Process Mining relies on historical event data as its single source of truth. Using such event data, process mining techniques visualise and analyse the behaviour and performance of organisational processes. To generate actionable recommendations from process mining, the event data must be of high quality. Over the past decade, there has been increasing recognition in the field of 1) the need for high-quality data and 2) the significant time and effort being spent on data pre-processing tasks. This paper provides an overview of the state-of-the-art in process data quality management research, proposes a four-phase data quality management lifecycle for a systematic treatment of data quality challenges and positions its role among recent advances in process mining, e.g., streaming process mining and object-centric process mining.