Interest surrounding big data has exploded exponentially since the past one year. In the beginning, the “big” aspect of big data was the top concern for business leaders and IT professionals alike: Where do we store these vast amounts of data? How do we manage data that continues to grow exponentially?

Within a short time a wave of advanced technology and solutions has flooded the market. As a result, the initial questions around big data have been solved.

Now, as the competitive landscape continues to evolve, technology vendors are racing to produce big data solutions that actually spur business growth by using analytics to find data insights. It’s no longer about the size of the data, or even the kinds of data companies can access, but how companies are collecting and managing data from multiple sources and using advanced data analytics to gain actionable insights.

By layering new analytical methods and tools on top of big data solutions, companies can tap into greater productivity and gain more insight into the past, present and future of their business—resulting in more informed decision making and measurable ROI.

A quick scan of today’s big data headlines reveals that, like any emerging technology trend, an influx of hype and unintentional misinterpretation of even the best approaches can leave many at a loss when trying to understand what big data means for them. But one thing is clear: The ability to separate hype from reality early on will be crucial to a company’s success in this arena.

Here are some developments in data analytics we expect to see in 2014 and beyond. They should provide some clarity for executives and enterprises that want to make major inroads with their data analytics initiatives.

Big data will be trumped by advanced analytics for data diversity.
The term “big data” has been misunderstood or generalized as the ability to extract intelligence from massive volumes of disparate data. In 2014, companies need to stop focusing on “big” and instead focus on “better,” i.e., better managing the variety and complexity of the data that is being acquired for complex analysis.

It’s not an easy task, but the tools and platforms to accomplish this are currently available (and becoming increasingly more useful), and will allow businesses to laser-focus on their advanced analytic capabilities.

Companies of all sizes already have large stockpiles of data, and we are seeing enterprise data warehouses in excess of 30, 40 or 50 terabytes across all sizes and types of organizations. But where do you go from there? Advanced analytics provides the insight needed when leveraging traditional, structured data—giving companies a starting point toward gaining increased operational efficiencies, profits and competitive advantage.

Enterprises that are successfully gaining legitimate value from their structured, transactional data assets will be able to layer on unstructured or semi-structured data sets to produce a complete picture. That will take their analytics initiatives to the next level.

Heightened focus on governance will improve analytics.

Moving forward, governance will be the major driver for increasing the ROI of data analytics. If businesses can properly gather and leverage all the investments they have made to produce a complete data landscape, they will ultimately be able to successfully extract valuable analytic insights. These platforms provide many of the operational insights into the “what” when holistically viewing data analytics.

When governance is layered over the framework of a data analytics platform, the result is a holistic understanding that ultimately improves the company’s ability to manage and measure ROI—a top concern for virtually every organization and business leader. The more companies invest in strategic analytics, the more they will need a robust data governance plan to ensure that proper results can be extracted from any given body of data. That will be the crux of immediate and long-term success.

More pilots for cloud analytics will take flight.

Although the benefits of cloud computing have become increasingly apparent (efficiency, flexibility, improved cash flow), some companies are still hesitant to take their big data analytics to the cloud, citing reasons that involve security, timing or overall access to resources. However, in 2014, we will see more organizations choosing to pursue smaller, piloted big data cloud initiatives. By taking a smaller-scale approach, enterprises can efficiently evaluate their data and more quickly respond to demand for actionable insights, as opposed to going all-in with their cloud initiatives at the start.

If, as many predict, 2014 is truly the year of machine-to-machine data, organizations will have a specific business case for pursuing smaller, more nimble analytics, and cloud computing is the de facto solution for doing so. Today’s cloud landscape consists of secure sources and solutions that can be easily tailored to fit the needs of a project of any size in a fraction of the time and cost associated with a major cloud overhaul.

We can expect adoption and innovation in data analytics to become more advanced and have greater precision in the years to come. Organizations already see the value of managing and leveraging their data, but the true value lies in how it is being managed. The tools, processes and trained personnel needed to successfully extract actionable insights from these data sets—whether structured, unstructured or semi-structured—will become the competitive advantage that matters most to your organization.

Eventually, as with any major technological advance, data analytics will be a commonplace feature of everyday life—occurring at all scales and in multiple capacities. Until then, companies need to focus on what they can do in the short term regarding their data analytics capabilities and put in place the foundations to create long-term success.