Data Model Descriptions
Data Model Descriptions define the business meaning of data that AI uses.
By providing labels and descriptions to measures, attributes, and saved expressions, you give the AI the context it needs to generate accurate queries, build meaningful reports, and produce relevant insight summaries.
To access Data Model Description, select .
The Data Model Descriptions page displays your workspace's Default Subject Area elements, organized into three categories:
- Measures: Numeric calculations, such as revenue, count of orders, or average price
- Attributes: Descriptive attributes, such as product name, region, or customer category
- Saved Expressions: Custom formulas or calculations you defined in your data model
For each element, you can configure:
| Column | Purpose |
|---|---|
| Enabled | Controls whether the AI can use the entity. |
| Name | Displays the system name of the entity. The value is read-only. |
| Label | A name you provide for the AI to use in responses. |
| Description | Provides business context for the entity. |
The AI relies on labels and descriptions to understand your data. Without them, it can only work with raw system names, which are often abbreviated, technical, or unclear.
Well-written labels and descriptions improve these elements:
- The accuracy of AI-generated BQL queries
- The quality of insight summaries
- The relevance of AI-built reports and dashboards
- How the AI names columns and explains answers to end users