Roche scaled its data mesh across 30+ domains with Monte Carlo observability and cut Snowflake credit costs by 20%.

Roche needed visibility into data reliability across distributed teams managing thousands of tables. It deployed Monte Carlo for anomaly detection, lineage, SLOs and data product monitoring. The page reports a 20% drop in Snowflake credit cost, 300+ data products delivering impact, and a 50%+ volume spike caught within 24 hours.

Monte Carlo is a data observability platform, used by pharma and healthcare data teams to detect pipeline and data quality problems before they reach users

This sits in the Scale & Govern stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to monitor, govern and keep validating an AI system once it is already live in production.