Digital processes drive an organization's digital transformation. Core business exhibit a high digital maturity, whereas support processes often receive less attention despite their essential role. This research develops and validates two models to support prioritizing processes for digitalization and to enable a continuous and cyclic process optimization. Case studies confirm their value, laying the groundwork for future research in data-driven process analysis and operational support.

Digital processes are the backbone of an organization's digital transformation to increase its competitive advantage. Digitalization efforts have increased in recent years but target quick-paced implementation of digital technologies for short-term benefits. Core business processes usually exhibit a high digital maturity, but processes that do not directly contribute to the value stream experience less attention in an organization's digital strategy. However, these support processes do not assume a less essential role. Instead, their daily execution enables core processes and affects an organization's productivity. For example, a quick and thorough employee onboarding minimizes mistakes and decreases the time until a new employee is self-reliant. This research addresses the identified gaps, and its approach leads to the primary research question structuring the approach to this topic: How can a systematic approach to process optimization contribute to uncovering process insights for digital business processes? This thesis aims to develop, implement, verify, and validate a systematic and structured approach to transform non-digital to digital processes, focusing on support processes and continuous data-driven optimization. Two models account for the associated tasks. The first model addresses the gap in prioritizing processes for digitalization by expanding the scope of existing process maturity models that satisfy most formulated requirements. The second model is a cyclic procedure model for process optimization originating from process mining approaches and expands designated activities to include business process management aspects and considers repeated model application to handle concept drifts due to ever-changing operation conditions. The verification and validation in two case studies prove the models' functionality and added value, further supported by feedback from a team of experts involved in developing, implementing, and testing. The research results set the foundation for future research to expand the scope of process analysis to data streams and provide proactive operational support in process execution.