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February 2017, Vol. 5, No. 1

Don't learn lessons on predictive modeling techniques the hard way

The 2016 presidential election ended in stunning fashion, and it wasn't just because of who won. Indeed, Donald Trump's upset victory over Hillary Clinton triggered a political earthquake of seismic proportions. But another big surprise was seeing a campaign so focused on big data and predictive analytics fall to the candidate driven more by emotion and intuition. And it wasn't just the Clinton campaign that got caught off guard when voting didn't follow the path predicted by most analytical models. Virtually all analytics-driven election forecasters projected that Clinton would win, some with a probability as high as 99%. Even Trump's own data analytics team put his chances of pulling off a victory at only 30% the day before Election Day last November. It's often said that organizations should be more data-driven. Businesses that make decisions based on data analytics tend to outperform those that don't, according to proponents. Unquestionably, cutting-edge enterprises -- from Google, Amazon and Facebook to the likes of Uber ...

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