Data quality assurance mechanisms
Data reasonableness is ensured through a three-tier validation system:
- Pre-processing control::
Add VALIDATION_RULES parameter to .env.local to define business rules (e.g. "order_date >= customer_join_date") - real time calibration::
Enable the -strict-mode parameter to automatically abort generation when the percentage of anomalous data exceeds 5% - Post-check::
Use the built-in validate.py script to run SQL assertion checks (e.g. "SELECT COUNT(*) WHERE age < 0″)
Typical problems are dealt with:
- For circular references: add the -no-circular-deps flag at generation time.
- Problems with out-of-bounds values: configuring fields.price.min=0 fields.price.max=10000 constraints
- Use the -sampling-ratio=0.1 parameter to generate a small sample for validation.
The program has been tested to reduce the data logic error rate to less than 0.2%
This answer comes from the articleMetabase AI Dataset Generator: Quickly Generate Real Datasets for Demonstration and AnalysisThe
May not be reproduced without permission:AI productivity tools " How to prevent anomalous associations in the generated dataset that do not fit the business logic?
