I am trying to find the sum for defective effort but I need it to only sum distinct WorkItemId where Links.TargetWorkItem.WorkItemId is not blank
Measures I currently have:
Defective Effort = CALCULATE(SUM('Work items with direct links'[Effort]),NOT(ISBLANK('Work items with direct links'[Links.TargetWorkItem.WorkItemId])))
For the Sum in the table below (and in the chart) I am wanting the total to be 31 not 35, The Effort for ID 10829 is Getting counted twice
The problem I was having turned out to be the relationship between two tables. Created a table with only one entry for each sprint # then tied all the tables back to that table with a 1:many instead of many:many. This resolved the problem.
Thanks everyone for your help.
Related
I am relatively new to PowerBI and struggling to accomplish the following task.
I have in total 3 Tables. 2 Tables are available and the 3rd is the outcome I am interested in.
Table 1 is a lookup table with yearly values for each option of a certain property.
Table 2 is a user input table containing the project list with a property that can be equal to one of the options in Table 1.
I am mostly interested in Mapping the projects with their yearly development based on one property. The outcome is represented in Table 3.
At the end of the day, I would like to plot the Sum across all projects against the year column (The 2 columns in red).
I hope someone can help here in finding the appropriate DAX logic. Thanks in advance!
You can bring the tables into a Power BI model, and from the modeling tab you can create relationships to accomplish this. Let me know if this is helpful.
I have a table visualisation that shows the populations of countries and a toggle switch that flips between 'sold' and 'unsold'. (This works with a measure that checks is a country is present in a sales table and assigns a 1 or 0 which is then used as a filter on the table visualisation).
Various slicers in the dashboard are used to filter the data model and retain the details of sales. When 'unsold' is selected therefore, the relevant countries are already filtered out of the countries data table and it is not possible to display them with their populations.
At the moment the workaround is to use a duplicate countries table that only has a one way filter, so that the rows remain regardless of filtering. This means that other slicers which interact with the rest of the data model don't filter the table visualisation as desired.
I am sure this must be possible using some combination of CALCULATE(), FILTER() and ALL() but I haven't managed to achieve this.
N.B. I can force the unsold countries to appear in a table visualisation using a constant measure (with formula: measure_name = 0) in a column .
Apologies if this is not very well explained, any help much appreciated.
Thanks for reading,
S
Image attached to (hopefully!) explain problem better.
Real scenario is more complicated hence not screenshotting from PBI.
I'm trying to create a column that has a total of values between 3 columns from 3 tables. How would I go about doing this?
The 2 tables are tables of values that share an id, and they are both linked to a table of account by Id. The goal is to add up 3 columns, and place it into a table grouped by the Id.
I've attempted summing them, trying to use the USERELATIONSHIP function, and creating a relationship between them. It seems to give very inaccurate results, as if it's summing all of the totals together, and passing them to each Id. That, or it won't let me use the column, as if it never existed.
EDIT: General Idea of what I'm trying to do (Lines should be pointing to Account's Id column, but I messed up the lines)
EDIT 2: I also forgot to illustrate or mention. There are more columns with information in each table that can't be summarized for each account preventing me from just merging the table together.
Make sure your data model looks like this (change names as you please, but the structure must be the same):
In dimensional modeling, your table "Account" is a Dimension, and both fee tables are Fact tables. The operation of combining data from multiple fact tables that share the same dimension is called "drill-across", and it's a standard functionality of Power BI.
To combine fees from these tables, you just need to use measures, not columns. This article explains the difference:
Calculated Columns and Measures in DAX
First, create 2 measures for the fees:
Fee1 Amount = SUM(Fee_1[Amount])
Fee2 Amount = SUM(Fee_2[Amount])
Then, create a third measure to combine them:
Total Fee Amount = [Fee1 Amount] + [Fee2 Amount]
Create matrix visual, and place Account_ID from the Account table on the rows. Then drop all these measures into the matrix values area, like this:
Result:
Of course, you don't have to have all these measure in the matrix, I just showed them for your convenience, to validate the results. If you remove them, the last measure still works:
I have created a powerpivot model include in the image below. I am trying to include the "IncurredLoss" value and have it sliced by time. Written Premium is in the fact table and is displaying correctly. I am aiming for IncurredLoss to display in a similar fashion
I have tried the following solutions:
Add new related column: Related(LossSummary[IncurredLoss]). Result: No data
DAX Summary Measure: =CALCULATE(SUM(LossSummary[IncurredLoss])). Result: Sum of everything in LossSummary[IncurredLoss] (not time sliced)
Simply adding the Incurred Loss column to the Pivot Table panel. Result: Sum of everything in LossSummary[IncurredLoss] (not time sliced)
A few other notes:
LossKey joins LossSummary to PolicyPremiumFact
Reportdate joins PolicyPremiumFact to the Calendar.
There is 1 row in LossSummary per date and Policy. LossKey contains this information and is the PK on that table.
Any ideas, clarifications or pointers are most certainly welcome. Thank you!
The related column should work. I was able to get it to work in both Excel 2016 and Power BI Desktop. Rather than bombarding you with questions, I'll try and walk through how I would troubleshoot further, in the hopes it gets you to a solution faster:
First, check the PolicyPremiumFact table inside Power Pivot and see if the IncurredLossRelated field is blank or not. If it is consistently blank, then the related column isn't working. The primary reason the related column wouldn't work is if there's a problem with your relationships. Things I would check:
Ensure that the relationships are between the fields you think they are between (i.e. you didn't accidentally join LossKey in one table to a different field in the other table)
Ensure that the joined fields contain the same data (i.e. you didn't call a field LossKey, but in fact, it isn't the LossKey at all)
Ensure that the joined fields are the same data type in Power Pivot (this is most common with dates: e.g. joining a text field that looks like a date to an actual date field may work, but not act as expected)
If none of the above are the problem, it doesn't hurt to walk through your data for a given date in Power Pivot. E.g. filter your PolicyPremiumFact table to a specific date and look at the LossKeys. Then go the LossSummary table and filter to those LossKeys. Stepping through like this might reveal an oversight (e.g. maybe the LossKeys weren't fully loaded into your model).
