THE DATA EXPRESS: BREAKING THE SILOS

Porterbrook is a leading UK company that leases around one third of the country’s passenger trains and carriages to rail operators. Since 1996, it has invested more than £1.5 billion in new trains and also works with major freight companies. This case study shows how Porterbrook used data to boost fleet performance, make maintenance more efficient, and transform its operations digitally.

DISCONNECTED TRACKS

Porterbrook’s rolling stock was generating a goldmine of sensor data every day, monitoring everything from temperature and vibration to braking patterns and energy consumption. But instead of flowing into a single stream, these rich insights were stored across disconnected systems, with minimal integration between the train telemetry, rail infrastructure data, external weather feeds, and operational records.

Without a centralised platform to bring it all together, Porterbrook struggled to correlate datasets or perform real-time analysis at scale. This fragmentation prevented predictive upkeep, and operations remained stuck in reactive mode. To add to the list of frustrations, existing infrastructure lacked elasticity: it could not scale or adapt as new data sources were introduced, restricting Porterbrook’s digital ambition to transform asset management across its rail portfolio.

BUILDING THE DATA RAILWAY

Elastacloud, part of the Acora Group, stepped in to design and execute a modern data architecture on Azure. The first sprint was migrating all sensor streams, rail network data and environmental feeds into a consolidated Azure Data Lake.

With the network data now singing from the same hymn sheet, Elastacloud deployed containerised microservices to streamline and coordinate data flows, bringing modularity and agility to the ecosystem. Azure services were fine-tuned to handle high-throughput ingestion, low-latency storage, and advanced analytics. Real-time pipelines were established to process rolling stock telemetry, infrastructure signals, and external inputs as they arrived.

To surface these insights, a custom web portal was carefully developed for Porterbrook’s customers, visualising performance indicators, flagging maintenance needs ahead of time, and offering a clear view into fleet health, all powered by AI-driven predictive maintenance models.

FULL STEAM AHEAD: INSIGHTS IN MOTION

Porterbrook’s unified data platform now turns massive datasets into real-time insights, spotting issues early, streamlining maintenance, and keeping performance on track. Predictive maintenance, powered by AI, is fully up and running, helping forecast failures, schedule repairs, and cut costs. All resulting in assets lasting longer, downtime drops, and operations running smoothly.

This data-driven leap has laid the foundation for a smarter, more resilient rail network, showcasing how a Data & AI strategy done properly can empower businesses with clarity and control.