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Contributed on July 22, 2020

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Challenges and Opportunities for Process Mining

Niels Martin from Hasselt University and his colleagues from Fraunhofer FIT, University of Melbourne, University of Tartu, University of Bayreuth, QUT, and RWTH Aachen are currently doing a study on process mining opportunities and challenges.

They are taking an interesting approach: First, they have asked a large group of process miners from academia and industry to name five opportunities and five challenges that they see for process mining in practice. Then, they classify these opportunities and challenges further and gradually narrow them down in subsequent rounds with the same people.

Which opportunities and challenges would you put on the list based on your own experiences? Here are the five opportunities and challenges that I came up with in the initial questionnaire:

Opportunities

• Make hidden processes visible. Processes are performed by many people and supported by IT systems. As a consequence, nobody can really “see” them in their entirety. Process mining makes these processes visible and shows what is really happening. This is the necessary prerequisite to improve anything, because you can only improve what you can “see” and measure.

• Provide objective reference point for discussion. Too many decisions are based on little more than gut feeling and opinions. This can lead to political situations that can even end in a deadlock situation, where nothing is decided (because people disagree about what the problem is). Process mining can bring rationality and peace into such discussions by making them fact-based.

• Accelerate improvement initiatives. Methodologies like BPM, Lean, and Six Sigma are used to improve processes. The basis for these improvement initiatives is the understanding of the current process. This often happens manually and is very time-consuming. With process mining, the process improvement professionals can engage the stakeholders on a completely different level by asking “Why are we doing it this way?” rather than spending a lot of time on understanding “What are we actually doing?”.

• Increase assurance in audits. Auditors today typically take samples of cases when they perform an audit for a process. Based on these samples, they assess the compliance for the whole process. With process mining, auditors can base their audit on the full population (100% of the data) and, therefore, increase the assurance of their audits.

• Provide process perspective for data scientists. Data scientists use a whole range of techniques, including statistics and machine learning, to perform advanced analyses on data for various use cases. However, all these techniques do not really capture the process perspective. Process mining is an additional tool in the toolbox of the data analyst that can provide a process view to, for example, visualize and analyze customer journeys.

Challenges

• Understanding what process mining can and cannot do. In some circles, there is a bit of a hype around process mining. This enthusiasm can be good to raise awareness about the topic but, for people who are new to process mining, it can make it difficult to understand what you can and cannot do with a process mining tool. Furthermore, the process mining tools on the market are very different, because they focus on different use cases. Organizations first need to think about their own use case to understand what they need.

• Preparing the data. One of the first insights when starting to look for process mining data is often that there are data quality problems. This insight is already valuable by itself, because the data is often also used for other types of analyses. However, addressing these data problems is a hurdle that the organization needs to face. Extracting and preparing the data can take time in the beginning, but it gets easier over time.

• Finding the right place for process mining in the organization. Process mining needs someone who does the analysis. Furthermore, to get the benefits that process mining can provide, it is also crucial that someone then actually does something based on those insights (for example, improve the process). Who will build up this expertise in the organisation? It can be a central process excellence team, a process analyst in a particular department, or an auditor. But whoever it may be, process mining needs to be integrated into their way of working.

• Building a culture of cooperation. Process mining can feel threatening for people who don’t feel safe that the results will not be used against them, or who do not fully understand the analyses that are performed and why. Taking people with you on the journey is very important to create acceptance of the results and to get their input. For this, building up a respectful culture of cooperation and clear communication about what is done, for which purpose, are essential — As is the respect for data privacy laws and practices.

• Keep doing it. It can be a challenge to get process mining to “stick” in the organization. Stopping with process mining after investing time and effort in it is a pity, especially because new initiatives become easier and add more benefits in the future. Companies need to make sure that they establish their process mining expertise in the whole team and not just rely on a single person, who might leave at some point in time. Furthermore, if external consultants are hired then they need to transfer their knowledge before the end of the project.

The study is still in progress. I am just one of the many people who are participating, and I am very curious about the results of this work. Follow Niels to read the study once it is published!