Big data can reveal actionable user groups as well—subscribers who would likely pay more for something more from the operator, such as greater bandwidth or network usage. Real-time business triggers can be created for location changes and subscriber group expansion, and network resources can be allocated appropriately and dynamically.

A comprehensive, telecom-centric big data solution can be leveraged to monetize new modes of communication as well. Machine to machine communication (M2M) promises to deliver the internet of everything, and visibility into this burgeoning communications network will expose the best market opportunities as well as the biggest challenges. The re-use of legacy wireline networks for Gigabit home networking (G.hn) is also likely to open new revenue streams for the savvy CSP.

In a highly competitive era, where traditional voice and messaging revenues continue to evaporate, big data can have a transformative effect on service providers who monetize their unique data assets. As Carlos Pinto indicated, customer data is the foundation of a rich revenue opportunity, and is the new currency of the digital economy. Of all companies, MNOs are best-positioned to provide exclusive and valuable subscriber insights to third-parties, either through anonymized data, or opt-in, non-anonymized data. Every second of the day, MNOs collect wholly unique information that is unavailable to companies in any other industry. By applying Content, Context, and Location insights, service providers can monetize a framework for deeper understanding of subscriber segmentation, service use habits, location, time-of-day, and more. Correlating content, context, and location delivers a more complete and therefore more valuable customer profile, and moves big data beyond an operational enhancement tool to a value center in its own right.

Seeing the light

Turning Big Data into actionable intelligence requires the right platform for the job, but not all Big Data solutions are equal. MNOs possess a real-time, comprehensive view of their customers, so the Big Data solution that’s most appropriate for their needs should have the ability to collect all subscriber interaction data in order to build unique patterns and insights. 

The telecommunications domain, however, presents one of the biggest Big Data challenges of all. Just as the UNIVAC can no longer be used as a billing system, legacy databases cannot be used for Big Data applications in telecom. Due in part to the buzz, countless Big Data solutions exist on the market, but few are properly designed for CSPs. That’s why a solution that provides total network visibility down to Layers 1 and 2 is crucial, and why solution providers with a history of telecom experience are best positioned to deliver a winning platform.