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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