Dimensional Grain: Preventing Apples-to-Oranges Comparisons

TL;DR

• Grain must be explicit for facts and measures.
• Incorrect grain leads to false comparisons.

The problem

• Metrics are compared across tables with different levels of detail.
• AI blends values that should never be compared.

Why it matters

• Incorrect grain produces misleading insights.
• AI explanations become unreliable.

Symptoms

• Totals don’t reconcile across reports.
• AI compares customer‑level metrics to order‑level metrics.

Root causes

• Grain not documented in metadata.
• Fact tables mixed in the same analysis without proper alignment.

What good looks like

• Grain documented for each fact table and KPI.
• Measures explicitly handle grain alignment.

How to fix

• Document grain in table and measure descriptions.
• Use bridge tables or aggregation logic to align grain.
• Add tests that validate grain assumptions.

Pitfalls

• Assuming the visualization implies the grain.
• Mixing fact tables in measures without explicit joins.

Checklist

• Fact table grain documented.
• Measures specify grain assumptions.
• Cross‑grain comparisons are controlled.

Framework placement

Primary ARF layer: Semantic Integrity. Diagnostic bridge: semantic-reliability, change-reliability.