Metric Reconciliation

When to use

• Two dashboards or reports show different values for the same KPI
• A metric changed unexpectedly after a data pipeline update
• Stakeholders question a number and need an authoritative explanation
• Preparing to merge or deprecate a legacy reporting source
• Onboarding analysts to a new data model and validating it against the old one

Process

1. Define the metric and scope — confirm the exact definition (numerator, denominator, filters, time zone) and the period under investigation. Mismatched definitions are the most common cause of discrepancy.
2. Pull values from both sources — extract the metric values for the same period from each source. Record absolute values, row counts, and the query or calculation path used.
3. Compute the gap — calculate the absolute difference and percentage gap. If the gap is within an agreed tolerance (e.g., ±0.1%), document it as accepted and close. See references/reconciliation_patterns.md for tolerance guidelines.
4. Trace the computation path — walk each source's query or pipeline step by step. Common divergence points: different join types, filter order, null handling, date truncation, or deduplication logic.
5. Identify the root cause — classify the cause using references/metric_discrepancy_guide.md (definition mismatch, data freshness, aggregation grain, calculation bug). Document the divergence point with a code snippet or query excerpt.
6. Resolve and document — fix the calculation or accept a canonical source, then complete assets/reconciliation_report_template.md and share with stakeholders.