3 most common misunderstandings about process mining

When we hear the word mining, we are surely reminded of times gone by. Who would have thought that mining could become a current technology trend in our millennium? Of course, we are not talking about precious metals here, but about processes. Like a few hundred years ago, there are reservations about this trend today.
 
For this reason, let’s take a closer look at the three most common claims and misunderstandings about process mining.

Misunderstanding #1 - Process mining is fingerpointing

Because process mining reveals possible weak points and bottlenecks in processes, this problem is often viewed critically by works councils. Are inefficient employees not outed and exposed? What about data protection? This concern is understandable and entirely justified. In the case of sensitive personal data, however, the highest level of security can be guaranteed by additional pseudonymization of the data. Of course, the human factor must also be considered when optimizing process flows. However, process analyses are less about assigning blame than about the desired process efficiency. The question we ask ourselves is not "who" is to blame, but "why" have processes stalled.

Misunderstanding #2 -  Process mining replaces the classis process mananger

The keyword "automation" always resonates with the idea that workers can be replaced. With the technological approach of process mining, analysts and process managers fear for their posts. The opposite should be the case. The processing and analysis of data was previously done manually with a high expenditure of time. The digitized process analysis, on the other hand, supports the process experts. You can fall back on current figures. Instead of focusing on collecting data, you can focus on the heart of process management, because it is still needed. Software is only as effective as its users. And at the latest when the planned transformation initiatives are being implemented, the human component is more in demand than ever in real business. 

Misunderstandig #3 - Process mining is all about the numbers

Certainly, it cannot be denied that the technology is based on collecting and analyzing data. Strictly speaking, this form of process mining requires numbers and data to be able to represent the processes visually. That is why process mining is not just a numbers game. That would be too short-sighted. Which key performance indicators are really meaningful? Which process strategies really help with the desire for optimized processes? The answers to these questions still require strategic and business-minded thinking. Machine learning and artificial intelligence are therefore still only supporting components on the way to optimized processes.

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