Telekom Malaysia Berhad (TM) is nation’s leading integrated telecommunications provider that is on a continuous journey of enhancing and elevating the nation’s telecommunications technology and services toward delivering connectivity to all Malaysians. They approached Blazeclan for help in building a reporting solution that performs sentiment analysis on Twitter and community forum data in near real-time.
TM was looking into a comprehensive solution to enable near real-time & better visibility of TV viewership in view of their ever-increasing data. By understanding the customers’ viewing behavior, TM intended to make data-driven decisions to grow their business.
Key Analysis Areas Included
- Actual user engagement at a particular hour which can be drilled down to respective channels.
- Visibility of new or repetitive users based on daily interval.
- Time vs. day user engagement to understand user behavior & identify peak viewing hour/day.
- User engagement duration with a particular channel (average time spent)
TM partnered with Blazeclan in developing a Near Real-time Viewership Dashboard. Blazeclan came up with a solution leveraging AWS Glue & AWS Lambda for processing the voluminous data. The approach involved the use of multi-node AWS Redshift cluster. This cluster brought scalability & efficiency in query resolutions for TM. For visualization purposes, AWS QuickSight was used in combination with SPICE for a rapid, real-time, in-memory calculation & analysis. This was engineered for rapid, advanced calculations & providing near real-time data for the dashboard.
- AWS infrastructure implementation.
- Data ingestion and data preparation.
- Dashboard development.
- Data warehousing.
- Iteration, evaluation, and streamlining.
Benefits Achieved by TM
Actionable Insights: The cloud-based data pipeline and BI dashboard enabled TM to get actionable insights on their user viewing patterns and trends for making better decisions and bringing overall improvement in end-user experience.
Serverless Architecture: TM was offered with a serverless architecture that enabled greater scalability, high flexibility, and faster time-to-release. They were able to achieve all this while maintaining cost-effectiveness.
High Performance: The solution offered TM with the ability to perform complex calculations at scale. This was done by using an in-memory engine, SPICE, for faster load times and automated, periodic data refresh.
|Amazon S3||AWS Athena||AWS Glue|
|Amazon QuickSight||AWS Lambda||Amazon CloudWatch|
|Secrets Manager||Amazon Redshift|