Related Skills

Use $analyze-data-quality when validation depends on whether the underlying data is trustworthy, comparable, fresh, or at the right grain.

Use $product-business-analysis when the task asks for a recommendation or decision after the validation pass.

Validate Data Analysis

Validate an analysis before it is shared with stakeholders. Focus on whether the question, data, methodology, calculations, visuals, claims, caveats, and recommendations are trustworthy enough for the stated audience and decision. This skill is for analysis QA, not raw dataset profiling alone. When validation depends on dataset reliability checks such as freshness, grain, missingness, duplicates, join coverage, or source mismatches, use $analyze-data-quality as a companion.

Workflow

1. Inventory the artifact and claims.

   Identify the report, notebook, spreadsheet, SQL, dashboard, chart, pasted analysis, or recommendation being validated. Inspect source artifacts when a path, link, query, notebook, spreadsheet, or dashboard is referenced. Extract the main question, audience, decision, key claims, headline numbers, data sources, time windows, populations, filters, comparison baselines, and stated caveats. Verify that every metric or KPI requested by the user appears in the analysis or is explicitly marked unavailable, not applicable, or out of scope.

2. Validate the question, methodology, and assumptions.