The adoption of industry standards and more mature platforms will shift big data’s focus from IT-driven infrastructure projects to business-driven data solutions. Those who adopt big data strategies early and aggressively will realize operational efficiencies and top-line growth.
In 2016, we will see solutions to many of these challenges and the emergence of powerfully differentiated strategies from organizations that leverage their big data assets. Here are some key developments to look for in 2016.
One of the challenges with big data solution adoption has been a disconnect between business and IT. In many instances, IT has led the way, building out a big data infrastructure and adopting a myriad of new tools, often without the context of a specific business problem. The result is frequently a solution looking for a problem to solve.
Smarter organizations have taken a different approach, building solutions to specific business problems or building a data-as-a-service offer, giving the business the flexibility to select the tools they need to solve their specific problem. In 2016, we will see much more of these two approaches.
Some of the key use cases driving big data adoption include compliance, regulatory risk reporting, cyber security and trade surveillance. In 2016, we will see increased interest in revenue-generating use cases such as customer 360.
In 2015, we saw the emergence of the data lake — a single store for all enterprise data characterized by the ability to collect vast amounts of data in its native, untransformed format at a very low cost.
The data lake offers much promise but it also has limitations.