Formation Big Data Analytics for Telecom Regulators

Code formation

BDATR

Durée

14 heures (généralement 2 jours pauses comprises)

Pré requis

There are no specific requirements needed to attend this course.

Aperçu

Pour satisfaire à la conformité des régulateurs, les CSP (fournisseurs de services de Communication ) peuvent exploiter Big Data Analytics, qui les aide non seulement à respecter la conformité, mais dans le cadre du même projet, ils peuvent augmenter la satisfaction de la clientèle et ainsi réduire le taux de désabonnement. En fait, puisque la conformité est liée à la qualité de service liée à un contrat, toute initiative visant à respecter la conformité améliorera «l’avantage concurrentiel» des DSP. Par conséquent, il est important que les régulateurs soient en mesure de conseiller / orienter un ensemble de pratiques analytiques Big Data pour les DSP, qui apporteront un bénéfice mutuel entre les régulateurs et les DSP.

Le cours comprend 8 modules (4 le jour 1 et 4 le jour 2)

Machine Translated

Plan du cours

1. Module-1 : Case studies of how Telecom Regulators have used Big Data Analytics for imposing compliance :

  • TRAI ( Telecom Regulatory Authority of India)
  • Turkish Telecom regulator : Telekomünikasyon Kurumu
  • FCC -Federal Communication Commission
  • BTRC – Bangladesh Telecommunication Regulatory Authority

2. Module-2 : Reviewing Millions of contract between CSPs and its users using unstructured Big data analytics

  • Elements of NLP ( Natural Language Processing )
  • Extracting SLA ( service level agreements ) from millions of Contracts
  • Some of the known open source and licensed tool for Contract analysis ( eBravia, IBM Watson, KIRA)
  • Automatic discovery of contract and conflict from Unstructured data analysis

3. Module -3 : Extracting Structured information from unstructured Customer Contract and map them to Quality of Service obtained from IPDR data & Crowd Sourced app data. Metric for Compliance. Automatic detection of compliance violations.

4. Module- 4 : USING app approach to collect compliance and QoS data- release a free regulatory mobile app to the users to track & Analyze automatically. In this approach regulatory authority will be releasing free app and distribute among the users-and the app will be collecting data on QoS/Spams etc and report it back in analytic dashboard form :

  • Intelligent spam detection engine (for SMS only) to assist the subscriber in reporting
  • Crowdsourcing of data about offending messages and calls to speed up detection of unregistered telemarketers
  • Updates about action taken on complaints within the App
  • Automatic reporting of voice call quality ( call drop, one way connection) for those who will have the regulatory app installed
  • Automatic reporting of Data Speed

5. Module-5 : Processing of regulatory app data for automatic alarm system generation (alarms will be generated and emailed/sms to stake holders automatically) :
Implementation of dashboard and alarm service

  • Microsoft Azure based dashboard and SNS alarm service
  • AWS Lambda Service based Dashboard and alarming
  • AWS/Microsoft Analytic suite to crunch the data for Alarm generation
  • Alarm generation rules

6. Module-6 : Use IPDR data for QoS and Compliance-IPDR Big data analytics:

  • Metered billing by service and subscriber usage
  • Network capacity analysis and planning
  • Edge resource management
  • Network inventory and asset management
  • Service-level objective (SLO) monitoring for business services
  • Quality of experience (QOE) monitoring
  • Call Drops
  • Service optimization and product development analytics

7. Module-7 : Customer Service Experience & Big Data approach to CSP CRM :

  • Compliance on Refund policies
  • Subscription fees
  • Meeting SLA and Subscription discount
  • Automatic detection of not meeting SLAs

8. Module-8 : Big Data ETL for integrating different QoS data source and combine to a single dashboard alarm based analytics:

  • Using a PAAS Cloud like AWS Lambda, Microsoft Azure
  • Using a Hybrid cloud approach

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