AI Readiness & Interoperability
Tooling Interfaces: SQL, DAX, and the Translation Layer
Different tools expose different query layers; AI must align with them.
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SQL and DAX feed a semantic layer for consistent answers.
TL;DR
- • AI answers depend on the query layer.
- • Translation layers must preserve meaning.
The problem (layman)
- • SQL and DAX can yield different results if semantics differ.
- • AI might query the wrong layer.
Why it matters
- • Cross‑tool consistency is required for trust.
- • Interoperability reduces maintenance cost.
Symptoms
- • Results differ between SQL queries and Power BI visuals.
- • AI answers are inconsistent across tools.
Root causes
- • Semantic logic only exists in DAX.
- • Translation between layers is unclear.
What good looks like
- • Semantic logic shared or mirrored across layers.
- • Clear documentation of which layer is authoritative.
How to fix (steps)
- • Identify canonical layer for each metric.
- • Document translation rules.
- • Validate consistency across tools.
Pitfalls
- • Assuming SQL and DAX are interchangeable.
- • Ignoring semantic differences in calculations.
Checklist
- • Authoritative layer defined.
- • Translation rules documented.
- • Cross‑tool tests pass.