Data quality is critical to organizational success. In order to improve and sustain data quality in the long term, process-driven data quality management (PDDQM) seeks to redesign processes that create or modify data. Consequently, process modeling is mandatory for PDDQM. Current research examines process modeling languages with respect to representational capabilities. However, there is a gap, since process modeling languages for PDDQM are not considered. We address this research gap by providing a synthesis of the varying applications of process modeling languages for PDDQM. We conducted a keyword-based literature review in conferences as well as 74 highranked information systems and computer science journals, reviewing 1,555 articles from 1995 onwards. For practitioners, it is possible to integrate the quality perspective within broadly applied process models. For further research, we derive representational requirements for PDDQM that should be integrated within existing process modeling languages. However, there is a need for further representational analysis to examine the adequacy of upcoming process modeling languages. New or enhanced process modeling languages may substitute for PDDQM-specific process modeling languages and facilitate development of a broadly applicable and accepted process modeling language for PDDQM.