Case Study: Medtronic achieves scalable management of machine-generated manufacturing data with MongoDB

How Medtronic Manages Machine Data in MongoDB

Medtronic, a global medical-device maker serving millions of patients and known for products like pacemakers, faced a growing data challenge: machines across its manufacturing process generate huge volumes and a wide variety of device and component data (more than 30 million samples per day), 40% of stored data was collected in the past two years, and regulatory rules require retention of device data for decades. That diversity and legacy storage across many obscure systems made analysis and regulatory retrieval slow and complex.

To address this, Medtronic piloted MongoDB by loading component data from a battery electrolyte-fill step, then expanded plans to ingest all component manufacturing data and build device-level views. MongoDB’s flexible document model eliminated costly joins, enabled simple, fast queries to retrieve complete component histories, and made cross-component analysis and scaling much easier—streamlining analytics, simplifying long-term data management, and improving the company’s ability to meet regulatory and operational needs.

Medtronic

Matthew Chimento

Principal test Engineer and Project Manager