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emarc1
Advocate II
Advocate II

Dynamically filtering erroneous data

Hello.

 

What is the best way to filter out erroneous data points? 

 

Example data showing a single product's (filtered by a slicer) value over time:

 

 

It should look like this (with the erroroneous £185 data point removed):

 

 

 

I can make a calculated column that outputs a high/low/normal flag, but it calculates over the unfiltered table of every product/value.

 

I don't think I can filter by measures so I don't think I could do something similar with that.

 

I can't just filter all data that goes above £100 for all products as the (correct) values of different product varies more widely than that.

 

I can filter the data manually but this isn't as easy for end users.

 

Here is an example table:

 

 

Product     Value
A           £9.30
A           £9.91
A           £9.12
A £185.85 B £31.25 B £31.29
B £0.031 B £31.32
C £0.52
C £0.51
C £0.53
C £0.50

 

Is there a way to make a calculated column that removes outliers for each product separately, within this table? (or better ways of doing a similar task)

 

Many thanks.

1 REPLY 1
DataInsights
Super User
Super User

@emarc1,

 

I adapted the solution described in the link below (kudos to the author!).

 

https://bielite.com/blog/scale-down-outliers-power-bi/ 

 

1. Create calculated column:

 

Outlier = 
VAR vMean =
    AVERAGE ( Table1[Value] )
VAR vStdDev =
    STDEV.P ( Table1[Value] )
VAR vResult =
    IF ( Table1[Value] > ( vMean + vStdDev ), 1, 0 )
RETURN
    vResult

 

DataInsights_2-1665262898133.png

 

2. Create measure:

 

Sum of Value (no Outliers) = 
CALCULATE ( SUM ( Table1[Value] ), Table1[Outlier] = 0 )

 

DataInsights_1-1665262617788.png

 





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