The Exploitation of Data in the 2016 Election

By January 18, 2017Analytics, Data Discovery

Unless you poor, unfortunate souls have been living under a rock without wifi, cable, or possibly even something as old fashioned as a newspaper, you should be aware that we are moving towards the inauguration of the 45th President of the United States of America on January 20, 2017. Regardless of political leanings, there was probably surprise in learning polling results, as forecasts fumbled with several wrong calls. After fumbles like that, people want someone – or something – to blame for being caught off guard by the election’s outcome. Did “big data” fail us? Was it used incorrectly, or even not at all?

Short and sweet: the data was misunderstood and taken out of context, wide margins of error were not considered in some analyses, and there was a miss in communication between data analysts and the media professionals who shared these forecasts with the public. An article by the New York Times referred to the phenomenon of the 2016 Presidential Election as an “overselling of precision,” claiming that “the rush to exploit data may have outstripped the ability to recognize its limits.”

When Barack Obama won the presidency, his campaigns were driven using data analytics platforms. Leonid Bershidsky, a Bloomberg View columnist, wrote a post about the data-driven campaigns and often references Obama’s campaign manager, Jim Messina. Messina was a strong believer in utilizing the power of data, but argues that the buzzword of “big data” is becoming obsolete and future campaigns needed to be targeted more closely: “Huge data sets are often less helpful in understanding an electorate than one or two key data points — for instance, what issue is most important to a particular undecided voter.” When Hillary Clinton competed for the seat, she also employed data scientists and analytics applications to draw in the undecided voter, whereas Donald Trump did not invest as much in data and science. Rather, he advanced through the power of a strong presence. This does not mean that employing data is a worthless endeavor. In Did Big Data get ‘Trumped’ in Election 2016?, writers remind that Donald Trump did not join the presidential race with a political background. However, the influence of celebrity paired with political science proved that “strategy still plays a role in winning campaigns and that more paths to victory exist than the ones that others have trodden. His strategy of mastering social media, large rallies, and media wars weren’t neglectful of the science of campaigns. They were strategic bets that invested in Trump’s strengths and capitalized on his celebrity blazing a unique path to victory.”

Fortune backs up this defense of big data: election results were not “a failure of data; [they were] a failure of forecasting and analysis — by humans. The data was as good as it could be, but the analysis of it lacked depth…We need to get better at understanding what the data can tell us — its potential and limitations — and how it fits into a broader analytical picture.” Clinton ran one of “the most data-driven” campaigns in U.S. history, according to the Economist, but her platform counted too much on what the data was telling them, and while data provides information, the interpretation of that information is entirely subjective. Clinton’s supporters may have just been seeing what they wanted to see, and she told demographics what they wanted to hear.

In short, big data should not be a scapegoat to hold any electoral woes or aggressions. Data is a tool, and it needs to be used properly. In the future, data of all sizes needs to be looked at more objectively, to give citizens a clear view of the possibilities. We all also need to remember that “data is not a substitute for innovation.” Winning takes not only the right tools, but strategy.

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