Process mining in action is an edited collection of papers on process mining. The papers are divided into three parts. The book should interest academics, executives, and professionals with an interest in the topic.

Part 1 is on the principles and value of process mining. It includes eight papers by editor Lars Reinkemeyer. “Process Mining in Nutshell” projects process mining to be a business process management (BPM) analytics technique based on events logs for digital mapping. It requires transparency to be successful. “How to Get Started” shows, from the onset, how purpose, (tool driver) people, and process tracing need to be clearly defined and should focus on first simplicity and then complexity. “Purpose: Identifying the Right Use Cases” shows how process mining exercises should be precisely defined by identifying use cases. “People: The Human Factor” underlines learning despite the availability and use of sophisticated IT tools--it’s all about the people. “Processtraces: Technology” asserts that process tracing is process mining’s technical foundation and recommends an open platform. Big data or raw data is obtained from various backend systems and used to yield smart datasets, which in turn are processed through data analytics tools to yield useful insights. In “Challenges, Pitfalls, and Failures,” the author states that “learning from mistakes” is the best approach and recommends agile techniques (“fail fast or scale fast”). Failures primarily result from deficiencies with respect to data availability, benefit calculations, return on investment (ROI) calculations, complexity monitoring, process conformance, team selection, project processes, “data hunger,” and content definition. “Process Mining, RPA, BPM, and DTO” discusses BPM in the context of process mining and its use in the specification and implementation of robotics process automation (RPA). It also touches on the concept of a digital twin of an organization (DTO). “Key Learnings” lists all ten key learning points without further explanation. Part 1 provides neither references nor a bibliography.

Part 2 consists of 12 papers on “Best Practices Use Cases,” written by process mining industry experts.

Part 3 has two papers. “Academic View: Development of the Process Mining Discipline,” by Wil Aalst, describes how a lack of analytical rigor affects progress and process hygiene. “Business View: Towards a Digital Enabled Organization,” by Lars Reinkemeyer, underlines the current trend toward a progressive use of process mining using currently available information technologies, as well as user expectations that process mining will be able to provide predictive scenarios and possible solutions in the near future. In the midterm and beyond, process mining will hopefully provide a framework for the development of self-learning and self-optimizing systems using artificial intelligence (AI), and benchmarking and standards will evolve.