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Is Trump's unexpected victory a failure for big data? Not really

Grant Gross | Nov. 10, 2016
Polls go by the old IT maxim: 'Garbage in, garbage out'

Wang said he's still evaluating the results. One reason for the incorrect predictions may have been late decisions by so-called undecided voters.

"Pollsters use the word 'undecided,' but what that really means is that they are unable to verbalize their preference -- they may not even know what it is themselves," he said. 

Early in the campaign, about 20 percent of Republican-leaning voters were undecided, Wang said. "For them, voting for Trump was a battle between party loyalty and reaction to a fairly radical candidate," he added. "It may be that party loyalty has won."

Wang and Tony Baer, a big data analyst at Ovum, both suggested that polls may have undercounted hard-to-reach voters. With many U.S. residents dropping their landlines, it's also more difficult for pollsters to target the right people, Baer said.

In other cases, people responding to the polls may have lied. Pollsters may have gotten "the wrong signals," Baer added by email.

"When you have data sets that are large enough, you can find signals for just about anything," he added. "So this places a premium on identifying the right data sets and asking the right questions, and relentlessly testing out your hypothesis with test cases extending to more or different data sets."

Asked for his reaction to Trump's victory, Baer said he was "just as perplexed as anybody."


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