
In a concise tutorial, Pragmatic Works demonstrates how to create a reusable DAX measure that shows which values a user has selected in a report, then surfaces that message in a card visual. The presenter, Angelica, walks viewers through the logic using core functions like IF, ISFILTERED, VALUES, and CONCATENATEX, and then formats a visual to display human-readable text such as “Years selected: 2019, 2020, 2021.” Consequently, this pattern helps viewers avoid confusion when cross-filtering makes charts appear to show unexpected data. As a result, the measure makes selection states explicit without disabling interactivity.
The video breaks the solution into clear steps: create a dedicated measures table, write the selected-values measure, add a card visual, and adapt the pattern for other fields like distance groups. Angelica also shows how to use IF together with ISFILTERED to detect whether a slicer or visual has an active filter, and then uses CONCATENATEX with VALUES to build readable lists of selections. While she focuses on examples like year selections and distance ranges, the same idea applies to categories or product lists. The video timestamps highlight setting up the table, building the measure, testing the card, and reusing the pattern for different fields.
First, create a small table in the model reserved just for report measures so you can find the new formula easily. Then add a new measure that tests filter state, for example using ISFILTERED or HASONEVALUE, and returns a formatted string; when several values are selected, CONCATENATEX joins them with commas or a chosen separator. Next, place that measure into a card visual and format the card to fit the report theme and layout. Finally, repeat the pattern for other fields by changing the referenced column and adjusting the formatting to suit length and context.
This approach improves usability because it keeps cross-filtering active while removing ambiguity about what a chart shows, which is especially helpful for occasional or non-technical users. On the other hand, showing long lists of selections can clutter the space and reduce legibility, so designers must balance completeness against readability by limiting displayed items or summarizing ranges. Moreover, the technique is low-cost to implement and highly reusable, but it adds a maintenance consideration when your model grows or when you rename fields. Therefore, teams should weigh immediate clarity for users against ongoing upkeep and potential visual noise.
One common challenge is handling many selections: concatenating dozens of values can produce very long strings that break layout or exceed visual limits, so developers often cap the count or show “N items selected” with an option to expand. Another issue is performance on very large models when the measure evaluates many values; prudent use of filter context and efficient DAX patterns matters here. Localization and formatting also complicate things, because separators, ranges, or date formats must match user expectations in different regions. Consequently, report builders must test across typical scenarios and plan for edge cases.
To keep the measure maintainable, store it in a dedicated measures table and give it a clear name that signals its purpose, like “Selected Values – Year.” Additionally, limit the number of concatenated items or present a short summary followed by a tooltip or drill-through for full details to reduce clutter. When reusing the pattern, adapt the separator and null handling for non-text fields and consider wrapping long text into a tooltip or secondary visual. Finally, run quick performance checks on representative data volumes to avoid surprises after deployment.
Teams that support mixed audiences will find this pattern a practical way to reduce support questions about “incorrect” visuals while keeping interactive filtering available. For governance, document the measure pattern and include examples in a report template so report authors can copy and adapt it without introducing inconsistent behavior. While the approach is simple, it pays to standardize naming, formatting, and limits so users see a consistent UX across reports. In the end, this measure offers a pragmatic balance of clarity, interactivity, and maintainability that many Power BI authors will value.
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