When it comes to hype and exposure, every week is a big week for Big Data. But rarely – if ever – has such a tool found itself directly in the middle of the mainstream media around the biggest story of the year.

In fact, the technology to analyze and leverage big data was lauded both inside and outside campaigns as a key influencer of last week’s presidential election.

For one, TIME Magazine’s election feature last week reported that “the era of big data has arrived” in politics, as President Obama’s team of number-crunchers allowed his campaign to focus on highly important, specialized groups of voters with the right messages. Additionally, New York Times pollster Nate Silver’s highly scrutinized predictive analytics practice ended up perfectly predicting the election outcome – 50 out of 50 states.

It’s easy to look at this picture and conclude that Big Data was the key catalyst. Very generally speaking, it was. But it was a particular portion of the Big Data equation that’s the real story – the ability of both groups to correlate and draw insights from the substantial variety of variables in the U.S. political climate.

For instance, the TIME piece cited the Obama campaign’s ability to realize that the American public is a highly fragmented group of diverse interests. Big Data aides used a combination of computing power and analytics to manage this fragmented base into a myriad of fundraising and voting campaigns. In Silver’s case, his complex strategy for weighting certain states’ demographic behaviors supercharged his polling prowess. It allowed him to properly segment and weight various fragments of voting districts throughout the Electoral College.

The key takeaway here is while both were technically “Big Data,” the primary issue was not the size of the data that needed to be analyzed. Instead, analysts were able to handle a large variety of data points, from a wide variety of sources, and then wrap around it a revolutionary degree of analytics and insight in order to make history.

We believe that Big Data strategists – and companies – should heed this example as a way to examine the heart of Big Data issue. It’s not size that matters as much as its complexity of sources. To those strategists who agree – or disagree – what do you think?

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