validate-data

Data validation for stakeholder reporting involves reviewing methodology, data quality, calculation logic, and potential bias to catch issues before sharing. It produces a structured validation report with severity levels, readiness assessment, and actionable improvements.

Reviewing SQL queries for calculation logic errors involves verifying aggregation grain, denominator correctness, date alignment, and join correctness to prevent analytical traps like join explosion or denominator shifting. This ensures metric alignment and accurate subtotals in your analysis.

Statistical bias checking in analysis QA is the process of flagging common analytical traps like survivorship bias, selection bias, average-of-averages, and timezone mismatches. It assesses visualizations and conclusion support to ensure your analysis is correct and interpretable.

Yes, automated validation applies to stakeholder-ready charts, narrative explanations, SQL queries, and reports. It systematically checks methodology, data quality, calculation integrity, and presentation quality to produce a structured readiness assessment.

The best way to catch analytical pitfalls before sharing an analysis is applying a pre-delivery QA checklist that flags join explosion, survivorship bias, denominator shifting, and average-of-averages. This ensures visualization accuracy and conclusion support with specific improvement recommendations.