What type of analysis helps summarize historical data in process mining?

Descriptive analytics is the type of analysis that focuses on summarizing historical data to understand what has happened in a process over a certain time frame. In process mining, this analysis provides insights into historical performance, bottlenecks, exceptions, and overall trends. It helps organizations visualize past activities, identify common patterns, and quantify the efficiency of processes based on previously recorded data.

Descriptive analytics forms the foundation for further analysis, as it lays out the essential information needed to inform decision-making, guide investigations into performance improvements, and to establish baseline metrics for comparing future performance. Its emphasis on summarizing what has already occurred is critical in process mining, where understanding past behaviors can lead to better process optimization strategies.

In contrast, predictive analytics is focused on forecasting future trends based on historical data, comparative analytics assesses performance against benchmarks or across different processes, and formative analytics is typically associated with ongoing assessments during a process rather than a retrospective analysis of completed data. Thus, in the context of summarizing historical data, descriptive analytics is the most appropriate type.