Bayer Consumer Health scales global self-service analytics with Unity Catalog

2026-03-04 · Source: Databricks · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Bayer Consumer Health selected Databricks, enhanced with Azure Services, as its unified data platform to transform raw data into reusable, quality-checked assets and enable Azure ML/AI services for developers. They leveraged Databricks to create "template-based environments" with dedicated service instances, ensuring resource isolation and lifecycle management for multiple teams working in parallel and avoiding "data tourism." The introduction of Unity Catalog replaced their Hive Metastore, providing centralized governance, enabling a "pull-based" data-sharing approach, and facilitating secure connectivity from development to production core data assets. This shift, combined with serverless capabilities, significantly accelerated data product implementation and time-to-market for analytics solutions by allowing engineers to use production-grade data for testing. Ultimately, Databricks and Unity Catalog established shared standards for data access and security, fostering self-service analytics and a data-driven organization by eliminating silos and inconsistent definitions.

Key takeaway

Bayer Consumer Health leveraged Databricks and Unity Catalog to establish a unified, governed data platform, overcoming data silos and accelerating data product development. This enabled template-based environments with dedicated resource isolation and centralized governance, facilitating secure, pull-based data sharing from development to production. The approach significantly improved time-to-market for analytics solutions and scaled self-service reporting by providing production-grade data for testing.

Topics

• Databricks Platform
• Unity Catalog
• Data Governance
• Self-Service Analytics
• Azure ML

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Data Engineer, MLOps Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.