To enable this knowledge discovery, a novel approach to big data is needed. Legacy analytic database solutions, as well as many modern offerings, aren't meeting the big analytics challenge.

For one, many organizations have created expensive static workarounds to the dual problems of big data and big analytics -- from laborious database tuning with armies of DBAs, to proprietary hardware -- which are not conducive to this new era of intense analytics.

Many companies also utilize databases originally designed for transaction processing in the 1980s, not the big analytics of today's hyper-fast business landscape, and are only able to pull from structured data sources but not unstructured sources such as the Internet, social media or even satellite imagery. Finally, old and slow solutions such as these are combined with today's complex solutions, and unable to draw out critical insights on business buried by this complexity.

By contrast, the new generation of analytic platforms solve the big analytics problem by integrating on two broad levels. On the infrastructure level, they integrate and leverage existing hardware and software technologies while satisfying the most demanding analytic requirements. On the data level, these platforms seamlessly integrate analytic algorithms written in any language and run them completely parallel inside the platform next to the data. The modern analytic platform is also able to integrate with and consume data, whether structured or unstructured, from multiple data sources in and out of the enterprise.