Default Aggregation: When SUM Is the Wrong Assumption

TL;DR

• SUM is not always correct.
• Set explicit aggregations or measures.

The problem

• Columns default to SUM even when the right behavior is average, max, or distinct count.
• AI queries often rely on default aggregation.

Why it matters

• Wrong aggregation leads to wrong answers with high confidence.
• Errors are subtle and often missed.

Symptoms

• Rates or percentages are summed.
• Balances are aggregated across time.

Root causes

• Default aggregation left untouched in model.
• No explicit measures for key fields.

What good looks like

• Explicit measures for critical metrics.
• Columns set to correct aggregation or “Do not summarize.”

How to fix

• Audit columns used in AI and reports.
• Define measures for rates and ratios.
• Set default aggregations in the model.

Pitfalls

• Relying on report visuals to override defaults.
• Leaving raw columns exposed for AI.

Checklist

• All ratio metrics use measures.
• Default aggregation settings reviewed.
• No critical metric depends on implicit SUM.

Framework placement

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