so impressive that it’s easy to be seduced by them and to overlook the need to use them wisely,” writes Rosenzweig. “Before leaders and their teams apply models, they should step back and consider their ability to influence the outcome.”

Sometimes external factors can be influenced, even when they can’t be directly controlled. Rosenzweig gives the example of a bank that chooses to deny someone a mortgage because they don’t save enough money each month. Of course the bank can’t force a person to change his or her spending habits, but a brief meeting explaining why the loan was declined gives that person the opportunity to make different choices in the future, and can create goodwill if handled well. The model tells you that the risk of default is unacceptably high, but individual initiative can still look for opportunities to increase performance.