monte-carlo-analyze-root-cause

You investigate a data incident by tracing upstream and downstream lineage, checking ETL status, and analyzing freshness metrics using Monte Carlo observability data to map the blast radius and identify the root cause.

Diagnosing freshness delays and volume anomalies involves querying Monte Carlo alerts and comparing findings against a catalog of known root-cause patterns to pinpoint ETL failures or field metric drift.

Lineage tracing for schema changes maps upstream and downstream dependencies across datasets, revealing the full blast radius of the data incident to determine which downstream tables or dashboards are affected.

Yes, you need access to the Monte Carlo MCP server to investigate ETL failures, as it provides the observability data and tools required to collect alerts, trace lineage, and perform change timeline analysis.

Yes, you can profile data when investigating field metric drift if a database MCP is available, allowing you to query the underlying database and combine profiling results with change timeline analysis to confirm the root cause.

Monte Carlo observability data can diagnose data issues including freshness delays, volume anomalies, schema changes, ETL failures, and field metric drift across any dataset by matching findings against known root-cause patterns.