How to validate a model against naming standards
Check a model against your naming conventions and structural rules, then correct what it finds efficiently.
When this is useful
- A model is about to become DDL and naming inconsistencies are cheapest to fix now.
- An imported legacy schema needs assessing against current standards.
- Several people have modelled parts of the same domain and the results do not match.
Before you start
- Naming standards defined for your organisation, and a model to check.
Step 1: Run the checks
Naming Standards checks names against the conventions; Validate Model checks the model's own consistency. Run both — they find different things.
Data ▸ Naming StandardsData ▸ Validate Model
Step 2: Read the health report
Health Report summarises the outcome, which is the form worth taking to a review.
Data ▸ Health Report
Step 3: Fix in bulk
Use the Bulk Editor to apply a naming correction across many objects in one pass. A convention change applied by hand across 200 tables is how inconsistency gets reintroduced.
Data ▸ Bulk Editor
What happens next
A model that passes is ready for DDL generation, and the same checks can be re-run after any significant change.
Example
An imported legacy schema scored against current standards: 340 findings, of which 280 were a single pluralisation convention corrected in one bulk edit.
Tips
- Fix the systematic findings first. The count usually collapses, and what remains is the interesting part.
- Run the check before the design review so the conversation is about the model rather than its spelling.
Limitations
- Checks report departures from declared rules; they do not rewrite the model.
- Naming standards and validation are separately enabled.