1. / Increased data availability for America’s largest telecom network provider resulted in a 35% reduction in data quality issues
Overview

An American telecommunications company that provides wireless services, is an internet service provider, and is the fourth-largest mobile network operator in the United States wanted to monitor the performance of the telecom network in near real time. Our solution was to develop an End-to-End – Full-Scope EDW-BI application that included Data Modeling, ETL, Cube, and Reporting framework, with a centralized data store as a single source of truth, resulting in improved operational performance and faster data processing.

Problem

Aside from the challenges associated with BI functional areas as data was scattered across different LOBs, Apexon specialists discovered that:

  • The client had no Centralized Data store to house customers service tenure details
  • There was no collection of Switch-Network logs (provided by vendors such as Nortel, Motorola, Samsung, and Lucent) and no scope for real-time network-cluster performance.
  • PLSQL modules were used to design data aggregation in certain LOBs. With the increase in data volume, the business was concerned about job execution time and frequent failures caused by in-memory processing.
  • New demographic services data was required to be collected and maintained in a resource-storage and cost-effective manner to cater to certain analytics requirements
Solution

We implemented the following solution to meet the customer’s requirement of monitoring performance in near real time.

  • End-to-end design of a full-stack EDW-BI application, including data modelling, ETL, cubes, and reporting framework
  • The data cycle extracts data from binary files in mediation servers and checks for data gaps and quality in the multi-threaded mediation and ETL stages.
  • Various Cubes were also designed and built with appropriate partitioning, aggregation, and caching strategies to perform capacity, performance, and network stats trending.
  • Building end to end ETL solutioning ingesting data into Hadoop as well as landing TD tables, Ingesting into Splunk and building Splunk dashboards and maintaining certain customer & demographic services data into SNOWFLAKE cloud dw, building snowsql reports and chartio dashboard.
Impact

At the close of the project, Apexon was able to deliver the following features and upgrades:

  • The new system handles 1.9 PB of data enabling seamless analytical processing on it
  • Due to Realtime Network-Cluster analysis, service downtime was reduced drastically
  • Improved service-network performance CSAT by 14.29%
  • New centralized datastore enabled business to easily get 56+ report/dashboards
  • Due to improvised business analytics, business rules could be modified and implemented seamlessly, improving Gross Profit by 5%
  • Resulted in 35% Reduction in Data Quality Issues
  • 20% Increase in Governance and Regulatory Compliances

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