AI Readiness & Interoperability
Interoperability: Aligning Power BI With the Rest of Your Stack
Interoperability ensures consistent semantics across BI, data platforms, and AI tools.
TL;DR
- • One semantic truth across tools.
- • Align definitions and query layers.
The problem (layman)
- • 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 (steps)
- • 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.