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Real-Time Analysis of Telco Data

Marginalised profit from network services is increasing pressure on communication service providers [CSP’s] to reduce or eliminate any major threat to these narrow margins. Threats are inherent in several parts of the organisation:

  • Revenue leakage - costing the industry $100 billion annually
  • Interconnect - inaccurate or missed inter-carrier billing
  • Fraud - a $12 billion annual industry problem
  • Churn - a multi-billion dollar problem worsened by the wireless number portability
  • Network - inefficient usage and least cost routing plans

Latency of visibility of any of these occurrences drives the magnitude of these threats, hence real time insight to performance is required. This requires continuous analysis of terabytes of CDR data using high-end data warehouses and powerful Business
Intelligence (BI) solutions.

Most carriers struggle with a gaggle of of general-purpose data warehouse solutions to store and analyze the mountains of data created every day.

Large networks and their associated switches, billing systems and service departments can generate hundreds of millions of individual CDRs daily. These terabytes of dynamic customer data will continue to grow exponentially as carriers add new services and as IP-based traffic increases.

This ever-expanding volume of data is straining the performance capabilities of traditional relational databases, servers and storage systems that provide the foundation for BI.

To analyze large volumes of records at the CDR level within reasonable time frames, or at a reasonable cost, using traditional methods requires sampling and summarising of millions of CDR’s in an effort to reduce the data load. Even this takes many hours of processing to analyze aggregated data sets on today’s platforms.

This processing limitation significantly reduces the effectiveness of:

And in turn, that affects the ability of the business to improve profitability.

Very few data warehouse solutions can meet this processing challenge. Of those which do, the following represent best in class:

Both of these data warehouses are optimized for handling real-time, terascale analysis of databases at the CDR level.


Telecommunications BI Challenges

Many critical telecommunications functions rely on fast, complex analysis of CDR data, including:

  • CRM - analyzing behavioral data to optimally target services and reduce churn
  • Billing - ensuring complete and accurate billing
  • Revenue Assurance - modeling call behavior
  • Network Performance - optimizing network operations using operations management programs.

Each of these functions improves in performance in direct relationship with improved access to CDR-level data.

Using traditional systems, the cost and time to acquire and manage large volumes of data rendered the process unviable. Using legacy servers and RDBMS systems, performing a single complex BI query against billions of CDR's takes hours or days.

This has prevented adoption of CDR-level analysis and prevents real-time proactive responses by carriers. In response to this challenge, most carriers either:

  • Summarize or filter the data for analysis
  • Create a massive, complex and often custom CDR warehouse to analyze CDR information.

Neither of these options provide complete information for decision-making.


Data Summarization

Call detail records are generated from:

  • Telecom networks and associated switches
  • Billing systems and
  • Service departments

Mainstream BI selects and summarizes data from these disparate data sources to create data marts for analysis. Summarizing the data means the CDR level of details is lost.

A Telco may produce 100 - 500 million CDRs per day.

Carriers must use this data to monitor:

  • Identify service adoption and usage
  • Monitor billing activity
  • Drive sales and marketing initiatives

The trade off is either to accept the cost and technical challenge of storing and accessing this data or not gaining visibility to calling patterns and the relationships between data using a sampling and summarizing approach.

For instance, if call volume was to decrease, and service levels increase, analysing cause and effect is not possible with only a subset of data. It would be impossible to analyze which event occurred first.

Further, aggregating data is inflexible from an analytical perspective, as fixed data
sampling formats are hard to change. If a particular data subset is skewing the main data set, it cannot be eliminated without programming changes to the sampling criterion.

This is time-consuming and costly, thereby limiting the value of the data.


Data Consolidation

One approach to this data challenge that avoids 'sample and store' is the use of consolidated warehouses to store all the CDR data. This approach provides key benefits such as:

  • Storing data in a single database, rather than in several data marts improves analysis, and is easier to maintain.
  • Allows analysis of trends based on complete historical detailed records
  • A consolidated warehouse approach provides a high degree of flexibility, supporting changes to analytical methods as the business changes.

Consolidated Warehouses

Building a terascale consolidated data warehouse requires:

  1. Construction of a large network of high-end servers
  2. Integration of terascale storage systems.
  3. Development of RDBMS software that can analyze millions or billions of CDRs.

These are costly projects that require large development and maintenance teams. As data volume grows, along with increasing complexity of historical, pattern-analysis queries and growth in the user community, many warehouses are unable to keep up with business demand.


Data Warehouse Appliances

Using a data warehouse appliance, such as Netezza solves the terascale data warehouse challenge without compromising on performance, supporting complex BI queries against billions of CDRs within minutes or seconds, versus hours or days.

A fully-integrated data warehouse appliance consists of:

  • A host computer
  • Arrays of hot-swappable mirrored storage
  • Custom chips and network switches that act as a powerful unit to manage data flows and process queries at the disk level.

Using Massively Parallel Processing architecture, systems such as Teradata ADW or Netessa NPS are specifically designed for high performance and scalability. Their performance capability dramatically reduces the latency of complex BI analysis, with data continuously loaded even during query performance. This means data is always completely current. More on Data Warehouse Appliances


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