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When Incomplete Cases Shouldn’t Be Removed

This is the fourth and last article in our series on how to deal with incomplete cases in process mining. You can find an overview of all articles in the series here.

There are also situations in which you should not remove incomplete cases from your data set. Here are two examples:

Finally, do not forget to assess the representativeness of your data set after you have removed your incomplete cases. For example, if it appears that 80% of your cases are incomplete then it would be very dangerous to base your process analysis on the remaining 20%!

If you do not have enough completed cases in your data set, you may need to go back and request a larger data sample from a longer time period to be able to get representative results.


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