Databricks Lakehouse Democratizes Data Access Across Global Tire Manufacturing Leader

Michelin, a global tire manufacturing company, pursued digital transformation by democratizing data access across its workforce. The company's strategy aimed to enable "citizen users" and distributed IT teams to develop their own use cases—from AI-powered supply chain optimization to carbon emissions reduction. However, the company's on-premises centralized data platform was rigid and fragmented, with data siloed by department, preventing employees from accessing the tools and data needed to address business challenges independently.

Michelin deployed the Databricks Data Intelligence Platform featuring Delta Lake and Databricks SQL on Microsoft Azure. The lakehouse architecture eliminated departmental data silos while preserving team autonomy, allowing employees to access data through notebooks for self-service transformations and analytics. An early use case applied machine learning to predict stock shortages 15 days in advance, enabling proactive supply chain intervention. The platform unified data sources, trained models collaboratively, and deployed them to production while Delta Lake continuously streamed real-time data and Databricks SQL enabled business analysts to query and visualize insights.

The platform's flexibility and scalability drove rapid expansion from initial pilots to hundreds of active use cases across the organization. Azure's cloud modularity provided isolated, pre-configured environments for all users, fostering adoption and collaboration. The platform now serves diverse personas—from citizen data analysts to data scientists—supporting applications ranging from routine business reporting to specialized machine learning development. This community-driven model has positioned Michelin as a data-driven organization where teams share data, code, and best practices to solve complex business challenges collectively, avoiding duplicated efforts and accelerating innovation.