Building a Unified Enterprise Data Platform
Achieved 40% reduction in operational costs and enabled predictive maintenance capabilities that prevented $30M in equipment downtime.
The Challenge
The client operated 50+ manufacturing facilities across 40 countries, each with its own set of systems and data formats. Critical operational data was trapped in silos, making it impossible to gain a holistic view of performance or predict equipment failures.
Our Approach
We employed our Strategineering methodology to first map the entire data landscape, identify high-value use cases, and design a modern architecture that could scale globally while respecting regional requirements.
The Solution
We implemented a cloud-native data platform built on Snowflake and Databricks, with real-time streaming from Kafka. Our dbt-based transformation layer enabled self-service analytics while maintaining governance.
The Results
Within 12 weeks of go-live, the platform was delivering actionable insights. Predictive maintenance algorithms have since prevented over $30M in equipment downtime, while operational visibility enabled 40% cost reductions.
Technologies Used
We selected these technologies based on the client's specific requirements, existing infrastructure, and long-term sustainability considerations.
DataVine transformed how we see our business. We went from flying blind to having real-time visibility across our entire global operation.
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