How to trace an AI analytics result back to data and computation

Define what reproducible means

A business reviewer needs the time window, sources, metric definition, and filters. An engineer also needs the query plan, model version, errors, and retries. SQL alone cannot explain why a particular data snapshot was used.

High-impact conclusions should include a reviewer and approval action.

Capture lineage before and after execution

Before execution record identity, project, and semantic configuration. During execution record sources, cutoff, query, and policy versions. At output record charts, reports, notifications, and sharing scope. Avoid storing unnecessary sensitive values.

A useful chain goes from chart to detail, detail to source, and source to its version.

Keep attribution and prediction bounded

Correlation is a lead, not a causal fact. Attribution needs rules, a comparison, or further validation. A forecast should state its assumptions, horizon, and uncertainty.

AskTable’s confirmed direction includes trend, anomaly, and attribution analysis; complex forecasting and cross-table diagnosis still need a real-data POC.

Use replay tests

Keep a fixed question set. After changing a metric, source, or permission, replay it and check that every changed result has an explainable cause. Versioning and review records make disputes diagnosable.