DAX Studio helps you navigate through the performance metrics; it improves the productivity in executing one or more of the collected queries, reducing the number of copy/paste operations required. In order to use the DAX Studio feature, first you must export the data collected by Performance Analyzer in Power BI Desktop.
Click through to see how it’s done.
As I said, this pretty much depends on the business requirements and after discussing that in length with the users, the solution is usually to simply add an “Others” row that sums up all values which are not part of the TopN items. For regular business users this requirement sounds really trivial because in Excel the could just add a new row and subtract the values of the TopN items from the Grand Total.
However, they usually will not understand the complexity behind this requirement for Power BI. In Power BI we cannot simply add a new “Others” row on the fly. It has to be part of the data model and as the TopN calculations is already dynamic, also the calculation for “Others” has to be dynamic. As you probably expected, also this has been covered already:
Oraylis – Show TopN and rest in Power BI
Power BI community – Dynamic Top N and Others category
This is a pain point that ideally I’d like the Power BI team to address. Gerhard does a good job showing how to do it, but Tableau has that (and more) built in.
This is a measure that returns a text-value, showing the number of rows of the adjusted filter context table, the MIN and MAX value of the selected column as well as up to the first 10 values. Just place this measure beneath the CALCULATE-measure in question and try to find the error
Just have in mind, that this only works for standalone CALCULATE-functions and not for those who are nested in other functions (who modify the evaluation context).
This looks to be quite useful.
What happens when we need to rank using multiple criteria? In the example below, we are going to look at resellers by name and key in a SalesTerritoryGroup, and we are going to rank them based on the number of items they have sold.
Let’s start with the model. I did slightly modify Rob’s original 3 table model approach. I now have 5 tables here, but the model conceptually still works the same. I have a sales table, a date table, a resellers table, and two lookup tables, one for geography and one for sales territory.
Read the whole thing.
Well maybe.. what happens here is that the DAX engine took the whole date range we have in context and shifts it back 12 months. This means for year 2019 it will use January first to December 31. So we get the entire year, is that what we want? Or do we want to see the sales for the previous year until the day we have data for this year so we can compare? Both need different DAX so let’s take a look.
Read on for a detailed analysis, including where you might go wrong.
Sometimes, when working with DAX, you might get the following error:
The expression refers to multiple columns. Multiple columns cannot be converted to a scalar value.
This error occurs whenever the DAX engine was expecting a single value, or scalar, and instead received a table of values instead. This is an easy error to make because many DAX functions, such as FILTER, SUMMARIZE and ALL, return table values.
Eugene lays out when each scenario occurs, so check it out.
These measures produce the correct figures month by month. However, at the year level the number of working days might be too big whenever there are incomplete months – this always happens before the end of the year, or when the first month with sales is not January as in the following example. As you can see in the following figure, in this case the yearly value of SalesPerWorkingDays is lower than the value of any month, which does not make much sense.
Click through for the solution.
A function ‘MAX’ has been used in a True/False expression that is used as a table filter expression. This is not allowed.
Read on for Eugene’s explanation and what you can do about it.
First things first: what is a semi-additive calculation? Any calculation can be either additive, non-additive or semi-additive. An additive measure uses SUM to aggregate over any attribute. The sales amount is a perfect example of an additive measure. Indeed, the sales amount for all customers is the sum of the individual sales for each customer; at the same time, the amount over a year is the sum of the amounts for each month.
A non-additive measure does not use SUM over any dimension. Distinct count is the simplest example: the distinct count of products sold over a month is not the sum of the distinct counts of individual days. The same happens with any other dimension: a distinct count of products sold in a country is not the sum of the distinct counts of the products sold in each city in the country.
Semi-additive calculations are the hardest ones: a semi-additive measure uses SUM to aggregate over some dimensions and a different aggregation over other dimensions – a typical example being time.
Semi-additive measures are probably the trickiest of the three, as you can easily work with additive measures and you know you won’t be able to do much with non-additive measures.
How is it possible to compute the difference between the two dates, only computing working days and skipping weekends and holidays? Simple math is no longer useful here, and DAX does not offer a predefined function.
A solution to this scenario requires a date table – more details here – with a specific column, IsWorkingDay, which indicates whether that particular day is a working day or not. The following figure shows an example:
Another good use of date tables (AKA calendar tables), which are also quite useful in T-SQL queries.