data-analytics-report-qalisted

# Report QA before publication ## Purpose The expensive data failures are not wrong queries; they are silent ones. A pipeline stopped three days ago and the dashboard shows a plausible flat line. A join dropped 4% of rows and the total still looks reasonable. A segment filter excluded the largest region and nobody noticed because nobody knew what the total should be. This skill is the fixed set of checks that catch silent failure before a decision is made on the number, and the honest statement of limits that goes out with it. ## Prerequisites - **Inputs:** the report or figures in final form; the query, model, or extract behind them; the metric definitions in use; at least one independent source to reconcile against; the prior period's published figures. - **Runtime:** ability to re-run the underlying query or inspect the pipeline's last successful run. A QA pass done by reading the output alone catches formatting errors and nothing else. If there is no independent source to reconcile against, say so explicitly in the output rather than skipping the check silently — unreconciled is a materially different claim from reconciled. ## Procedure Run all seven. They are ordered so the cheap checks that invalidate everything come first. 1. **Freshness.** What is the maximum timestamp in the underlying data, and when did the pipeline last complete successfully? Compare against the expected cadence. Stale data that renders without error is the most common silent