Monetization of telecom data lakes beyond functional silos

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Telecommunications industry has traditionally been an early digital leader itself, in addition to being key enabler for digital revolution among other industries. They have been the pipelines of new data-oil. Massive data is generated within Telcos on regular basis, that is not considering external related data like social feeds. Majority of these data are generated by telecom-native systems like Network, Charging systems and Enterprise, CRM lead-2-cash processes.

Analytics function has been more business user centric and less oriented towards operations and networks functions. Typical use cases and users have been from- MIS, Reporting, Business Assurance, Customer Care functions. Core network and operations functions relied on the native tools for measurement, analysis and troubleshooting. That is not to discredit the operators that tried and led holistic view of aspects like Customer Experience through massive IT investments. However, many end objectives like- Profitability, Assurance, Customer Experience demand a cross-functional view across Network, Finance, IT, Commercial data.

The functions generate different scale, variety, speed of data, an ideal case for Big data analytics. The adoption of big data lakes in telecom to some extent allowed ingestion of varied sets of data across telecom organization. On paper, this can break the siloed data-mart limitations of yesteryear. Operators are at various stages of implementing the massive data lakes. But, even operators at the forefront of adoption await use cases to justify the massive investments.

This turns out be an expensive solution in search of problem. The objective of this post is to discuss a sample objective that can open doors for cross-functional analytics use cases in big data era. Let’s consider a use case of assurance. Imagine a scenario where certain data sessions have not been recorded in an online charging system, thus potentially causing revenue leakage. The discovery itself can happen through different sources and to varying degrees of depth, a topic for discussion for some other time.

Let’s consider the two data sources that are primarily relevant- Network element and Charging system. The charging data have been traditionally analytics native, subject to a lot of analysis, reporting by beyond the process owners themselves. However, oftentimes, after detailed drill-down of data, the last mile root-cause analysis can occur at system level logs. These are considered semi-structured data that are traditionally not part of data warehouse, business intelligence solutions. However new-age tools (proprietary, open source) allow for analysis of semi and unstructured data.

The perfect holistic analytics solution then should be one that continuously reports gaps from structured data (XDRs) and further quickly links system level logs data to arrive at actionable root-causes. This will be enabled by the schema-less architecture that can adopt multiple data across spectrum of structure. The use cases are truly unlimited and can foster true data-driven analytics cooperation across multiple functions of operator. And what other industry to lead this than Telecom!

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Digital Business Consultant and an eternally curious soul

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