This is an editorial for a special issue "Innovations in Exploring Sequential Process Data" in the journal Zeitschrift für Psychologie. Process data refer to log files generated by human-computer interactive items. They document the entire process, including keystrokes, mouse clicks as well as the associated time stamps, performed by a test-taker to complete a task. Due to their vast potential for providing in-depth insights into how test-takers approached the administered tasks, process data from educational assessments have gained increasing attention in the psychometric, psychological, and educational science literature. However, data that support detailed documentation of test-takers' cognitive and behavioral processes above and beyond the mere time required for solving the task are commonly complex and versatile, and leveraging their potential is therefore not straightforward. Examples for such data are keystroke data, clickstream data, navigation behaviors, video stream, or eye-tracking, just to name a few. This topical issue highlights the potential and plurality of this still developing field of research. It illustrates the large variety of sequential process data that can be obtained from computerized questionnaire and test administrations, the broad array of research questions and measurement challenges that can be tackled by making use of their rich information and sequential nature, and how these data can be used for theory building and evaluation alike. It further showcases how these complex data types can be synthesized through inherently contrasting viewpoints; drawing on exploratory machine learning and data mining techniques on the one hand and perceiving them through the lens of cognitive theory on the other.