X-Data Standard uses a large model and semantic analysis to identify core fields in selected data assets. This feature extracts lookup table definitions, recommends standard mappings, and intelligently generates data standard definitions.
Prerequisites
To use X-Data Standard, you must purchase both the Data Standard and the X-Data Standard function plans.
You have configured and enabled X-Data Standard. For more information, see AI Assistant.
Permissions
Super administrators, Data Standard administrators, and custom global roles with the Standard-Manage permission can use X-Data Standard.
Flow for extracting lookup table definitions
The large model intelligently extracts lookup table definitions through the following flow: Configure data scope > Identify core fields > Explore and sample data > Extract lookup table definitions
Data scope: You select the data scope from which to extract lookup table definitions.
Identify core fields: The large model analyzes the semantics of the selected data assets to identify core fields for the next step.
Explore and sample data: The system samples and explores the identified core fields to understand their data distribution.
Extract lookup table definitions: Based on data asset metadata, exploration results, and sample data, the large model generates lookup table definitions. These definitions include a name, code, and description for the lookup table, along with code values, code names, and optional code descriptions.
Standard definition flow for Artificial Intelligence Recommendation
The large model intelligently extracts data standard definitions through the following flow: Configure data scope > Identify core fields > Explore and sample data > Extract data standard definitions.
Data scope: You select the data scope from which to extract data standard definitions.
Identify core fields: The large model analyzes the semantics of the selected data assets to identify core fields for recommendations.
Explore and sample data: The system samples and explores the identified core fields to understand their data distribution.
Extract data standard definitions: Based on asset metadata, exploration results, and sample data, the large model intelligently reverse-engineers and generates data standard definitions. These definitions include business properties (data standard code, name, English name, type), technical properties (data type, is unique value, is nullable, range), the data standard set and folder, effective period, owner, description, mapping monitoring configuration, intelligent mapping configuration, and associated information.
Flow for recommending standard mappings
The large model intelligently recommends standard mappings through the following flow: Configure data scope > Identify core fields > Recommend standard mappings.
Data scope: You select the data scope for which to recommend standard mappings.
Identify core fields: The large model analyzes the semantics of the selected data assets to identify core fields for recommendations.
Recommend standard mappings: The system parses metadata, exploration results, and sample data. Using the data standard definitions, it intelligently matches fields to data standards.