metric-diagnostics

To diagnose metric changes, reproduce the metric across contexts, validate driving factors using multi-source evidence, and generate calibrated explanations for your reports.

Identify driving factors by performing driver decomposition on the time-series metric, verifying consistency through multi-source data reads, and validating likely drivers against live evidence.

Generate shareable reports for anomaly investigations by producing calibrated explanations and artifacts that summarize the diagnosed metric movements, gaps, and discrepancies.

Use driver decomposition for data reconciliation when you need to understand what drove a metric movement, validate likely drivers across multi-source data, and calibrate the explanation.

Yes, diagnosing gaps and discrepancies is applicable across analytics workflows, reproducing metric behavior to verify consistency and validate driving factors using multi-source evidence.

Yes, you can validate driving factors by executing multi-source data verification, reproducing the metric behavior, and generating calibrated explanations for shareable dashboards.