Building a Better Ball Team with Big Data: Lessons for Executives

Loraine Lawson

Executives are becoming a bit skeptical about Big Data’s ability to deliver real business change, it seems. The problem isn’t the skepticism — the problem is, it’s based on misunderstanding.

Don’t believe me? Good. Ask a European soccer coach or, if they’re in the U.S., Oakland Athletics. Or, if you prefer, ask Michael Schrage, who first noted the problem during a Big Data seminar held in London for senior executives.

Schrage is an author and research fellow at MIT’s Sloan School’s Center for Digital Business. He asked the room full of executives, “How much more profitable would your business be if you had, for free, access to 100 times more data about your customers?”

The response wasn’t what he expected.

“… not a single executive in this IT-savvy crowd would hazard a guess,” he writes in a recent Harvard Business Review blog post. “One of the CEOs actually declared that the surge of new data might even lead to losses because his firm's management and business processes couldn't cost-effectively manage it.”

Schrage contends this attitude is based on a misconception about becoming data-driven, but actually, it will require more human decisions.

“The reason why my London executives evinced little enthusiasm for 100X more customer data was that they couldn't envision or align it with a desirable business outcome,” he writes. “Would offering 1000X or 10,000X more data been more persuasive? Hardly. Neither the quantity nor quality of data was the issue. What matters is how — and why — vastly more data leads to vastly greater value creation. Designing and determining those links is the province of top management.”

Specifically, executive leaders need to decide what matters most and make a commitment to the desired outcome — then decide how Big Data can help you get there, he says.

This advice differs from what I’ve heard previously, which is that companies should approach Big Data as more of an exploration to see what the data reveals, rather than a query.


As a happy coincidence, European soccer shows both can be true.

European soccer teams are super-aggressive about data. They don’t just monitor the usual stats, they actually wire up players at practice to collect data in all biochemical data, GPS data and vital signs, reports InformationWeek. Coaches can actually create player-specific drinks and nutritional supplements for each player, based on blood and saliva tests.

Here’s the catch: Most of it? Meaningless.

It turns out there’s no correlation between how much distance a player covers on field during the game and outcome, the article notes. Likewise, traditional stats — number of tackles, shots on goal or other popular game trivia — are useless. Those measures don’t impact the actual outcome of the game.

But by monitoring all this data, they did find data that correlated with winning, InformationWeek reports:

It took time for team managers and their statistics gurus to understand which of those data points to pay attention to. The percentage of completed passes compared to the number of interceptions is a good indication of consistent victory, as is an error rate lower than 18 percent.

And, in the case of Oaklands Athletics, they used the data to “identify players who would be a unique asset to the team,” and exploit their opponent’s weaknesses, on a small budget.

The moral of that story: They did not know which data they needed, but they knew they wanted the outcome they wanted to create.

The same will be true for businesses. Big Data can create big outcomes, but you’ll have to identify the outcome you want, so you can identify the data that matters to your sport and your particular teams.

Now, it’s true, you shouldn’t expect overnight wins. There’s actually a steep Big Data learning curve, as a recent CompTIA survey showed.

And that’s not to say that a bit of skepticism isn’t deserved — there is a lot of hype now.

“In a new era of Watson, Windows and Web 2.0 technologies, any organization that treats access to 100X more customer data as more a burden than a breakthrough has something wrong with it,” Schrage warns.

So don’t dismiss Big Data as useless, just because it’s being hyped. Instead, Schrage suggests CIOs and executives:

  • Rethink how your organization adds value.
  • Treat Big Data and the algorithms that run them like managing top talent.
  • Ask “What value matters most, and what marriage of data and algorithms get us there?” instead of how do we get more value from more data?


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