Advancing Process Mining from the Core: Managing Process Mining Project Portfolios from Data Processing to Process Improvement

Process mining is a specialized form of data-driven process analysis that organizations use to understand and improve their business processes. Applying process mining techniques such as process discovery, conformance checking, or enhancement using event logs as the central data source generates insights into process behavior, performance, and compliance. Turning these insights into action supports evidence-based process improvement and strategic decision-making. Therefore, process mining supports multiple phases of the business process management lifecycle (i.e., process discovery, process analysis, process improvement and implementation, and process monitoring and controlling) using data about the execution of a process. The groundwork for these outstanding developments has been laid in academia, where a huge research stream focuses on developing and improving new process mining algorithms for various use cases, resulting in a strong technology core for process mining and analysis techniques. The overall purpose of this dissertation is to advance process mining by building on its solid technological core around the numerous process mining analysis algorithms by adding the missing pieces in preceding and subsequent steps of end-to-end process mining projects. Furthermore, this dissertation also abstracts from a single-project perspective and contributes on the managerial side to the broad applicability of process mining in organizations. Applying design science research principles, the research objectives of this thesis are primarily addressed through design-oriented research by creating and evaluating multiple artifacts in the form of reference architectures, methods, and instantiations. Ultimately, researchers in the process mining field as well as practitioners on the vendor and adopter side should benefit equally from the contributions of this thesis. Therefore, this cumulative dissertation comprising five research papers addresses three challenges that slow down the widespread adoption of process mining in organizations. First, research on adopting process mining at the enterprise level is somewhat fragmented, leading to a call for better guidance on managing process mining project portfolios, complemented by a holistic understanding of the opportunities and challenges of using PM in organizational settings. Therefore, this dissertation provides two deliverables to address this research need: Research Paper P1 provides a holistic overview of the opportunities and challenges of using process mining in organizations. Further, Research Paper P2 developed a method to manage portfolios of process mining projects in a value-oriented manner. Second, for process data quality management, there is a need for a dedicated environment focused on detecting, measuring, and repairing data quality problems. Research Paper P3 proposes a reference architecture for process data quality management to address this research need. The reference architecture is designed to be comprehensive and flexible.