Foreword By Prof. Wil van der Aalst

Data science is the profession of the future. However, it is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to processes. At the same time, process analysis professionals need to learn how to incorporate data from the IT systems into their work.

Process mining is a technology that has gained more and more popularity over the past years. As the figure in Figure 1 shows, process mining bridges the gap between the traditional, model-based process analysis (which focuses on processes but does not use any data) and data-centric analysis techniques such as machine learning and data mining (which use data but do not analyze end-to-end processes).


Figure 1: Process mining bridges the gap between model-based process analyses and data-centric analysis techniques.

The process mining analysis can have many different goals, for example, answering performance-oriented or compliance-oriented questions. What makes process mining so powerful is that it is a generic tool that can be used by a practitioner without programming. In many ways, process mining is as revolutionary for processes as spreadsheets were for numbers. Spreadsheets replaced calculators and the waiting times for mainframe calculations. They empowered professionals in finance and everywhere to build models and do things they could not do before. With process mining, the process analyst does not need to wait for their IT department until they set up the new report that they asked for. Instead, they can request a data extract and analyze the process themselves, without having to know all their questions in advance.

When we started with our process mining research at the Eindhoven University of Technology in 1998, we initially called it ‘workflow mining’. People questioned its usefulness, because they doubted that the data would be available. Today, nobody says that anymore. There exists so much data that people are looking for new ways to leverage their data. We quickly realized that this technology had a much wider spectrum of application than just workflows. Process mining can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using a booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, it is no longer acceptable to just look at processes and data in isolation.

It is exciting to see process-mining tools in use at so many organizations. Moreover, many more people are still learning about process mining. For example, more than 100,000 people from all over the world have already followed our Process Mining MOOC so far. As process mining techniques become more powerful and are applied in many application domains, it is important to use this novel technology in a responsible manner.

Like any other data analysis technique, process mining is an instrument that can be misused, either deliberately or due to insufficient data-science knowledge. Understanding how limitations in data quality, incomplete cases, or your particular analysis perspective might limit your analysis capabilities is crucial to delivering results that are ethical and constructive for your organization.

From the beginning, the team at Fluxicon has not just focused on building their process-mining tool but also invested in the process mining community. They have contributed by spreading the word about this new technology but also in raising the bar of what a process-mining practitioner ought to know about this new instrument and the methodology to use it well.

Anne Rozinat and Christian Günther, the founders of Fluxicon, are true process mining experts. Both did a PhD in process mining and have pioneered the field. For example, Anne was the first to work on conformance checking and data-aware process mining. Christian developed discovery techniques able to deal with semi-structured processes and was the first to add token-based animations to discovered process models. Based on these experiences, they developed Disco and thus lowered the threshold for starting with process mining significantly.

Anne and Christian have now compiled their knowledge into this comprehensive process-mining guide. This book is an important step for the process mining community and should be on the desk of every process-mining practitioner.

– Wil van der Aalst, January 2018

Note Wil van der Aalst is a full professor of Process and Data Science at RWTH Aachen University. He is the author of the book Process Mining: Data Science in Action and the creator of the Process Mining MOOC.

Wil van der Aalst has published 200 journal papers, 20 books (as author or editor), 450 refereed conference/workshop publications, and 65 book chapters. Many of his papers are highly cited (with over 83.000 citations he one of the most cited computer scientists in the world and has an H-index of more than 136 according to Google Scholar) and his ideas have influenced researchers, software developers, and standardization committees working on process support.