Fins Technology: Moneyball and the HR Department
Associated Speakers
Michael Lewis

Fins Technology
By Joseph Walker
April 16, 2012



The human resources department is known for being touchy-feely, but in the age of big data, it’s becoming a bit more cold and analytical. From figuring out what schools to recruit from to what employees should be offered flexible work arrangements, data analytics are helping HR professionals make more informed decisions.

The success of Oscar nominated film Moneyball isn’t hurting either, said James Raybould, director of insights at LinkedIn. The movie, based on the Michael Lewis book, tells the true story of Oakland A’s General Manager Billy Beane. Using statistical analysis, Beane was able to recruit undervalued baseball players and lead his underfunded baseball club to the playoffs.

“We’re seeing a lot of companies actually aspire to that movie,” he said last week during a panel on big data at the Impact 2012: The Business of Talent conference convened by consulting group Bersin & Associates. “How do I make Moneyball for HR?”

Capital One, the credit card company and bank, has automated data reports on employee attrition, headcount and promotions. It is also beginning to analyze the characteristics of its most successful employees, like what schools they went to and what their majors were, said Mark Williams, statistical analysis manager for workforce analytics at Capital One. “Now we’re going back through resumes and creating a lot of that data,” he said.

In the wake of the financial crisis, when the compensation structure of many banks were criticized for incentivizing excessive risk-taking, Williams has also been asked to do an analysis of how pay is linked to sales performance.

“We do risk very well; we don’t lend to people who won’t pay us back…. Part of that is we have really good governance over our credit models; we have a staff of statisticians and that’s their job,” he said. “What I’m looking to do is a very similar thing in creating a governance process around some of the risk metrics for compensation.”

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