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Big Data: Problem Data Skews Predictions
Despite increasingly sophisticated algorithms, long-term and even short-term forecasting still often come up short and often miss the mark by wide margins. Why is that? Cleaning data and identifying which data is most important are key.
Pew Research commented following the 2016 presidential election that “the fact that so many forecasts were off-target was particularly notable given the increasingly wide variety of methodologies being tested and reported via the mainstream media and other channels.”
It’s not just election forecasts that fail. AppleInsider recently reported how both IDC and Gartner grossly underestimated Apple’s quarterly sales estimates. “Despite issuing numbers that were so off base that they portrayed the very direction of the market and of individual companies incorrectly, numbers issued by the two companies were picked up and presented as fact by a variety of tech blogs.”
Nate Silver, statistician and journalist, said that “we’re not that much smarter than we used to be, even though we have much more information—and that means the real skill now is learning how to pick out the useful information from all this noise.”
