Semantic Integrity
Measure Singularity: Reducing Metric Sprawl Without Losing Flexibility
Measure singularity keeps one true metric while allowing controlled variants.
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A base measure feeding multiple variants.
TL;DR
- • Create one base measure and derive variants.
- • This keeps flexibility without confusion.
The problem (layman)
- • Metric sprawl creates dozens of slightly different measures.
- • AI cannot reliably distinguish variants.
Why it matters
- • Singularity improves trust and simplifies governance.
- • It lowers maintenance cost.
Symptoms
- • Multiple “adjusted” versions of the same KPI.
- • Measures copied into different datasets.
Root causes
- • No base measure pattern.
- • Local ownership without shared standards.
What good looks like
- • Base measure + explicit variants with clear names.
- • Variants reference the base measure directly.
How to fix (steps)
- • Create base measures for key KPIs.
- • Rebuild variants as wrappers around base measures.
- • Deprecate duplicate definitions.
Pitfalls
- • Allowing variants to diverge silently.
- • Keeping unused measures “just in case.”
Checklist
- • Base measures exist for top KPIs.
- • Variants reference base measures.
- • Duplicates removed or deprecated.