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TRANSMISSION TECHNOLOGIES

Big Data Analytics for Telecom Regulators

Course Overview:

To meet compliance requirements of telecommunications regulators, CSPs ( Communication service providers) can tap into Big Data Analytics which will not only help them meet compliance requirements but also increase customer satisfaction and hence reduce churn.

Since compliance is related to Quality of Service that is spelled out in a contract, any initiative towards meeting the compliance will improve the “competitive edge” of the CSPs. Therefore, it is important for Regulators to be able to advise/guide CSPs on a set of Big Data analytic practice that will be of mutual benefit to both the regulators and the CSPs.

Course Duration:

2 days of course : 8 modules, 2 hours each.

Course Outlines:

  • Case studies on how regional Telecom Regulators use Big Data Analytics for imposing compliance
  • Reviewing Millions of contract between CSPs and their users using unstructured Big Data Analytics
  • Extracting Structured information from unstructured Customer Contract and map them to Quality of Service obtained from IPDR (in short for Internet Protocol Detail Record) data & Crowd Sourced App data. Metric for Compliance. Automatic detection of compliance violations
  • USING App approach to collect compliance and QoS data- release a free regulatory mobile App to the users to track and analyze automatically. In this approach, regulatory authority will release free App and distribute it among the users. The App will collect data on QoS/Spams etc and report it back in analytic dashboard form
  • Processing of regulatory App data for automatic alarm system generation (alarms will be generated and emailed/sms to stake holders automatically)
  • Use IPDR data for QoS and Compliance-IPDR Big data analytics
  • Customer Service Experience & Big Data approach to CSP CRM
  • Big Data ETL (in short for Extract, Transform, Load) for integrating different QoS data source and combine to a single dashboard alarm based analytics
Summary
Article Name
Introduction to Big Data and Data Analytics
Description
Main characteristics of Big Data-volume, variety, velocity and veracity; Data Warehouses – static schema, slowly evolving dataset Hadoop/Spark Based Solutions – no conditions on structure of dataset.
Author

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