Are your data analytics predictions models suffering from the same problems as the models that predicted Hillary Clinton would easily win the US Presidential election? Here are a few things to consider.
How good is the data we rely on in our lives and businesses? After hundreds of polls were conducted and analyzed to predict who would win the US presidential election, the data scientists, statisticians, and media outlets that analyzed those polls failed to predict that Donald Trump would win against Hillary Clinton on Election Day. It was a shocker to many.
That brings up an important question for IT organizations that are investing in data and tools to analyze it. How good is that data? How good are those insights? Can we trust them? How can we make our predictive models more accurate? What lessons can we learn from the polling data issues that can be applied to our own organizations’ data efforts?
The first thing to do is ensure the quality of the data.
“Polls are just like any other analytical model,” said Bennett Borden, chief data scientist at the law firm Drinker Biddle & Reath, in an interview with InformationWeek. You have to ask, “Where are you getting your data from? Clearly there was a whole swath of the population that we did not reach in this polling data.”
Borden followed election polling on the FiveThirtyEight media site headed up by Nate Silver, who won fame for correctly predicting the 2008 election. The trained statistician subsequently released a bestselling book, The Signal and The Noise: Why So Many Predictions Fail but Some Don’t.
Silver’s site was the most pessimistic about a Clinton win, but it still had her as the favorite…
Are your data analytics predictions models suffering from the same problems as the models that predicted Hillary Clinton would easily win the US Presidential election? Here are a few things to consider. (Click image for larger view and slideshow.) How good is the data we rely on in our lives and businesses?
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