validate-data

To validate analysis claims before stakeholder reporting, review metric definitions, join logic, denominators, and time windows. Ensure conclusions are supported by evidence, check for bias risks, and verify reproducibility so stakeholders are not misled by weak data quality.

Checking SQL queries for data quality involves reconciling metrics, validating join logic, verifying denominators, and confirming time windows. This process identifies common data-quality risks that can distort results and ensures calculations are accurate before sharing.

Reviewing dashboard charts for misleading conclusions requires checking chart integrity, narrative claims, and caveats. This presentation QA ensures stakeholders are not misled by weak evidence or deceptive visuals and confirms recommendations are fully supported by evidence.

Yes, analysis validation applies to reports, notebooks, spreadsheets, SQL queries, and dashboards. It reviews methodology, checks data quality, traces evidence, and confirms whether the analysis answers the right business question before stakeholder sharing.

Methodology reviews need baseline and assumption checks to confirm the analysis answers the right business question using defensible definitions. Without validating baselines, calculations may contain bias risks and unsupported conclusions that mislead stakeholders.

Limitations when checking metric definitions include identifying unsupported claims, tracing evidence gaps, and detecting bias risks. If reproducibility fails or caveats are missing, the analysis is not ready to share and must be fixed before stakeholder presentation.