Short answerTelecom operators can run churn prediction, network analytics and revenue assurance on one Databricks lakehouse, sharing customer, usage and network data under one governance model instead of separate tools. Start with one shared customer and usage model, then add churn, network and assurance use cases on top of it.
Key takeaways
- Very large event volumes fit the lakehouse.
- Shared data improves churn and assurance models.
- Care assistants can answer from the same governed data.
Telecom operators collect huge volumes of data: network events, usage records, billing, customer care and digital channels. Few use it fully.
Three use cases with clear value
- Churn prediction: combine usage, billing, complaints and network quality to predict who is likely to leave, then trigger targeted offers.
- Network analytics: stream network events into Delta tables and show quality by site and region in near real time.
- Revenue assurance: reconcile usage, rating and billing automatically to find leakage early.
Adding AI for customers and staff
With customer and product data governed in Unity Catalog, operators can build care assistants with Agent Bricks and Vector Search. Agents get answers from one place instead of searching several systems.
From telco to techco
Many operators want to offer data and AI services to their own business customers. A well-governed lakehouse is the starting point. It lets the operator package data products safely and repeat them across markets.
Our approach
We start with one high-value use case, usually churn or revenue assurance, and build the shared foundation as we go. Each new use case then reuses the same pipelines and governance.
About the author
NeuralShore Engineering, Databricks delivery team
Databricks architects, data engineers and AI engineers at NeuralShore, DLytica’s Databricks delivery company. Leadership comes from IBM data and AI architecture.