Addressing problems associated with Power BI’s relationship auto-detection capabilities starts with looking at how such relationships are established, if any. The auto-detect function will, in some instances, form relationships based on the semantics of the columns or data values that do not properly reflect the intended data model. Here are a few recommended ways to curb or turn off the impact of such relationships:
Turn Off Auto-Detection of Relationships: Toggling this option off would be ideal in the event of further issues caused by auto-detect. You can do this by going to the tab in the left-hand corner labeled File, navigating to Options and Settings, and then selecting Options. Under the Data Load section, turn off the option that states Auto detect new relationships after data is loaded. This stops Power BI from attempting to make relationships in the model every time new datasets are imported.
Manually Define Relationships: After turning off automatic relations, you can now build the relations you want on your own. Open the Model view of your Power BI Desktop. This page enables users to relate fields by dragging and dropping or using the Manage Relationships window. This promotes good relationship management, including the ability to designate the cardinality and cross-filter direction.
Review Relationship Properties: After the relationships have been created manually, it is also important to consider the relationship properties of each of the created relationships. Pay attention to the cardinality (one-to-one, one-to-many, etc.) employed and ensure it corresponds to the data structure each mutually exclusive segment holds. In addition to that, also include the cross-filter direction. In cases of complex models with multiple interrelated tables, bidirectional filtering may be necessary for proper data display between the tables.
Employ the Data View for Data Analysis: Some results are unexpected because of poor data quality. This is where the Data view feature comes into play. You may clean the data out of your tables from their kinks. Overlapping data, empty data, or any data that would interfere with that connection should be looked into. It is also worth it to clean data up before joining tables in order to cut down on potential problems later.
Use DAX to measure the result above. When a problem persists, even after relationships are resolved, DAX measures are used to check the outputs. Make some simple measures to count or aggregate data in reference tables. This should help you understand where the problems are coming from.
Frequent Testing of Relationships: As your data model grows and develops, the need to test your relationships is equally critical. Examine the structure of your model and determine if each relationship has a corresponding connection based on the business logic of its members. The need to do so is even more pronounced whenever alterations to the data sources take place.
By doing this, you will be able to regain control of the relationships in your Power BI model; hence, the data representation functions for your analytical purpose. A proper data model is vital in capturing the correct information, and therefore, it is worth spending time on this activity.
