Case Study: Roche builds a self-service data mesh with Monte Carlo

Using DataOps To Build Data Products and Data Mesh

Roche, one of the world’s largest biotech companies, needed to modernize its legacy on-prem data environment, which was hard to scale and prone to quality issues. Using Monte Carlo for data observability as part of a broader DataOps and data mesh initiative, Roche set out to improve reliability, speed, and self-service access to trusted data products.

Monte Carlo helped Roche monitor data health across freshness, volume, schema, and quality, with results fed into their orchestration and cataloging stack. Combined with cloud migration and modern data platform tooling, this enabled Roche to build a composable data mesh with automated SLIs and governance, ultimately growing from zero to 40+ data products in two years and reducing major data quality incidents while dramatically improving delivery speed.

Roche