Tuesday, January 1, 2013

"Sure, Big Data Is Great. But So Is Intuition"

An NYT article is skeptical of inflated claims for Big Data:

The quest to draw useful insights from business measurements is nothing new. Big Data is a descendant of Frederick Winslow Taylor’s “scientific management” of more than a century ago. Taylor’s instrument of measurement was the stopwatch, timing and monitoring a worker’s every movement. Taylor and his acolytes used these time-and-motion studies to redesign work for maximum efficiency. The excesses of this approach would become satirical grist for Charlie Chaplin’s “Modern Times.” The enthusiasm for quantitative methods has waxed and waned ever since.

Big Data proponents point to the Internet for examples of triumphant data businesses, notably Google. But many of the Big Data techniques of math modeling, predictive algorithms and artificial intelligence software were first widely applied on Wall Street.

At the M.I.T. conference, a panel was asked to cite examples of big failures in Big Data. No one could really think of any. Soon after, though, Roberto Rigobon could barely contain himself as he took to the stage. Mr. Rigobon, a professor at M.I.T.’s Sloan School of Management, said that the financial crisis certainly humbled the data hounds. “Hedge funds failed all over the world,” he said.

The problem is that a math model, like a metaphor, is a simplification. This type of modeling came out of the sciences, where the behavior of particles in a fluid, for example, is predictable according to the laws of physics.

In so many Big Data applications, a math model attaches a crisp number to human behavior, interests and preferences. The peril of that approach, as in finance, was the subject of a recent book by Emanuel Derman, a former quant at Goldman Sachs and now a professor at Columbia University. Its title is “Models. Behaving. Badly.”

It really is a matter of proper scope, and consciousness of limits. Big Data is wonderful for finding the Higgs Boson among billions of particle paths, or tracking potential credit card fraud. It is not so good at many other tasks where the data is absent or incomplete or misleading. In those cases, it is little different from ancient farmers looking at the sky and seeing mythical animal patterns.

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