Semantic Integrity
Naming Measures So Humans and AI Agree
Consistent naming helps AI select the right measure and reduces ambiguity.
TL;DR
- • Names are instructions for AI.
- • Explicit names reduce accidental misuse.
The problem (layman)
- • Ambiguous measure names cause AI to pick the wrong calculation.
- • Similar names hide different definitions.
Why it matters
- • AI relies on labels to choose measures when context is unclear.
- • Good names reduce the need for custom prompts.
Symptoms
- • Measures named “Revenue,” “Revenue1,” “Revenue_Adj.”
- • No indication of currency, time window, or exclusions.
Root causes
- • No naming standard.
- • Legacy measures created by different teams.
What good looks like
- • Names encode metric scope, unit, and time basis.
- • Deprecated measures are clearly labeled.
How to fix (steps)
- • Adopt a naming convention (Metric | Scope | Unit).
- • Rename measures and update report references.
- • Add descriptions that expand on the name.
Pitfalls
- • Renaming without updating dependent measures.
- • Over‑compressing meaning into acronyms.
Checklist
- • Naming standard documented and enforced.
- • All key measures renamed to reflect scope and unit.
- • Descriptions mirror the naming logic.