Every voice call and IP service in a telecommunications network generates usage records.  These service usage records, Call Details Records (CDR) for a voice network and IP Data Records (IPDR) for IP networks, contain information about the call or session that is used for applications such as billing, service quality monitoring and fraud detection. Data record formats are controlled by standards, but unfortunately standards vary by industry. For example mobile, cable networks and IP SIP-based networks all have different reporting capabilities and requirements.

Most service providers analyze CDRs through batch processing, usually once a day, but sometimes as infrequently as weekly or even monthly. Even where certain types of CDRs are processed more frequently, this is at best hourly. The reason is in part the performance restrictions of the underlying legacy platforms, but also a belief that real-time, sub-second CDR analysis is too costly. This may have been true with traditional RDBMS-based, big data processing frameworks, but a streaming analytics platform can now enable millions of CDRs per second to be collected and processed in parallel, through complex event processing.

Real time analytics for CDR applications offer the lowest total cost of performance of any approach – less hardware, lower software costs, with prebuilt integrations and analytics libraries, significant lower implementation and maintenance costs.

Here’s how a streaming analytics platform running real time analytics on a streaming data ingestion framework can support the primary applications for Telecom.

 

Real-time fraud prevention

Call fraud that remains undetected can get passed through to customer bills and have a detrimental impact on customer experience. Even in most cases where fraud is detected, detection tends to be the following day or even after the weekend, resulting in direct revenue loss and additional OPEX expenditure to correct. Streaming analytics enables the immediate, real-time detection of suspect call activity based on data streams such as switch logins, IP spoofing events, unusual call destinations and unusual call usage patterns.

 

Real-time call rating

Rating is the process of determining the charge for each call or service session.  The process involves CDR collection, removal of duplicates, session reconstruction, caller ID or IP verification, cost calculation based on the rate plan, application of discounts and finally storing the records in a CDR warehousing. Although rating is a core function of any billing operation, most rating engines still operate in batch mode due to scalability, data integration and rapidly changing business requirements. Streaming CDR processing and analytics can deliver real-time rating at scale, while persisting aggregated rating information continuously through to the core billing platforms.

 

Customer Reporting

Access to real-time billing information has a significant positive impact on customer experience. Streaming analytics for real-time rating, performance monitoring and policy control can be used to deliver real-time visibility of service performance, costs and discounts to the customer.

 

Optimization of Least Cost Routing (LCR)

A routing system provides the network path for a call or session based on quality requirements, discounting models and operator profitability. For example, the selection of the lowest cost interconnect partner for roaming calls in a wireless network, or for SIP-based SBC (Session Border Controller) networks, the selection of the lowest cost IP network path given the desired quality. Operational intelligence from streaming CDR analysis can be used to update least cost routing tables dynamically, either to improve a customer’s service where quality has fallen below the SLA, or to increase profitability where quality can be maintained at appropriate levels.

 

Call performance monitoring

Most performance monitoring tools aggregate data by 15 minute intervals, usually aggregated by port or even by network device. However, most IP-based services are bursty by nature, where problems may last for a few seconds, but be sufficient to force a quality issue or dropped call. Streaming analytics can provide real-time monitoring of both network performance and CDR data in order to identify issues in real-time. Streaming intelligence can be also be integrated with the LCR database, policy server or bandwidth managers in order to drive dynamic updates.

 

Real-time profitability analysis

Service providers calculate metrics such as gross margin based on comparing the cost to the customer versus the cost of service delivery for that customer. However, margin analysis is normally an offline activity based on long-term trend data. With streaming analytics, the profitability of a service offering, a customer type, or even an individual customer can be visualized in real-time. This enables real-time decisions to improve profitability, for example, through dynamic updates of the least-cost routing algorithms.

 

Dynamic policy management

The Policy Manager or Server is responsible for (a) the selection of quality and routes and (b) actions to be taken (often by customer type against SLAs). For example when a call drops or has quality issues, or bandwidth limits are reached during the session, dynamic updates based on streaming analytics can drive corrective action to bring the service back within its SLA. Other policy-driven actions include the proactive notification of the consumer, or the application of discounts applied during or after the session.

 

Standard Formatting for CDR Warehousing

Much of the cost of CDR processing is the conversion of many different CDR formats and structure to a common format that can be used for further analysis. Streaming ingestion addresses this as part of the core stream processing pipeline, transforming all CDRs to a standard format in real time, and delivering this as a continuous, real-time Load to existing CDR warehousing platforms.