In order to answer these questions, a Big Data solution for telecom must possess the following attributes:

• the ability to manage petabytes of data on a daily basis (1 petabyte = 1,000 terabytes);
• the capacity to integrate a wide variety of data sources and types of data;
• highly reliable;
• metadata aware;
• has low latency;
• and has deep insight into the network.
  

To achieve total visibility, the telecom big data solution must be able to mix terabytes of structured data from CRM, billing, and network sources with petabytes of unstructured data from applications and services like Netflix, Facebook, and Twitter. Most solutions on the market meet some of these criteria but lack deep insight into the network itself. Other solutions might tap into high-level network layers, but don’t have visibility down to Layer 1 through Layer 3 of the network. Hiding in the shadows of big data are risks (as well as rewards) that can only be illuminated by total network awareness. For MNOs, a big data solution without total network visibility is incomplete and that is why network equipment providers have a very important role to play in the Big Data space.

Is your Big Data solution telecom specific?

In a recent study, IDC forecasts a 50-fold growth in the amount of data over the next seven years, and much of this information will be carried over mobile-broadband networks. There are expected to be 9 billion mobile subscriptions by 2017, and Ericsson predicts mobile broadband use will more than triple over the same time period. The challenges facing MNOs are significant and unique, which is why big data solution providers with telecoms expertise are preferable.

Justin Van Der Lande, senior analyst, Analsys Mason, recently advised communications service providers (CSPs) that “suitable suppliers not only need to be able to provide the technology to store, model and visualise data, but they also need to have telecoms-specific knowledge as well.”

The power of network data

Total visibility into all data streams, including network data, enables truly remarkable use cases for CSPs and provides the foundation for future growth in telecommunications. “In the not-so-distant future, mobile operators will also be able to generate revenues from the packaging and selling of this data,” writes Carlos Pinto, senior manager, Analysys Mason. “With traditional revenues such as voice and SMS under pressure from web-based services and over-the-top (OTT) providers, and with mobile broadband now reaching its peak of profitability in developed economies, we expect big data to be the next source of enhanced profitability.”

The appropriately designed big data solution enables CSPs to derive actionable intelligence from network data that results in closer intimacy with customers, more effective promotions, improved operational efficiency, and new revenue streams. A leading MNO in the United States reduced churn by 50% in one quarter with its big data strategy. A quad-play operator in the Middle East estimates the top five uses cases for big data represent a potential value of $3.6 billion to the company over three years.

Customer experience management (CEM), another top area of interest for CSPs, can be dramatically improved with deep network visibility. Extracting network data and feeding it into the big data solution allows CSPs to identify customer groups that are subject to network congestion, poor coverage, faulty configurations, malfunctioning devices, and/or inappropriate subscriptions, and then take proactive action. Any number of profiles can be created and analyzed in real-time, such as high signaling impact groups, high spenders, nomadic customers, churned subscribers, and influencers. The impact and uptake of new devices can be instantly understood. Further, armed with network data, customer-centric RAN and device optimization becomes a reality.