Process Mining is introduced and explained, including Its benefits for Data Science, and key resources for further exploring Process Mining, including videos, articles, and MOOCs.
Imagine that your data science team is supposed to help find the cause of a growing number of complaints in the customer service process.
They delve into the service portal data and generate a series of charts and statistics for the distribution of complaints over the different departments and product groups.
However, in order to solve the problem, the weaknesses in the process itself must be identified and communicated to the department.
You then include the CRM data and with the help of Process Mining you are quickly in the position to identify unwanted loops and delays in the process. And these variations are even displayed automatically as a graphical process map!
The head of the CS department can detect at first glance what the problem is, and can immediately undertake corrective measures.
Right here is where we see an increasing enthusiasm for Process Mining across all industries: The data analyst can not only quickly provide answers but also speak the language of the Process Manager and visually display the discovered process problems.
Data scientists deftly move through a whole range of technologies. They know that 80% of the work consists of the processing and cleaning of data. They know how to work with SQL, NoSQL, ETL tools, statistics, scripting languages such as Python, data mining tools, and R. But for many of them Process Mining is not yet part of the data science toolbox.
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