If none of the above reveals anything, or if the related column is not blank inside Power Pivot, my suggestion would be to try a newer version of Excel (e.g. Excel 2016), or the most recent version of Power BI Desktop.
If the issue still occurs in the most recent version of Excel/Power BI Desktop, then there's something else going on with your data model that's impacting the RELATED calculation. If that's the case, it would be very helpful if you could mock up your file with sample data that reproduces the problem and share it.
One final suggestion I have is to consider restructuring your tables before they arrive in your data model. In your case, I'd recommend restructuring PolicyPremiumFact to include all the facts from LossSummary, rather than having a separate table joined to your primary fact table. This is what you're doing with the RELATED field to some extent, but it's cleaner to do before or as your data is imported into Power Pivot (e.g. using SQL or Power Query) rather than in DAX.
Hope some of this helps.
I have two tables from Azure SQL in PowerBI, using direct query:
EMP(empID PK)
contactInfo(contactID PK, empID FK, contactDetail)
which have an obvious one-to-many relationship from EMP.empID to contactInfo.empID. The foreign key constraint is successfully enforced.
However I can only create a many-to-one relationship (contactInfo.empID to EMP.empID) in PowerBI. If I ever try the opposite, PowerBI always automatically converts the relationship to many-to-one (by swapping the from and to column), which prevents me from creating visuals. Does PowerBI think the two are equivalent?
Update:
What I'm doing is to just create a table in PowerBI showing the join results of these two tables. The foreign key constraint is contactInfo.empID REFERENCES EMP.empID, which is many-to-one. That should not be a problem, I guess, since I can directly query the join using SQL.
Please also suggest if I should create the foreign key in the opposite direction.
More info on failure to create visual
The exact error message is:
Can't display the data because Power BI can't determine
the relationship between two or more fields.
Version: 2.43.4647.541 (PBIDesktop)
To reproduce the error:
DB schema is as follows:
What I want is a table in PowerBI showing contact and sales info of am employee, that is, joining all the four tables. The error will occur when VALUES of the table visual contains "empName, contactDetail, contactType, productName", however, error will NOT occur if I only include "empName, contactDetail, contactType" or "empName, productName". At first I thought the problem may lie in the relationship between contactInfo and emp, but it now seems to be more complicated. I guess it may be caused by multiple one-to-many relationships?
Expanding my comments to make an answer:
Root of the Problem
In your data model, a single employee can have multiple contacts and multiple sales. But, there's no way for Power BI to know which contactDetail corresponds to which productName, or vice versa (which it needs to know to display them together in a table).
Deeper Explanation
Let's say you have 1 emp row, that joins to 10 rows in the sales table, and 13 rows in the contactInfo table. In SQL, if you start from the emp row and outer join to the other 2 tables, you'll get back (1*10)*(1*13) rows (130 rows in total). Each row in the contactInfo table is repeated for each row in the sales table.
That repetition can be a problem if you do something like sum the sales and don't realize a single sales record is repeated 13 times but might be fine otherwise (e.g. if you just want a list of sales and all associated contacts).
Power BI vs. SQL
Power BI works slightly differently. Power BI is designed primarily to aggregate numbers, and then break them down by different attributes. E.g. sales by product. Sales by contact. Sales by day. In order to do this, Power BI needs to know 100% how to divide numbers up between the attributes on your table.
At this point, I'll note that your database diagram doesn't include any obvious metrics that you'd use Power BI to aggregate. However, Power BI doesn't know that. It behaves the same whether you have metrics to aggregate or not. (And failing all else, Power BI can always count your rows to make a metric.)
Let's say that you have a metric on your sales table called Amt Sold. If you bring in the empName, productName, and Amt Sold columns, Power BI will know exactly how to divide up Amt Sold between empName and ProductName. There's no problem.
Now add in contactDetail. Using your database diagram, Power BI has no way of knowing how an Amt Sold metric in the sales table relates to a given contactDetail. It might know that $100 belongs to empID 27. And that empID 27 corresponds to 3 records in the contactInfo table. But it has no way of knowing how to divide up the $100 between those 3 contacts.
In SQL, what you'd get is 3 contacts, each showing the $100 amount sold. But in Power BI, that would imply $300 was sold, which isn't the case. Even equally dividing the $100 up would be misleading. What if the $100 belonged entirely to 1 contact? So instead, Power BI shows the error you're seeing.
My Recommendations
If you can, I recommend changing your data model before your bring it in. Power BI works best with a single fact table, which would contain your metrics (like amount sold). You then join this fact table to as many lookup tables as you like (e.g. customer, product, etc.), directly. This allows you to slice & dice your metrics with any combination of attributes from any of the lookup tables. I would recommend checking out the star schema data model and the concept of lookup tables: powerpivotpro.com/2016/02/data-modeling-power-pivot-power-bi
At the very least, you would want to flatten your tables (i.e. merge the contactInfo and sales tables into a single table before importing them into your data model.
It may be that Power BI isn't the best tool for what you're trying to accomplish. If all you want is a table showing all sales & contact info for an employee, without any associated metrics, a regular reporting tool + SQL query might be a better way to go.
Side Note: You can't reverse a many:one relationship to get past this error. The emp table contains one row per empID. Both the contactInfo and sales tables contain multiple rows with the same empID. This means the emp table is necessarily the "one" side of the relationship to both those tables. You can't arbitrarily change that.