KB article
Variance Decomposition for Business Users
Variance decomposition explains changes using business‑friendly components.
arf-kbanalytical-explainabilityvariance-decompositiondriversexplanation-template
TL;DR
- Explain “what changed” in components users understand.
- Price‑volume‑mix is a common pattern.
The problem
- Business users see changes but not the underlying reasons.
- AI explanations often lack structure.
Why it matters
- Structured explanations accelerate decision‑making.
- It reduces debate about root causes.
Symptoms
- Teams disagree on why a KPI moved.
- AI produces inconsistent explanations.
Root causes
- No decomposition measures in the model.
- Drivers are not mapped to business levers.
What good looks like
- Standard decomposition measures (price, volume, mix).
- Explanations align with business levers.
How to fix
- Define decomposition logic for key KPIs.
- Store driver measures in the model.
- Use consistent explanation templates.
Pitfalls
- Over‑simplifying decomposition for complex metrics.
- Mixing multiple decompositions without clarity.
Checklist
- Decomposition measures defined.
- Drivers linked to business levers.
- Explanations validated by users.
Framework placement
Primary ARF layer: Analytical Explainability. Diagnostic bridge: semantic-reliability, execution-reliability, change-reliability.