title | description | author | ms.author | ms.reviewer | ms.service | ms.subservice | ms.topic | ms.date | LocalizationGroup | no-loc | |
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Update your data model to work well with Copilot for Power BI |
Before you start using Copilot with your semantic model, evaluate your data to see if there are ways you can improve its performance. |
maggiesMSFT |
maggies |
guptamaya |
powerbi |
pbi-reports-dashboards |
conceptual |
01/23/2024 |
Create reports |
|
[!INCLUDE applies-no-desktop-yes-service]
Before you start using Copilot with your semantic model, evaluate your data. You may need to do some clean-up work on your semantic model so that Copilot can derive insights from it.
[!INCLUDE copilot-notes]
The following table lists the criteria to help you create accurate reports with Copilot. These items are recommendations that can help in generating accurate Power BI reports.
Element | Consideration | Description | Example |
---|---|---|---|
Table Linking | Define Clear Relationships | Ensure that all relationships between tables are clearly defined and logical, indicating which are one-to-many, many-to-one, or many-to-many. | "Sales" table connected to "Date" table by "DateID" field. |
Measures | Standardized Calculation Logic | Measures should have standardized, clear calculation logic that is easy to explain and understand. | "Total Sales" calculated as the sum of "SaleAmount" 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. | "Year_To_Date_Sales", "Month_Over_Month_Growth", etc. |
Fact Tables | Clear Delineation | Clearly delineate fact tables, which hold the measurable, quantitative data for analysis. | "Transactions", "Sales", "Visits". |
Dimension Tables | Supportive Descriptive Data | Create dimension tables that contain the descriptive attributes related to the quantitative measures in fact tables. | "Product_Details", "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, avoiding 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 are not 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 "Open", "Closed", "Pending", etc. |
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)", "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 are analyzing. | Indicate if the data is real-time, daily, weekly, etc. |
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 | Document the structure of the data model, including tables, columns, relationships, and measures, for reference. | A data dictionary or model diagram provided as a reference. |