MicrosoftLogisticsPredictive MaintenanceReal-time AnalyticsAI and Machine LearningData AnalyticsData PlatformCost ReductionCustomer Experience2024MicrosoftMS#0078d4LogisticsUnited KingdomNetwork Rail needed to modernize its data analytics solution for asset management and maintenance. The company’s Intelligent Infrastructure program chose Microsoft Azure to help build several new solutions, bringing…Network Rail oversees a dizzying amount of infrastructure across England, Scotland, and Wales: 20,000 miles of…Jul 31, 20245Predictive Maintenance|Real-time AnalyticsAzure Data Factory · Azure Databricks · Azure Machine Learning02024-07-315
Network Rail needed to modernize its data analytics solution for asset management and maintenance. The company’s Intelligent Infrastructure program chose Microsoft Azure to help build several new solutions, bringing together multiple data streams for asset management. One of the tools gives maintenance engineers better insight into the 20,000 miles of railway track and countless other assets that make up the network.
↗Network Rail oversees a dizzying amount of infrastructure across England, Scotland, and Wales: 20,000 miles of railway track; 30,000 bridges, tunnels, and viaducts, many of them dating back to the Victorian era; and a vast network of signals, intersections, and stations.
↗One of the tools gives maintenance engineers better insight into the 20,000 miles of railway track and countless other assets that make up the network.
Reported impact
Business outcome
As thrilling as it has been to develop Intelligent Infrastructure, which Young says has the potential to “revolutionize the railway,” it is the response from its users that stands out most. “For me, hearing users say they want to adopt and even enhance our tools going forward is massive,” says Mason. “It’s about continuous improvement.” While Network Rail is still early on its journey, the business already has plans to extend ’s functionality moving forward.
Cost Reduction
Customer Experience
Solution
Network Rail needed to modernize its data analytics solution for asset management and maintenance. The company’s Intelligent Infrastructure program chose Microsoft Azure to help build several new solutions, bringing together multiple data streams for asset management. One of the tools gives maintenance engineers better insight into the 20,000 miles of railway track and countless other assets that make up the network. Network Rail’s Intelligent Infrastructure program worked to optimize processes by creating a Microsoft Azure–based solution, called “ insight ,” that captures the wealth of data produced by railway assets. It helps employees predict and plan maintenance, in turn saving time, improving employee and customer experiences, and making the railway a safer, more efficient operation.
Data flow
Data moves from Azure Data Factory and connected sources into Azure analytics services and Azure Databricks, where it is transformed, modeled, and stored for governed access before being served to business users through Power BI dashboards.
Solution architecture
4 components · 3 layers
Ingest
Azure Data FactoryMoves and orchestrates data from source systems into the cloud analytics environment.
Compute
Azure Machine LearningSupports model training, experimentation, and deployment for custom machine learning workloads.
Azure DatabricksProcesses and transforms large-scale data for analytics, machine learning, and operational insights.
Serving
Power BIDelivers dashboards, reports, and self-service analytics to business users.
Architecture clues
Azure Data Factory: Moves and orchestrates data from source systems into the cloud analytics environment.
Azure Databricks: Processes and transforms large-scale data for analytics, machine learning, and operational insights.
Azure Machine Learning: Supports model training, experimentation, and deployment for custom machine learning workloads.
Network Rail oversees a dizzying amount of infrastructure across England, Scotland, and Wales: 20,000 miles of railway track; 30,000 bridges, tunnels, and viaducts, many of them dating back to the Victorian era; and a va
One of the tools gives maintenance engineers better insight into the 20,000 miles of railway track and countless other assets that make up the network.
Power BI: Delivers dashboards, reports, and self-service analytics to business users.
Evidence from the source
As thrilling as it has been to develop Intelligent Infrastructure, which Young says has the potential to “revolutionize the railway,” it is the response from its users that stands out most. “For me, hearing users say they want to adopt and even enhance our tools going forward is massive,” says Mason. “It’s about continuous improvement.” While Network Rail is still early on its journey, the business already has plans to extend ’s functionality moving forward.
Network Rail needed to modernize its data analytics solution for asset management and maintenance. The company’s Intelligent Infrastructure program chose Microsoft Azure to help build several new solutions, bringing together multiple data streams for asset management.
Network Rail needed to modernize its data analytics solution for asset management and maintenance. The company’s Intelligent Infrastructure program chose Microsoft Azure to help build several new solutions, bringing together multiple data streams for asset management. One of the tools gives maintenance engineers better insight into the 20,000 miles of railway track and countless other assets that make up the network.
Network Rail’s Intelligent Infrastructure program worked to optimize processes by creating a Microsoft Azure–based solution, called “ insight ,” that captures the wealth of data produced by railway assets. It helps employees predict and plan maintenance, in turn saving time, improving employee and customer experiences, and making the railway a safer, more efficient operation.
Today, railway engineers use , built using a combination of Microsoft Azure Data Factory Power BI Azure Databricks , and Azure Machine Learning , to explore years’ worth of data to understand how assets are performing and predict when they might need to be replaced. “Before , we were unable to manage that volume of data and to produce the outputs that we do today,” recalls Mason.
Review how this reference was scored and trace each normalized claim back to its documented source.
01
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value70/100
Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth20/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact0/25
0 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability21/25
10 of 12 key fields link to a named source.
Direct evidence20/20
5 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
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