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Data Quality Problems In Process Mining And What To Do About Them — Part 1: Formatting Errors

Data Center Cleanup

[This article previously appeared in the Process Mining News – Sign up now to receive regular articles about the practical application of process mining.]

Data for process mining can come from many different places. One of the big advantages of process mining is that it is not specific to some kind of system. Any workflow or ticketing system, ERPs, data warehouses, click-streams, legacy systems, and even data that was collected manually in Excel, can be analyzed as long as a Case ID, an Activity name, and a Timestamp column can be identified.

However, most of that data was not originally collected for process mining purposes. And especially data that has been manually entered can always contain errors. How do you make sure that errors in the data will not jeopardize your analysis results?

Data quality is an important topic for any data analysis technique: If you base your analysis results on data, then you have to make sure that the data is sound and correct. Otherwise, your results will be wrong! If you show your analysis results to a business user and they turn out to be incorrect due to some data problems, then you can lose their trust into process mining forever.

There are some challenges regarding data quality that are specific to process mining. Many of these challenges revolve around problems with timestamps. In fact, you could say that timestamps are the achilles heel of data quality in process mining. But timestamps are not the only problem.

In this …

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