Overview
A major rail operator needed to consolidate operations data from multiple sources—track sensors, maintenance logs, timetables, and crew scheduling—into a single analytics platform for real-time decision support.
The Challenge
Data lived in silos: legacy mainframes, SQL Server databases, and spreadsheets. Preparing a weekly operations report took days. There was no visibility into real-time performance or predictive maintenance opportunities.
The Approach
We designed a modern data architecture using Azure Data Factory for ingestion, Azure Synapse for transformation and storage, and Power BI for reporting. The medallion architecture (bronze/silver/gold) enabled incremental loads and clear data lineage.
Implementation
Key components included:
- Bronze layer: Raw ingestion from multiple sources with change data capture
- Silver layer: Cleansed, joined datasets with business rules
- Gold layer: Pre-aggregated metrics for dashboards and ad-hoc analysis
- Power BI: Real-time dashboards with Drill-through and bookmarks
Results
- 80% reduction in report generation time (from days to hours)
- Unified real-time dashboard for operations, maintenance, and scheduling
- Predictive maintenance pilots using ML models on historical failure data
- Self-service analytics for 200+ operations staff
Key Lessons
- Start with clear use cases—don’t boil the ocean
- Data quality at source is critical; invest in profiling early
- Power BI deployment pipelines and governance prevent report sprawl
- Pilot predictive use cases before scaling