1/2/13 Big Data Is Great, but Don’t Forget Intuition - NYTimes.com 1/4 www.nytimes .com/2012/ 12/30/technolo gy/big-data- is-great-but- dont-forget -intuition.html?r ef=techn… Sure, Big Data Is Great. But So Is Intuition. John Hersey By STEVE LOHR [1] Published: December 29, 2012 It was the bold title of a conference this month at the Massachusetts Institute of Technology [2] , and of a widely read article in The Harvard Business Review last October: “Big Data: The Management Revolution.” [3] Related Bits Blog: Big Data: Rise of the Machines [4] (December 31, 2012 ) Andrew McAfee [5] , principal research scientist at the M.I .T. Center for Digital B usiness [6] , led off t he conference by saying that Big D ata would be “the next big chapter of our business history.” Next on st age was E rik Brynjolfsson [7] , a professor and director of the M.I.T. center
4
Embed
Big Data Is Great, but Don’t Forget Intuition - NYTimes.pdf
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
7/27/2019 Big Data Is Great, but Don’t Forget Intuition - NYTimes.pdf
and a co-author of the article with Dr. McAfee. Big Data, said Professor Brynjolfsson, will
“replace ideas, paradigms, organizations and ways of thinking about the world.”
These drumroll claims rest on the premise that data like Web-browsing trails, sensor signals,
GPS tracking, and social network messages will open the door to measuring and monitoring
people and machines as never before. And by setting clever computer algorithms loose on the
data troves, you can predict behavior of all kinds: shopping, dating and voting, for example.
The results, according to technologists and business executives, will be a smarter world, with
more efficient companies, better-served consumers and superior decisions guided by data and
analysis.
I’ve written about what is now being called Big Data [8] a fair bit over the years, and I think it’s a
powerful tool and an unstoppable trend. But a year-end column, I thought, might be a time for
reflection, questions and qualms about this technology.
The quest to draw useful insights from business measurements is nothing new. Big Data is a
descendant of Frederick Winslow Taylor’s [9] “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, notablyGoogle. 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 [10] 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 [11], a former quant at Goldman Sachs and now a professor at
Columbia University. Its title is “Models. Behaving. Badly.”
Claudia Perlich [12], chief scientist at Media6Degrees, an online ad-targeting start-up in New