The primary benefits of unlocking big data can be summarized in three categories: innovation, optimization, and control.

Many organizations are introducing new services and products using different ways of analyzing structured, unstructured, and semistructured information. Others are employing much larger data sets to make their processes more efficient and cost-effective. And still others focus on addressing their compliance requirements by storing and managing vast amounts of information.

One of the key differentiators between organizations with higher and lower levels of big data competency is the existence of a big data strategy. Organizations that have such a strategy incorporate into it a range of capabilities. These organizations understand the different requirements of their various internal user groups. They understand the shortcomings that exist in strategic, operational, and tactical decision making due to the lack of timely, complete, and actionable data. They understand as well that there are different workloads for big data use cases and different technology requirements to support those workloads.

This explains why there remains a significant role for traditional, proven technology. While there is a lot of focus on some of the newer technologies like Hadoop, the traditional relational databases for data warehousing will continue to exceed the average growth rates in warehousing technology of the previous five years. IDC research shows that analysis of operational data is the number one driver for investment in big data technologies — it ranks higher than analysis of data from social media. This clearly indicates the need for different types of technologies to coexist.