Making Predictive Customer Support a Reality for Telcos

This post details how telcos can leverage data streaming for predictive customer support by ingesting and processing real-time data (customer behavior, network performance, subnetwork data) to train predictive models that preemptively detect and rectify anomalies. It outlines the challenges of siloed data, high data volume, disconnected teams, batch ETL, legacy technologies, and lack of scalability. Confluent's platform enables streaming data on-premises and across clouds, connecting disparate data sources with pre-built connectors, processing streams with Apache Flink to build live data products, and governing data with Stream Governance (Stream Lineage, client-side field-level encryption, RBAC, Schema Registry, Data Portal). The solution implementation includes a hybrid architecture with connectors for IBM MQ, Splunk, PostgreSQL, and SQL Server, mirroring topics to Confluent Cloud for stream processing and data product creation, and downstream enrichment for ML model training and dashboards. A Flink SQL query example is provided for identifying unhealthy network situations based on call success rates and clearing codes.