Big Data = Moneyball for Your Company

March 31, 2012
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In every business, people have to analyze information in real time to arrive at the right decisions.

In every business, people have to analyze information in real time to arrive at the right decisions.

The problem is that very few people can correctly solve important business problems in real time. In fact, most people can’t even solve these problems if they have unlimited time to do so, according to John Lucker (@johnlucker), a principal at Deloitte Consulting and James Guszcza, senior manager at the firm.

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So to avoid becoming overwhelmed, decision makers rely on their gut feelings when weighing various factors, the authors say.

But people are pretty biased about the ways they make decisions – biases that can sabotage efforts to unlock the value in all the data their businesses collect. That’s because the human mind just hasn’t evolved enough to allow us to make the kinds of business decisions we have to make every day, according to Lucker and Guszcza.

“The human brain is very bad at juggling decisions,” Lucker told an audience at GigaOM’s recent Structure:Data conference.

And it’s not easy for businesses to overcome some of those biases, according to the author of the GigaOm article. For Lucker, however, companies have to overcome the “human factor” if they want to successfully drive organizational change. The way to do that is by using statistical analytics and predictive models, he says.

Take for example, “Moneyball,” the story of how Billy Beane of the Oakland A’s used analytics to identify undervalued baseball players – a story that has far-reaching implications for many industries. The premise behind “Moneyball” is that the team could do a better job acquiring new players by using a computer-generated analysis of the pertinent data than the scouts could do using their gut-feelings.

“Moneyball” is a early example of “workforce intelligence,” according to Lucker and Guszcza. And analytics can bridge the gap that often exists between workforce-related data sources and the business issues to which they should be applied. For example, they say predictive models are being built so HR managers can make better hiring decisions.

However, it’s just not enough for Billy Beane to have the data to back up his decisions, he also has to win over the Oakland A’s execs to change the way they run the team, according to Lucker. Similarly, Lucker says while you have to have the data, you also have to establish a clear link between your corporate strategy and what you’re doing with big data and analytics. If not, the new processes just won’t work.

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Linda Rosencrance
Spotfire Blogging Team