Question

Why is my DAX measure displaying incorrect values when using time intelligence functions?

I’m working on a Power BI report using time intelligence functions like TOTALYTD, SAMEPERIODLASTYEAR, etc., but the DAX measures are returning incorrect values. The totals or comparisons don’t match expectations, and I suspect the issue lies in how my date table or DAX calculations are structured. What are the best practices for using time intelligence functions in Power BI, and how can I troubleshoot the incorrect values?

Answer 1

Erroneous output is typical when using DAX time intelligence functions in Power BI due to the incorrect establishment of the date table or misapplication of the DAX calculations. These are some of the dos and don’ts.

• Have A Proper Date Table: Power BI’s time intelligence functions like TOTALYTD and SAMEPERIODLASTYEAR need a good functioning date table without any gaps and marked correctly as per its usage, which means it should not have any boundaries. This table ideally covers all the dates on which the data is depicted. It should be marked as a ‘Date Table’ in Power BI. Ensure that the date table is inclusive of all the dates related attributes such as year, quarter, month, and day as these will Incorporate and Address Geographic and Temporal Contexts as Well: In cases where you have data visualizations that include time intelligence features and also geo diagrams or location-based charts, it is very important to make sure that the date field and the location field are made to interact appropriately.
• For instance, if you are visualizing your data in the form of maps with sales knitted by a country and you take into account the figure in relation to year-to-date sales, then both the time and location slicers should be set up such that they do not cause filtering conflict. When time and location elements are not properly balanced, aberrations in the expected aggregations may occur in the measures calculated using DAX, and this, in turn, produces inaccurate information in the maps or location charts.
• Check DAX Syntax for Effect on Time: DAX time intelligence functions pay particular attention to syntax and filter context when calculating values. Hence, the a need to audit your measures.
• For instance, remember that TOTALYTD or SAMEPERIODLASTYEAR should be used on the date column of your date table and not the fact table. Furthermore, make sure that the function is processing an appropriate date range of data by performing the test using less complex DAX measures in isolation. In the event that your totals are incorrect, you may have to reconfigure your filters or change the way in which the time intelligence functions are used.
• Thus, by maintaining the presence of a proper date table, the correct and proper relationships between tables, geographic and temporal filters, and DAX syntax, many problems faced with time-addressing calculations in Power BI can be overcome, and correct results can be obtained. Assist DAX in filtering and aggregating the time-dependent data. DAX, when presented with a date table that is partial or is not declared as a date table, will render processes inaccurately.
• Make Sure The Date Table Is Properly Connected To The Three Fact Tables: Ensure that the date dimension is connected to your main data (fact) table through a single, active relationship, usually on a date key. In Power BI, the date table should join only to one column of the fact table to allow proper comprehension of the time intelligence functions. In DAX, if there are several dates, such as an order date and a delivery date, make those relations and activate them in DAX as required.