When I listen to people who are skeptical about process mining, I notice that there are still quite a few misunderstandings.
I thought that it might be worth clarifying some of these misunderstandings. So, here are seven typical objections against process mining and how I would react to them.
1. Too good to be true
Especially if one is coming from an academic background, one has to understand that there is a wide gap between what is possible and what people are used to in a typical business setting. Often people cannot grasp what process mining does simply by telling them about it.
Whenever possible, I try to show them the technology. People who have seen a demo of process mining tools are consistently enthusiastic about it.
2. Nobody needs this
Process mining is a generic technology (just like data mining) that must be put in a concrete context to highlight its value. The specific benefits that process mining provides vary depending on whether you use it, for example, for increasing operational efficiency, for risk management and assurance, for reducing errors, or for controlling partners for quality of service contracts.
I try to put myself in the shoes of that person to understand the specific context they are coming from. I then try to provide a concrete example that is relevant and highlights the business benefits in that situation.
3. Never-ending story
Sometimes, there is the misconception that you need endless data collection and data improvement before you can actually start with process mining. The truth is that process mining starts with the data that is already there. One usually starts very simple and iterates as much as is needed. Each iteration brings new value, and even the data quality problems that may surface in the beginning provide value as they can compromise other business tools (KPI reporting, dashboards, etc.) because the underlying assumptions about the measured process don’t actually hold.
I would explain that the only mandatory requirements towards data for process mining are (1) a case ID, (2) an activity name, and (3) a timestamp. When I use an example to show the kind of data that is needed, people usually understand that they have lots of data in that format that can be used right away.
4. Only useful for BPM systems
The key misunderstanding here is that process mining can only be applied to processes that are fully controlled by IT systems. In fact, the processes only need to be observable in some form. It is true that for rigidly configured and model-driven BPM systems there is often little value in re-discovering the process flows. However, even programmed workflows allow for considerable degrees of freedom. There are usually parts of the process that are automated, and some other parts are controlled by humans (but still observable). There often remains quite some flexibility in the way people can operate, and as a consequence there is little insight into what they actually do.
I try to explain that there is a difference between IT systems that fully control the business process and those that support these processes (and as a consequence make them observable by collecting data as a byproduct). Process mining can be applied to a wide variety of data sources including database extracts, transaction log files, and Excel sheets.
5. Doesn’t work in flexible environments
Yes, you probably won’t be able to extract an executable BPMN model from a super flexible healthcare process, where every patient follows a different path. But then again, you most likely don’t want to. Process mining has much broader capabilities than rediscovering executable models. For example, Christian‘s thesis describes applications for process mining in flexible environments, and we at Fluxicon have further developed his techniques to provide tools that are particularly suitable to analyzing also less structured process data.
I would counter by saying that process mining is more useful in flexible environments than for completely controlled BPM systems. One can learn a lot more because the actual process is invisible and emerges on the go. By observing what is happening, you can identify best practices and things that go wrong (and add rules to better steer the system where needed). You can also read Keith’s Swensons post on Flipping the Process Lifecycle to see how process mining fits into the Adaptive Case Management (ACM) paradigm.
6. Not new
Well, it’s true that process mining is not that new anymore. The research at Eindhoven University of Technology started around 1998 in this area and influences can be traced back until even earlier. Everything is a remix. But it’s new as a structured approach to analyzing data from a process perspective that is now finding its way out of the research lab into the business world.
I usually try to explain the differences of process mining compared to traditional process modeling and data mining, Business Intelligence, simulation, and standard query tools to position the technology. The main differentiator is the process focus and the generic framework to analyze data from a process perspective.
7. Just paving the cow paths
In his article on Desire Lines or Cowpaths, Wil van der Aalst addresses the objection of people saying that there is no need to know how things work right now as they want to change it for the better anyway. They use the BPR mantra “do not pave the cow path” to support their arguments. This discussion comes down to the broader question of whether one should do an ‘as-is’ analysis in the beginning of a process improvement project or not. Process mining is about ‘as-is’ analysis, other methods are doing interviews, walk-throughs etc.
I would respond that one cannot properly redesign a process without understanding it first. Understanding the current process is just the “zero measurement” that you need in order to know where you are at the beginning of your process improvement project, and to measure how far you have come in the end. You can also take a look at this discussion in the Lean Six Sigma group, where ca. 200 people argue that it would be a big mistake to skip ‘as-is’.
Some final words
The process mining manifesto has given some more visibility to process mining, which is great. Let’s all provide further examples and case studies to substantiate the specific benefits of process mining. A great example is Alberto Manuel’s experience report about process mining here. If you have some process mining experiences to share but don’t have your own blog, feel free to contact us and we can report on it here.
My hope is that we all continue to substantiate the concrete benefits in balance with expectations, not to create a hype. It’s also not necessary that everybody is a “believer”. Let’s not make an ideology out of it. For some people process mining may not be applicable, and others may have hidden agendas that prevent them from acknowledging the usefulness of this new technology.
What other objections have you come across in your discussions about process mining? Or do you have your doubts yourself, and did not find them addressed in this post? I am really curious to hear them: Let’s continue the discussion in the comments!