Case Study: Johnson & Johnson achieves faster, lower-cost supply chain analytics with Databricks

How Johnson & Johnson Leverages the Databricks Lakehouse

Johnson & Johnson, the global consumer goods and pharmaceutical company, faced major supply chain data challenges from fragmented systems, manual data extraction, and a legacy Hadoop environment that could not scale to meet near-real-time service levels. The lack of a unified data layer slowed insights, limited flexibility, and made it harder to optimize inventory, pricing, logistics, and procurement decisions.

Johnson & Johnson worked with Databricks and the Databricks Data Intelligence Platform on Azure to build a common data ingestion layer, unify 35+ data sources, and run analytics and machine learning through Delta Lake, Photon, and Databricks SQL. With Databricks, the company reduced data engineering costs by 45-50% and cut data delivery lag from about 24 hours to under 10 minutes, while improving forecasting, supply chain visibility, and patient therapy tracking.

Johnson & Johnson

Mrunal Saraiya

Sr. Director - Advance Technologies