“We are in the midst of a great infrastructure project that in some ways rivals those of the past, from Roman aqueducts to the Enlightenment’s Encyclopédie,” write Viktor Mayer-Schönberger and Kenneth Cukier in their recent book, Big Data: A Revolution That Will Transform How We Live, Work, and Think. Like those other infrastructural advances, it will bring about fundamental changes to society.”Some of the changes are well known, and already upon us.
Algorithms that predict stock-price movements have transformed Wall Street.
Algorithms that chomp through our Web histories have transformed marketing.
You can now find dedicated analytics teams in the human-resources departments of not only huge corporations such as Google, HP, Intel, General Motors, and Procter & Gamble, to name just a few, but also companies like Mc Kee Foods, the Tennessee-based maker of Little Debbie snack cakes. Last year he appeared at a large conference for corporate HR executives in Austin, Texas, where he reportedly stole the show with a talk titled “The Moneyball Approach to Talent Management.” Ever since, that headline, with minor modifications, has been plastered all over the HR trade press.
The application of predictive analytics to people’s careers—an emerging field sometimes called “people analytics”—is enormously challenging, not to mention ethically fraught. It requires the creation of a vastly larger box score of human performance than one would ever encounter in the sports pages, or that has ever been dreamed up before.
To some degree, the endeavor touches on the deepest of human mysteries: how we grow, whether we flourish, what we become.
Most companies are just beginning to explore the possibilities.
Michael Lewis and his best seller Moneyball, the general manager of the Oakland A’s, Billy Beane, became a star.
The previous year, Beane had turned his back on his scouts and had instead entrusted player-acquisition decisions to mathematical models developed by a young, Harvard-trained statistical wizard on his staff. The A’s, a small-market team with a paltry budget, ripped off the longest winning streak in American League history and rolled up 103 wins for the season.
Only the mighty Yankees, who had spent three times as much on player salaries, won as many games.
The team’s success, in turn, launched a revolution. What would seem at first glance to be nothing but a memorable tale about baseball may turn out to be the opening chapter of a much larger story about jobs.
In the years that followed, team after team began to use detailed predictive models to assess players’ potential and monetary value, and the early adopters, by and large, gained a measurable competitive edge over their more hidebound peers. Predictive statistical analysis, harnessed to big data, appears poised to alter the way millions of people are hired and assessed. As a piece of business jargon, and even more so as an invocation of coming disruption, the term has quickly grown tiresome.
But there is no denying the vast increase in the range and depth of information that’s routinely captured about how we behave, and the new kinds of analysis that this enables.
By one estimate, more than 98 percent of the world’s information is now stored digitally, and the volume of that data has quadrupled since 2007.