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Three Misconceptions About Barriers to Big Data Analytics

The data tsunami is upon us – most organizations are drowning in it. The presidential election delivered on the promise of Big Data through Nate Silver’s masterful use of predictive analytics, namely that ‘there is gold in them hills’ of data. Analytics can convert this raw data into gold. But does every organization need a Nate Silver to find that gold?

That’s the question that most CIOs are asking and the search for the right tools and technologies for Big Data Analytics seems fraught with obstacles from cost to know-how. And it’s no wonder – if you are seeking Nate Silver-style results but you’re approaching Big Data Analytics with limited-capacity BI/Visualizations tools or the usual brute force database queries, Hadoop processes, or OLAP, then, yes, Houston, you do have a problem.

The truth is, pattern-based predictive analytics solutions, fueled by the most advanced class of mathematical algorithms, overcome these so-called roadblocks, many of which are outlined in a recent TechRepublic article, “10 roadblocks to implementing Big Data analytics.” Pattern-based predictive analytics solutions can actually yield smarter, faster and more accurate results – regardless of scale. And you don’t need to be Nate Silver to use them. 

This recent article listed ten roadblocks to implementing Big Data analytics, but we took a closer look at the three biggest obstacles and conducted a side-by-side comparison of approaching Big Data analysis using conventional methods versus technology designed specifically for Big Data Analytics HERE on our blog.

Photo Courtesy: IUGG

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