![]() The previous choice of handling in the data model is a much more sound practice. ![]() This is risky because what if you forget? Or what if someone new creates a new report and doesn't know? So, don't do this please. Third choice (worst choice): Your last choice is to change the aggregation in every individual visual. This is the most common way of handling the issue. The dataset serves as our semantic layer, so by setting this in the data model then each report visual can take advantage of this setting. Another way is to set the data type to text. Second choice (shown in this post): Set the summarization setting in the data model correctly for every column in every table. With this choice, no downstream reporting tool whatsoever will try to aggregate columns like SalespersonDept or YearNbr because the column will be detected as text by Power BI. Of course, we live in the real world and this can't be done unless we're creating a brand new source database. The article explains the behavior, strengths, and weaknesses of these. For instance, if you have a data warehouse SalespersonDept should be a varchar(4) instead of an integer. The provided content is an article discussing the differences between the DAX functions SUMMARIZE and GROUPBY in Power BI. Options for Resolving Aggregation Issuesįirst choice: Solve this in the underlying data storage. It's just visual level filters that are applied after the data aggregation occurs in the visual (thanks to Javier Guillen for confirming I wasn't crazy!). Grouping and summarizing information is a powerful feature of Excel pivot tables and Power BI table and matrix visualizations. Note that slicers, page filters, and report filters don't behave this same way-they each return the correct data based on SalespersonDept 1001 because they filter before summarization. Not a good user experience, right? So the message here is that the visual level filters are applied *after* the data aggregation occurs within the individual table or chart.
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