Case Study: Kargo Prevents Six-Figure Data Quality Issues with Monte Carlo

How Kargo Prevents Six-Figure Data Quality Issues With Monte Carlo

Kargo, a rapidly growing omnichannel advertising company, needed a more reliable way to manage data quality across a highly complex, acquisition-driven data stack. As the company scaled its Snowflake-centered platform, it faced extreme-volume auction and transaction data challenges, where a single pipeline failure caused by bad partner data led to a three-day outage and a $500,000 loss. To address this, Kargo turned to Monte Carlo’s data observability platform.

Monte Carlo was implemented to monitor freshness, volume, schema anomalies, and custom business rules across Kargo’s modern data stack, while also providing lineage, query change detection, correlation analysis, and Slack alerts for fast investigation. With Monte Carlo, Kargo’s developers could quickly spot and revert problematic changes, including one incident that would have cost about $20,000, and the company significantly improved ownership and reliability of its data operations.

Kargo

Andy Owens

Vice President of Analytics