RESOURCE CENTER
Evaluate AI data analysis with evidence—not slogans.
Use practical guidance for natural-language analytics, database compatibility, ERP-derived data paths, least-privilege access, deployment, and answer validation.
EVALUATION MAP
Start with the question. Verify the boundary.
- 01Define the use case and accountable decision owner
- 02Map approved sources, permissions, and processors
- 03Compare results with an authorized reference
Evidence-led guidance for an accountable review.
RESOURCE CENTER
Choose the review path that matches your question.
Move from product fit to technical validation with bounded, buyer-oriented guidance.
AI Data Analyst
Define the user, question, answer, evidence, and decision boundary.
Natural Language to SQL
Evaluate query generation with representative schemas and reference reports.
Database Compatibility
Separate supported, qualified, review, beta, and coming-soon paths.
Security & Deployment
Map processors, permissions, retention, support access, and ownership.
Output Validation
Compare consequential answers with authorized evidence and owner sign-off.
EVALUATION FAQ
Questions to answer before adopting an AI data analyst
Use these answers to structure evaluation—not as a substitute for testing your own sources, permissions, and reference reports.
CONTINUE THE EVALUATION
Move from category research to technical evidence.
Review source compatibility, deployment boundaries, database permissions, and validation requirements before selecting a production path.