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Two Misunderstandings About Process Mining and How to Avoid Them

Giving good presentations is hard. You want your give your audience a new perspective – but to send them in the right direction you need to know where they are coming from. Otherwise, they can be lost on the way.

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In the past weeks, I have given several invited talks about process mining. As always, I was met with the enthusiasm I usually encounter when people hear about process mining for the first time and see it in action. And there were loads of follow-up questions and discussions in the break afterwards.

From the questions that I get, I can typically see whether the attendees have understood the concept. Good questions are, for example, about data quality or additional analysis questions (“Can I also do X?”). Not so good questions (for me as a presenter, of course) reveal more fundamental misunderstandings.

I think I got a better grip on two common misunderstandings last week, which I want to share with you here. Perhaps this is useful for your own process mining presentations as it might help you to spot similar confusion.

1. Event logs are not actually logs

The first misunderstanding is mostly caused by the terminology (“logs”, “event logs”, “transaction logs”) and the way that we talk about data in the process mining field. When we say “logs”, people often think of something like an Apache server log or an error log. Of course, this is not what we usually have in mind for process mining.

The transaction records we look for are rarely written out of the information system as an actual “log file”. The relevant information is typically stored in some internal database. Sometimes there is a direct way to export the data as a CSV1 file. Other times we start hunting down timestamped data for relevant business events in the system’s database. For example, in a sales process, relevant process milestones such as “Proposal sent” are most likely recorded somewhere in the supporting IT system, together with a timestamp of when that milestone happened.

One of the big advantages of process mining is precisely that there is no fixed format or “log file” required, but rather that existing information that is already available in your system is used in novel ways. Because there are very few requirements towards the data to be able to do process mining (Case ID, Activity, and Timestamp), it is applicable to almost any process.

_My recommendation: Show an example and emphasize that the “event log” does not need to be there in that form, but that the data can be extracted from almost any IT system. _

2. Processes are already there

People who are not working with business processes sometimes wonder why businesses would bother to “create processes” at all. They do not realize that all these processes we are talking about are already there. All organizations have processes, hundreds of them – to buy things, to hire people, to sell, to produce, to engineer, to advice, to support clients, to support employees, and so forth – Process mining just helps to understand them better.

Conversely, people in the BPM field often think of processes only in terms of the BPM lifecycle. We talked about this in a previous post on 7 misunderstandings about process mining. It is way too narrow to think about processes just in the context of automation. There are many processes leaving traces in non-BPM IT systems today (for example CRM, ERP, PDM, ITSM, Home-made or legacy systems, data warehouses, or even Excel). These processes can be perfectly analyzed with process mining as they are.

_My recommendation: Keep in mind where your audience is coming from. Is their background in BPM? You might want to emphasize that process mining is applicable to processes from other types of systems, too. Is their background very far from typical process improvement topics? You should give them an example of what kind of processes you are talking about. _

Have you had similar experiences when you explained process mining to people who are new to the topic? What other misunderstandings have you encountered? Let us know in the comments!

  1. Comma-Separated Values. ↩︎

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