Semantic Integrity
Calculation Groups Without Chaos
Calculation groups can simplify models, but they need clear rules and naming.
Rendering diagram...
A measure is transformed by a calculation group, then labeled.
TL;DR
- • Calculation groups should be predictable and documented.
- • Unclear groups lead to confusing AI results.
The problem (layman)
- • Calculation groups apply transformations across measures without clear visibility.
- • AI may not know which calculation group is active.
Why it matters
- • Hidden transformations can change metric meaning.
- • Explainability suffers when calculation context is unclear.
Symptoms
- • Users are surprised by time‑shifted results.
- • AI returns answers that don’t match report views.
Root causes
- • No standard naming or documentation for groups.
- • Multiple groups that overlap or conflict.
What good looks like
- • Calculation groups are limited and documented.
- • Active group is always visible in outputs.
How to fix (steps)
- • Audit calculation groups and remove unused ones.
- • Document each group’s effect in metadata.
- • Expose active group in AI responses.
Pitfalls
- • Using groups as a shortcut for missing base measures.
- • Stacking multiple groups without validation.
Checklist
- • Calculation groups documented.
- • Active group visible in outputs.
- • No conflicting group definitions.