Case Study: Seasoned optimizes data incident management and improves data quality at scale with Monte Carlo

How Seasoned Optimized the Data Incident Management Process to Improve Data Quality at Scale

Seasoned, a service industry job board and hiring app, faced challenges with managing and resolving data incidents effectively. Gaps in their alerting and triage process, combined with diffused ownership of data assets, often led to delayed responses and unresolved data quality issues. To address this, they partnered with the data observability vendor Monte Carlo to optimize their data incident management process.

Monte Carlo provided a solution centered on four key principles: establishing clear data ownership, integrating data producers into the triage process, standardizing ticketing and documentation, and treating every alert as an opportunity for process improvement. By leveraging Monte Carlo's Data Reliability Dashboard, lineage capabilities, and Notifications 2.0 feature, Seasoned decentralized incident management. This empowered their analysts as first responders, improved accountability, and streamlined alert routing. The result was a more efficient, scalable process with better visibility into data dependencies and incident resolution times.

Seasoned

Sean McAfee

Senior Ops Engineer