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Optimize your semantic model for Copilot in Power BI

APPLIES TO: Power BI Desktop Power BI service

We recommend that you evaluate your data before you start using Copilot with your semantic model. You might need to clean up your semantic model so that Copilot can derive insights from it.

Note

Keep the following requirements in mind:

Considerations for semantic models for Copilot use

You can create accurate reports with Copilot by using the criteria in the following table. These recommendations can help you to generate accurate Power BI reports.

Element Consideration Description Example
Table linking Define clear relationships Ensure that all relationships between tables are clearly defined and logical. Indicate which are one-to-many, many-to-one, or many-to-many. The Sales table is connected to the Date table by the DateID field.
Measures Standardized calculation logic Measures should have standardized, clear calculation logic that's easy to explain and understand. Total Sales is calculated as the sum of the SaleAmount field from the Sales table.
Measures Naming conventions Names for measures should clearly reflect their calculation and purpose. Use Average_Customer_Rating instead of AvgRating.
Measures Predefined measures Include a set of predefined measures that users are most likely to request in reports. Like Year_To_Date_Sales or Month_Over_Month_Growth.
Fact tables Clear delineation Clearly delineate fact tables, which hold the measurable, quantitative data for analysis. Transactions, Sales, and Visits.
Dimension tables Supportive descriptive data Create dimension tables that contain the descriptive attributes related to the quantitative measures in fact tables. Product_Details and Customer_Information.
Hierarchies Logical groupings Establish clear hierarchies within the data, especially for dimension tables that could be used to drill down in reports. A Time hierarchy that breaks down from Year to Quarter to Month to Day.
Column names Unambiguous labels Column names should be unambiguous and self-explanatory. Avoid the use of IDs or codes that require further lookup without context. Use Product_Name instead of ProdID.
Column data types Correct and consistent Apply correct and consistent data types for columns across all tables to ensure that measures calculate correctly and to enable proper sorting and filtering. Ensure numeric columns used in calculations aren't set as text data types.
Relationship types Clearly specified To ensure accurate report generation, clearly specify the nature of relationships (active or inactive) and their cardinality. Mark whether a relationship is One-to-One, One-to-Many, or Many-to-Many.
Data consistency Standardized values Maintain standardized values within columns to ensure consistency in filters and reporting. If you have a Status column, consistently use filters like Open, Closed, or Pending.
Key performance indicators (KPIs) Predefined and relevant Establish a set of KPIs that are relevant to the business context and are commonly used in reports. Return on Investment (ROI), Customer Acquisition Cost (CAC), or Lifetime Value (LTV).
Refresh schedules Transparent and scheduled Clearly communicate the refresh schedules of the data to ensure users understand the timeliness of the data they're analyzing. Indicate if the data is real-time, daily, weekly, and so on.
Security Role-level definitions Define security roles for different levels of data access if there are sensitive elements that not all users should see. Sales team members can see sales data but not HR data.
Metadata Documentation of structure For reference, document the structure of the data model, including tables, columns, relationships, and measures. A data dictionary or model diagram provided as a reference.

The following table lists other criteria that can help you create accurate Data Analysis Expressions (DAX) queries with Copilot. These recommendations can help you generate accurate DAX queries.

Element Consideration Description Example
Measures, tables, and columns Descriptions In the description property, define each element and how you intend to use it. Only the first 200 characters are used. [YOY Sales] description could be: "Year-over-year (YOY) difference in Orders. Use with the 'Date'[Year] column to show by years other than the latest year. Partial years compare to same period of the prior year."
Calculation groups Descriptions Calculation items aren't included in the model metadata. Use the description of the calculation group column to list and explain the use of the calculation items. Only the first 200 characters are used. For example, the Time intelligence sample calculation group column could have this description: "Use with measures and date table for Current: current value, MTD: month to date, QTD: quarter to date, YTD: year to date, PY: prior year, PY MTD, PY QTD, YOY: year over year change, YOY%: YOY as a %." A table with measures could expand on the usage with a description like: "Measures are used to aggregate data. These measures can be shown as year-over-year by using this syntax CALCULATE([Measure Name], Time intelligence[Time calculation] = YOY)."

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