The potential for Big Data in Telecoms is immense, with the global big data market in the telecom sector growing at a CAGR of 55.24% over the period 2011-2015. Streaming integration and analysis of call data records (CDRs and IPDRs)combined with customer, device, location and network data is at the forefront of Big Data revolution in telecoms, offering real-time analysis and decisioning across a wide range of applications including real-time rating, least-cost routing, customer experience, location-based services and service performance.
Ahead-of-the-curve Telcos are starting to look into stream processing platforms that enable service providers to deliver real-time, actionable intelligence about their customers, billing, network and service operations- all while taking control of their system development and lifecycle costs.
But where’s the value coming from? Telecommunications has always presented a complex management problem. Many companies are struggling to deliver real-time performance and analytics as data volume, velocity and variety increase. But while the scale of the problem is exploding with the move towards all-IP networks, on-demand services and packet bandwidth, there remains an on-going focus on reduction of IT costs and the rationalization of the systems landscape.
A proper streaming Big Data solution can address, once and for all:
- Dynamic network operations (congestion, failures, traffic, bandwidth);
- QoS/QoE (failures, service activation and degradation, SLAs, hogging);
- Revenue management (real-time rating, charging, billing and billing integration);
- Call fraud (real-time fraud and roaming fraud detection).
For more information on real-time applications and how to implement them on a stream processing platform, read our whitepaper on CDR analytics. From this, you will learn how streaming analytics can be applied in wireless, cable, wireline, VoIP and other SIP-based service providers:
- How to leverage streaming Big Data technology to integrate and analyze streams of CDR, device, network and service data in real-time;
- Ways to enrich the customer experience, deepen subscriber relationships, and drive automated actions to optimize service and network performance in real-time;
- Optimized Customer Care workflows for efficient troubleshooting and reduced costs;
- Real-time fraud detection and prevention from CDR analytics for cost reduction and improved customer satisfaction.