Unified Data Platform for Global Manufacturing Company

The Challenge

A global manufacturing company with operations across 12 countries was struggling with fragmented data across multiple systems. Equipment sensor data, ERP records, quality metrics, and supply chain information lived in isolated silos, making it impossible to get a comprehensive view of operations.

Unplanned equipment downtime was costing the company millions annually, but without unified data, they couldn't implement effective predictive maintenance. Different facilities used different systems, and there was no standardized approach to data governance.

• 15+ disparate data sources across global operations
• $25M+ annual losses from unplanned equipment downtime
• No centralized view of production and quality metrics
• Manual reporting taking weeks to compile
• Inconsistent data definitions across facilities

Our Solution

We designed and built a modern lakehouse architecture on Databricks that unified all operational data into a single, governed platform. The solution includes real-time data ingestion from IoT sensors, batch processing from enterprise systems, and a semantic layer that provides consistent business definitions.

The platform enables advanced analytics including predictive maintenance models that analyze equipment sensor data to predict failures before they occur. A self-service analytics layer empowers business users while maintaining data governance and quality.

• Lakehouse architecture on Databricks with Delta Lake
• Real-time IoT data ingestion from 10,000+ sensors
• Unified data catalog with business glossary
• Predictive maintenance models for critical equipment
• Self-service analytics portal for operations teams
• Comprehensive data governance and lineage tracking

Implementation Approach

We implemented the platform using a medallion architecture (bronze, silver, gold) that progressively refined data quality while maintaining full lineage. Each facility was onboarded in waves, with standardized connectors and transformation templates.

• Phase 1: Platform foundation and core data pipelines
• Phase 2: IoT integration and real-time streaming
• Phase 3: Data governance and catalog implementation
• Phase 4: Predictive maintenance model development
• Phase 5: Self-service analytics and rollout