Three UK Transforms Network with Azure AI Analytics

The Challenge

Three UK, a leading mobile telecom operator in the UK, faced intense pressure from surging data traffic driven by 5G rollout, video streaming, online gaming, and remote work. With over 10 million customers, peak-hour congestion in urban areas led to dropped calls, buffering during streams, and high latency impacting gaming experiences. Traditional monitoring tools struggled with the volume of big data from network probes, making real-time optimization impossible and risking customer churn.[1]

Compounding this, legacy on-premises systems couldn't scale for 5G network slicing and dynamic resource allocation, resulting in inefficient spectrum use and OPEX spikes. Three UK needed a solution to predict and preempt network bottlenecks proactively, ensuring low-latency services for latency-sensitive apps while maintaining QoS across diverse traffic types.[2]

Quantitative Results

• 25% reduction in network congestion incidents
• 20% improvement in average download speeds
• 15% decrease in end-to-end latency
• 30% faster anomaly detection
• 10% OPEX savings on network ops
• Improved NPS by 12 points

Implementation Details

Implementation Overview

Three UK's implementation of Azure Operator Insights began in 2022, starting with a proof-of-concept (PoC) on select urban sites in London and Manchester. The platform was chosen for its telecom-specific pre-built ML models trained on anonymized global operator data, accelerating deployment.[1] Integration involved streaming network data via Kafka to Azure Event Hubs, processing through Synapse Analytics, and applying ML via Azure Machine Learning for predictive analytics.

Technical Architecture

The core architecture featured real-time data ingestion from eNodeB/gNodeB probes (PM/PM data), enriched with subscriber metadata. Azure Synapse handled big data pipelines, while Operator Insights dashboards provided visualizations of KPIs like RRC connection drops and throughput variance. Custom ML models forecasted traffic 15-30 minutes ahead, triggering auto-remediation via APIs to RAN controllers.[3]

Challenges during rollout included data privacy compliance (GDPR) and hybrid cloud latency. Overcome by federated learning and Azure Private Link, ensuring secure, low-latency access. Training involved labeling historical congestion events, achieving 95% model accuracy post-fine-tuning.

Phased Rollout and Training

Phase 1 (Q3 2022): PoC on 10% of network, validating 20% latency gains. Phase 2 (Q1 2023): Scaled to 50% coverage, integrating with OSS/BSS. By Q4 2023, full deployment across UK sites. Over 200 engineers trained via Azure certifications, fostering a data-driven culture.[4]

Overcoming Key Hurdles

Initial data silos were unified via a central lakehouse. Legacy tool migration used Azure Data Factory for ETL. Cost optimization via reserved instances kept TCO 40% below on-prem. Ongoing monitoring via Azure Monitor ensured 99.99% uptime.[5] This holistic approach not only resolved congestion but enabled 5G innovations like network slicing for enterprise IoT.

Current Enhancements

As of 2025, Three UK extends to GenAI for natural language queries on insights and agentic automation for self-healing networks, aligning with industry trends.[2]