The tool's unique AI rule generation and Faker data filling dual-engine architecture effectively solves the problem of the lack of business logic in purely random data.GPT-4o first builds a data model based on the user's description, for example, "cross-border e-commerce orders" will automatically include exchange rate conversion fields; Faker fills in specific values according to the rules of regionalization, such as U.S. address generation with state abbreviations, Chinese mobile phone number to meet the specifications of the number field. Faker then fills in specific values according to the rules of localization, such as the U.S. address generation including state abbreviations, China's cell phone number in line with the specification of the number. Quality assurance mechanisms include:
- integrity check: Required fields are automatically completed, e.g., medical data to ensure that the diagnosis code matches the ICD criteria.
- Consistency maintenance: Foreign key associations 100% correct in multi-table data, eliminating orphan records
- Semantic Compliance: Generated annual CEO salary automatically matches industry benchmarks and avoids common sense errors
In a financial stress test scenario, the generator creates a credit dataset containing 200 correlation tables, successfully reproducing the asset correlation rupture characteristics in the 2008 crisis, and verifying the business reference value of the data.
This answer comes from the articleMetabase AI Dataset Generator: Quickly Generate Real Datasets for Demonstration and AnalysisThe
