Case Study: Abacus Medicine Achieves Reliable Cloud Data Quality with Monte Carlo

How Abacus Medicine Built a Modern Data and AI Stack with Databricks and Monte Carlo

Abacus Medicine, a Copenhagen-based pharmaceutical company, needed a more scalable way to manage data quality across a fragmented, on-premises data landscape. As the business grew and data became more critical for pricing, trading, sourcing, and supply chain decisions, the team struggled with limited visibility, slow incident detection, and a testing-heavy approach that was too time-consuming to maintain.

To modernize its stack, Abacus Medicine moved to Databricks in the cloud and implemented Monte Carlo for data observability, using automated monitoring, alerting, and root cause analysis to catch issues earlier. The result was faster detection and resolution of data incidents, more reliable self-serve analytics, less time spent on troubleshooting, and near-immediate time to value—Monte Carlo was deployed in days, with the team citing an expected build time of months versus two to four weeks for a bought solution.

Abacus Medicine

Malte Olsen

Head of Business Analytics