Major Telecom Provider – Real-Time Network Analytics
Outcome
20% reduction in network downtime and faster incident triage through predictive analytics.
The Problem
Millions of broadband and media devices were monitored using slow, inconsistent reporting tools. This prevented early fault detection, limited capacity planning, and increased customer-impacting outages. Engineering teams needed real-time visibility without a spike in operational overhead.
Our Solution
We designed and implemented a real-time analytics platform built around streaming telemetry, governed data models and automated orchestration. The platform enables predictive alerts, scalable ingestion and audit-ready data access.
Key actions
- Kafka-to-BigQuery streaming telemetry pipelines
- Airflow orchestration for dependencies, recovery and scheduling
- Terraform-defined infrastructure for secure provisioning
- Governed data models for network and data science teams
- Full audit logging for access and admin control
Impact
- 20% year-on-year reduction in network downtime
- Faster fault detection through proactive alerts
- Reusable data models improving onboarding and analytics speed
- Higher governance standards through enforced logging and secure access
Why It Mattered
The shift from reactive incident management to predictive operations improved customer experience and reduced operational risk – while creating a foundation for future AI-driven optimisation.
Tech Snapshot
Kafka • BigQuery • Airflow • Terraform • Python • SQL