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Analytics vs. Human Decision-making Revisited

February 24, 2014

Most managers understand the value of data-driven decisionmaking that scholars such as Erik Brynjolfsson at MIT Sloan and others have advocated for several years. We know that entertainment businesses like Caesar’s, logistics firm UPS, and even Gallo Wine use big data and analytics for competitive advantage. It also keeps them ahead of those using old-style, gut instinct and relying on the highest paid person’s opinion (HIPPO). By now, Moneyball is a classic example of analyzing data to win the game, and even political campaigns are adopting these techniques for predicting results and planning strategies, as described here.

Yet these managers, like me, also have a nagging feeling that human decisionmaking has its strengths and its value, too.

Now, Phil Rosenzweig, a professor of strategy and international business at the International Institute for Management Development (IMD), in Lausanne, Switzerland, has articulated some of the flaws to the all-analytics approach. He writes that there are times when executives must do their job and lead — taking action, using decisive judgment and exuding self-confidence to yield immediate results. Rosenzweig offers these insights in a new McKinsey Quarterly article, The Benefits—and Limits—of Decision Models, and in his new book, Left Brain, Right Stuff: How Leaders Make Winning Decisions (Public Affairs, January 2014).

Rosenzweig doesn’t dispute the value of unbiased data to support some decisions– in fact he calls some recent applications of analytics “truly dazzling,” and he writes:


Combining vast amounts of data and increasingly sophisticated algorithms, modeling has opened up new pathways for improving corporate performance. Models can be immensely useful, often making very accurate predictions or guiding knotty optimization choices and, in the process, can help companies to avoid some of the common biases that at times undermine leaders’ judgments.

At the same time, he does not see them replacing strong management in every case.

For things that executives cannot directly influence, accurate judgments are paramount and the new modeling tools can be valuable. However, when a senior manager can have a direct influence over the outcome of a decision, the challenge is quite different. In this case, the task isn’t to predict what will happen but to make it happen. Here, positive thinking—indeed, a healthy dose of management confidence—can make the difference between success and failure… In our embrace of decision models, we sometimes forget that so much of life is about getting things done, not predicting things we cannot control.

The takeaway for me is that making good decisions isn’t an either/or process where you can analyze the data or go by gut instinct. Rosenzweig correctly states that both analytics and the players contributed to the Oakland A’s winning season. And that blended combination of skills is often the winning answer for business executives as well.

What are your thoughts and experiences? Share them here.