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Alta88
Helper IV
Helper IV

Creating Flags for Data Discrepancies

I'm building a table that pulls data from one of our systems for auditing purposes to cut down on manual review through actual account screens. The idea is to create flags for multiple fields so every morning I can see which accounts have errors.

 

Would this involve creating nested IF formulas? I have little experience in creating these. 

 

 

4 REPLIES 4
Alta88
Helper IV
Helper IV

How would I put the following into DAX syntax?

 

If Credit Score is less than 625, then flag as error.

 

If credit score is 625 or greater, then flex pay should equal $2K, otherwise, flag as error.

 

If Credit Score is 700 or greater, then flex pay should equal $10K, otherwise, flag as error

v-henryk-mstf
Community Support
Community Support

Hi @Alta88 ,

 

Assume that the data from the first day is used as the baseline, and after each update of the data. Create a measure to compare the new column of data with the baseline data, and if there is a difference, mark it in red. Refer to the following test.

vhenrykmstf_0-1661243245943.png

after refresh:

vhenrykmstf_1-1661243445105.png

M_ = IF((MAX(Sheet1[Day1]) - MAX(Sheet1[Update]))=0,"black","red")

vhenrykmstf_2-1661244086395.png


If the problem is still not resolved, please provide detailed error information and let me know immediately. Looking forward to your reply.


Best Regards,
Henry


If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

I like that concept, but our audit process is more so about flagging errors based on user errors and specific account criteria. We build several types of accounts and each have specific criteria based on those types. 

amitchandak
Super User
Super User

@Alta88 , You can use functions like countx, sumx, etc with earlier, and create a new column

https://goodly.co.in/earlier-function-dax-power-bi/

 

If this does not help
Can you share sample data and sample output in table format? Or a sample pbix after removing sensitive data.

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