ErpCoder

PowerBI: Filter an unrelated table using Measures

In some cases you want to filter a table based on a selection which is not related to the table. For example the SalesTable is realted to the CustTable via the Account. The QuotationTable is related to the SalesTable (QuotationID -> Origin). The QuotationTable also has an Account field but is not related to the CustTable.

Such a situation can be avoided by using a better design approach, however you may come across such a problem one day 😉

Compare SelectedValue

Create a new measure in the CustTable that returns the selected value. Place a table visual showing the CustTable in the report. For debugging reasons you may want to place a card visual with your measure as well.

M_SelectedValue = SELECTEDVALUE(CustTable[Account])

In the not-related table (QuotationTable) create a new measure that returns the account number which will be used to filter the records

M_Account = SELECTEDVALUE(QuotationTable[CustAccount])

Add a second measure to compare the selected CustTable value to the CustAccount value in the Quotation table and returns a numeric value (not a boolean). If there is no CustTable selected return e.g. 2, if the selected values are equal return 1, else return 0.

M_Filter = IF(
    ISBLANK([M_SelectedValue]),2,
    If([M_SelectedValue] = [M_Account], 1, 0)
    )

Place a table visual in your report showing the QuotationTable and both measures

Configure a filter on the visual to show only records where the M_Filter value greater than 0.

If no customer is selected all quotations are shown because the M_Filter is 2.

If a customer is selected M_Filter is 1 for all matching records or 0 otherwise.

Now you can hide the M_Filter in the QuotationTable so your users don’t get confused

PowerBI: Compare different Timespans and Products in one Matrix

A recent requirement at a customer was to compare Sales Quantity and Sales Totals of different time spans in one matrix. The customer is selling saisonal as well as all-time products. It should be possible to compare the sale of saisonal products from last season to the actual sales.

Data Model

The original data contains 6 rows: The product, the corresponding collection, the sold quantity, the unit price, calculated total price (Qty * Price), date.

In order to make the Quantity and Totals available for a row-like visualization the original data was unpivoted. This created a new column called “Attribute” and another Column called “Value”. The unit (pcs, €) have been added to the attribute to make it more readable. The resulting data looks like the following in PowerBI:

Filter tables

To implement 2 independet filter options based on date and collection 4 independet tables have been added. A date table and a collection table for both filter options. The date tables can easily be generated by using the Calendar() function:

Filter1Date = CALENDAR(Date(2021,1,1), Date(2022,12,31))

Filter2Date = CALENDAR(Date(2021,1,1), Date(2022,12,31))

The collection tables are generated by using the Distinct() function on the collection column in the sales data table. Each of the tables contain a distinct list of collections available for filtering.

 Filter1Collection = DISTINCT(Sales[Collection])

 Filter2Collection = DISTINCT(Sales[Collection])

Measures

Three measures have been create. Two measures that sum up the Value column in the sales data filtered on the selected values in the Filter1* table or Filter2 table:

M_Sum1 = CALCULATE(
    SUM(Sales[Wert]),
    FILTER(
        Sales,        
        Sales[Date] >= FIRSTDATE(Filter1Date[Date]) &&
        Sales[Date] <= LASTDATE(Filter1Date[Date]) &&
        Sales[Collection] in VALUES(Filter1Collection[Collection])
        )
    )

M_Sum2 = CALCULATE(
    SUM(Sales[Wert]),
    FILTER(
        Sales,        
        Sales[Date] >= FIRSTDATE(Filter2Date[Date]) &&
        Sales[Date] <= LASTDATE(Filter2Date[Date]) &&
        Sales[Collection] in VALUES(Filter2Collection[Collection])
        )
    )

The third measure calculates the difference between the other two measures:

M_Diff = [M_Sum2]-[M_Sum1]

Visuals

Finally the report contains 5 visuals. Two slicers for date and collection on the left, another two slicers for date and collection on the right, and a matrix in the middle. The matrix shows the Attribute column as row and shows M_Sum1, M_Sum2 and M_Diff as values.

For example comparing collection “Summer 2021” from 1. Jan. 2021 till 30. Sep. 2021 to the aggregated values of “Collection 2022” + “Basic Wear” from 1. Jan 2022 till 30. Sep. 2022.

The slices on the left only affect the M_Sum1 measure which shows quantity and totals in the Filter1 column while the slicers on the right effect only M_Sum2 measure which shows the results in Filter2 column. The difference is calculate dynamically when adjusting the slicers.