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Moneyball Doesn’t Lead to Decision-Making Home Runs

Posted by Gary Corrigan

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Jun 11, 2014 8:00:00 AM

As we discussed in our last blog, big data is highly influential and is certainly changing the decision-making process across the business landscape. More organizations are consuming tools such as Hadoop and YARN to get in on the data-crunching fun. According to a survey that IDC conducted, 32% of businesses have already deployed a Hadoop solution. Meanwhile, 31% said they plan to deploy within the next year. However, for those organizations looking to use Hadoop to run their own version of Moneyball, they may be expecting too much.

If you remember, Moneyball was coined by New York Times writer Michael Lewis, and it is the practice that Oakland A’s GM Billie Beane used to outflank his large market baseball competitors. Beane used statistical analysis to forecast player performance based on the statistical factors he was looking for. As depicted in a best-selling book and an Oscar-nominated film, the data-driven strategy not only resonated with baseball teams, but it has also had a profound impact on business leaders seeking a way to gain greater value with limited resources. This is especially true for small and mid-size (SMB) companies.

The challenge with fully adapting the Moneyball formula in enterprise settings is that there are many factors that drive business success. And the measurement of success is not as definable as looking at stat lines or the win/loss column. As Nate Silver points out in his comparison, baseball teams win or lose primarily based on the output of their players. However, CEOs don’t necessarily win or lose in a way that keeps them ahead in the standings. And it’s harder to recognize the contributions that individual employees make toward the successful outcomes; direct statistical attributions are much harder to come by.

Also, businesses go through constant volatility. Ebbs and flows can happen hour to hour. Focused statistical analysis can help CIOs and CFOs make better decisions, especially as it relates to making projections and forecasting outcomes – but it is not an absolute determiner.

In that regard, to use a baseball analogy, businesses should be more concerned with hitting daily singles, doubles, and triples, instead of going for the home run swing every time. They can use statistical analysis and forecasting to consistently maximize their smaller, day-to-day gains. And just like in Beane’s case, it would help if CIOs knew exactly what they were looking for before they started to dive in.

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