“BOLLOCKS”, says a Cambridge professor. “Hubris,” write researchers at Harvard. “Big data is bullshit,” proclaims Obama’s reelection chief number-cruncher. A few years ago almost no one had heard of “big data”. Today it’s hard to avoid—and as a result, the digerati love to condemn it. Wired, Time, Harvard Business Review and other publications are falling over themselves to dance on its grave. “Big data: are we making a big mistake?,” asks the Financial Times. “Eight (No, Nine!) Problems with Big Data,” says the New York Times. What explains the big-data backlash?
Big data refers to the idea that society can do things with a large body of data that that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voice-recognition and translation, for example) that only work well when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data.
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