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 tool or the usual brute force database queries, Hadoop processes, or OLAP, then, yes, Houston, you do have a problem.

The truth is pattern-based 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 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 let’s take a look at the three biggest obstacles and conduct a side-by-side comparison of approaching big data analysis using conventional methods versus technology designed specifically for big data analytics.

Business know-how

It’s difficult to know how to query big data to answer the big questions. Present skills fall short in businesses when using traditional querying and mining strategies powered by the data scientist. Pattern-based predictive analytics do not rely on queries, searches and data modeling. Instead, they automatically query the data for you to reveal what you should be paying attention to.

Data cleanup

Big Data and business analytics are only as good as the data itself. This is why cleaning up data to ensure that incomplete, inaccurate, and duplicate data is removed should be the first step of any big data project. The CIO must explain this and secure top management’s support for a big data cleanup, which will seem to those on the outside as a lot of effort expended for no tangible results. The best approach to selling the process is to present the facts upfront so there are no surprises.

Data cleansing isn’t an issue with the pattern-based approach. It automatically weeds out garbage data based on connections between data points to identify the hidden gems within your data.

Developing new talent

Data engineers and data scientists are in high demand and they are very expensive. Larger volumes of data spread across multiple sources means data scientists/engineers will spend more time organizing the data rather than analyzing it. And if their queries and models aren’t right to begin with, the entire model has to change and that takes even longer – during which you’ll lose your competitive advantage.

That’s the beauty of pattern-based analytics: No IT army, no data cleanup, no data scientist or querying, just automatic and accurate results.