The hard part of enterprise data Q&A is not only generating SQL. It is identifying the metric definition, checking completeness, handling permissions and explaining the result. A data agent that returns one number is limited; one that shows source, definition, anomalies and next validation step is closer to a workflow.
1. The short answer
The same term can represent different definitions across departments. Revenue, active users, conversion and inventory require time ranges, filters and exclusions. The agent should turn a question into a confirmable analysis plan before querying instead of choosing a plausible field silently.
2. The context
Context includes metric definitions, freshness, owners, permissions and known anomalies, not only table schemas. Every result should state query time and data scope. If a source is delayed or incomplete, expose the limitation instead of hiding uncertainty in fluent text.
3. The real trade-offs
Enforce permissions in the database or semantic layer. A model should not bypass row or column rules through guessed table names, generated SQL or cached results. Mask sensitive fields, return aggregates where appropriate and log every request for review.
4. How to put it into practice
Decision preparation requires comparisons, trends and anomaly explanations, but the agent should not make the final business decision. A useful output lists influential changes, assumptions to verify and suggested actions while leaving execution to a person or controlled tool.
5. Common failure modes
A chat-style BI surface without metric governance is fragile. So is a specific-looking answer that hides SQL, source and definition. Explainability is not decoration; it is a condition for adoption in high-impact decisions.
6. FDE perspective
The product boundary should be preparing a better decision, not deciding for the company. The closer a workflow gets to budgets, customers, pricing or performance, the more it needs definitions, evidence and approval.
A practical checklist before shipping or buying
- Confirm metric definitions before querying.
- Show scope, freshness and source with every result.
- Enforce access at the data layer.
- Keep high-impact decisions advisory and approved.
What to do next
Start with one workflow whose metric definition and permissions are clear, then produce a sourced report.
This article is based on public documentation and research from NIST AI Risk Management Framework. The FDE perspective is editorial analysis, not a quotation from the source.
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