Big Data Analysis Tool Predicts Republican Debate Winners, Losers

By Wayne Rash  |  Posted 2015-09-19 Print this article Print
Big Data Debate

Wisconsin governor Scott Walker wasn't eliciting any significant response at all, no matter what he said. The debate audience simply didn't seem to react to Walker or his message.

Other candidates including New Jersey governor Chris Christie and former governor Mike Huckabee weren't doing as poorly in the results as Walker, but the Twitter response to those candidates was mostly tepid with some occasional but limited activity. The other GOP candidates were at least holding their own.

Of course, none of this is forecasting the eventual outcome of 2016 election. It's much too early to even know who will be running in 2016, much less know what the sentiment of the voting public might be.

But we did learn the sentiment of the viewing public on that night of the debates and it was later supported by independent pollsters and by the usual pundits. What was critical was that I learned the results at least 12 hours before anyone else did.

Had the candidates or their parties been watching these results, they would have known what was resonating with the public, what wasn't resonating, and in some cases they would have been able to make timely changes.

While there wasn't any coaching going on during the debates, over the course of any election campaign there's a lot of communication between candidates and their strategists. If they find that something the candidate is saying isn't resonating, or worse, is turning voters off, the candidates will change their message quickly enough.

But something else important came out of our work with LUX2016 during the debate. As part of the process, we discovered just how agile this cloud-based big data analysis tool is.

"LUX is a real-time analytics platform," Waldrop explained. "It includes a complex events processor with plug in modules [and] an intuitive easy to use interface." Waldrop said that you can combine inquiries "as if they were Lego building blocks and stack them on top of each other."

Waldrop said that one thing that makes LUX so powerful is the fact that it's data agnostic. While we were analyzing Twitter messages as an indicator of voter sentiment, we could have been measuring anything that's quantifiable.

The difference between LUX and many other big data analysis platforms is the fact that it runs in real-time. We watched voter sentiment change as it happened, but Waldrop said that it could have been watching security events or the temperature of bearings on a factory full of manufacturing machines.

LUX started out in the intelligence community and Waldrop won't discuss how those clients were using the software, but what's really important is that it's no longer necessary to analyze big data weeks or months later. Now analysis can be done in real time.


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