Case Study: SeatGeek reduces data incidents to zero with Monte Carlo data observability

How SeatGeek Reduced Data Incidents to Zero with Data Observability

SeatGeek, the mobile ticketing marketplace, was struggling with recurring data downtime and hard-to-diagnose anomalies that were often first spotted by business users in BI reports. To reduce the time spent root-causing issues and improve trust in its internal data, SeatGeek turned to Monte Carlo’s data observability platform.

Using Monte Carlo’s ML-enabled anomaly detection, field-level lineage, automated alerts, and incident tracking, SeatGeek could identify and trace issues faster across its data stack, including third-party sources. The result was a drop in data incidents from 10 per month to 0 in the quarter after implementation, a 50% reduction in root-cause analysis effort, improved ELT stability, and significantly faster time-to-discovery.

SeatGeek

Brian London

Director of Data Engineering