KB article
Interoperability: Aligning Power BI With the Rest of Your Stack
Interoperability ensures consistent semantics across BI, data platforms, and AI tools.
arf-kbai-readiness-interoperabilitysemantic-compatibilitycanonical-metrictooling-interfaces
TL;DR
- One semantic truth across tools.
- Align definitions and query layers.
The problem
- Different tools implement metrics differently.
- AI results differ across interfaces.
Why it matters
- Consistency across tools reduces confusion.
- Interoperability enables scalable AI adoption.
Symptoms
- SQL reports don’t match Power BI dashboards.
- AI answers differ by tool.
Root causes
- Metrics defined separately in each tool.
- No semantic governance across systems.
What good looks like
- Canonical metrics reused across tools.
- Clear authoritative layer for semantics.
How to fix
- Identify the canonical semantic layer.
- Align definitions and naming across systems.
- Test cross‑tool consistency.
Pitfalls
- Re‑implementing metrics in each tool.
- Ignoring tool‑specific differences.
Checklist
- Canonical layer identified.
- Cross‑tool metric alignment tested.
- Governance applied across stack.
Framework placement
Primary ARF layer: AI Readiness & Interoperability. Diagnostic bridge: data-movement-reliability, semantic-reliability, execution-reliability, change-reliability.