Book Excerpt August 28, 2008, 5:00PM EST

Book Excerpt: The Numerati by Stephen Baker

By building mathematical models of its own employees, IBM aims to improve productivity and automate management

By Stephen Baker

Takriti headed the IBM team Aleksandra Mojsilovic

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BusinessWeek's 2006 Cover Story, "Math Will Rock Your World," announced a new age of numbers. With the rise of new networks, the story argued, all of us were channeling the details of our lives into vast databases. Every credit-card purchase, every cell-phone call, every click on the computer mouse fed these digital troves. Those with the tools and skills to make sense of them could begin to decipher our movements, desires, diseases, and shopping habits—and predict our behavior. This promised to transform business and society. In a book expanding upon this Cover Story, The Numerati, Senior Writer Stephen Baker introduces us to the mathematical wizards who are digging through our data to decode us as patients, shoppers, voters, potential terrorists—even lovers.

One of the most promising laboratories for the Numerati is the workplace, where every keystroke, click, and e-mail can be studied. In a chapter called "The Worker," Baker travels to IBM (IBM), where mathematicians are building predictive models of their own colleagues. An excerpt:

On a late spring morning I drive up into the forests of Westchester County, N.Y., to the headquarters of IBM's Thomas J. Watson Research Center. It sits like a fortress atop a hill, a long, curved wall of glass reflecting the cotton-ball clouds floating above. I have a date there with Samer Takriti, a Syrian-born mathematician. He heads up a team that's piecing together mathematical models of 50,000 of IBM's tech consultants. The idea is to pile up inventories of all of their skills and then to calculate, mathematically, how best to deploy them. I'm here to find out how Takriti and his colleagues go about turning IBM's workers into numbers. If this works, his team plans to apply these models to other companies and to automate much of what we now call management.

Takriti, a slim 40-year-old with wide, languid eyes, opens the door of his small office. He wears a rugby shirt tucked tightly into blue jeans. I tell him that being modeled doesn't sound like much fun. I picture an all-knowing boss anticipating my every move, perhaps sending me an e-mail with the simple message, "No!" before I even get up my nerve to ask for a raise. But Takriti focuses on the positive. Imagine that your boss finally recognizes your strengths, he says—maybe ones that are hidden even to you. Then he "puts you into situations where you will thrive."

COMMODITIZING WORKERS

Still, Takriti confesses that he's nervous. His assignment is to translate the complexity of highly intelligent knowledge workers into the same types of equations and algorithms that are used to fine-tune shipping or predict the life span and production of a mainframe computer. With time, he and his team hope to build detailed models for each worker, each one complete with a person's quirks, daily commute, and allies, perhaps even enemies. These models might one day include whether the workers eat beef or pork, how seriously they take the Sabbath, whether a bee sting or a peanut sauce could lay them low. No doubt, some of them thrive even in the filthy air in Beijing or Mexico City, while others wheeze. If so, the models would eventually include this detail, among countless others. The idea is to build richly textured models that behave in their symbolic realm just like their flesh-and-blood counterparts. Then planners can manipulate them, looking for the most efficient combinations.

Takriti's team is hardly starting from scratch. IBM has long been a leader in converting all kinds of complex systems into numbers. Right after World War II, Big Blue used a new science called Operations Research to construct a mathematical model of the company's industrial supply chain. It included its costs and capabilities, as well as limitations, or constraints. Once the supply chain existed as numbers, engineers could experiment with it—optimizing it—and later incorporate the improvements in the real-life version. This drove efficiency and lowered costs.

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