metric-diagnostics

To find the root cause of unexpected metric movements, you can decompose the changes by segment and mix shifts. This validates data quality and identifies the specific drivers causing performance spikes or regressions.

Investigating anomalies and validating data quality requires reproducing metric definitions and aggregating source data. This structured diagnostic workflow rules out measurement errors before analyzing actual performance drivers.

Decomposing metric changes quantifies segment contributions by breaking down movements into within-segment performance shifts and mix changes. This process isolates whether specific business segments or broad factors drive the variance.

You can reconcile conflicting metric definitions across data sources by querying connected structured data and semantic layers. The workflow cross-references definitions to calibrate explanations and resolve discrepancies.

You need connected structured data sources and a semantic layer to diagnose gaps between actual and expected values. Access to business context is required to reproduce metrics, run live queries, and produce calibrated explanations.

Automated driver decomposition for anomaly detection is not suitable when your underlying structured data sources lack a connected semantic layer. Accurate root cause analysis requires accessible business context to reproduce metric logic.