Business Process Simulation (BPS) offers organizations a risk-free environment to evaluate process changes and support decision-making. However, the quality and reliability of simulation outcomes are highly dependent on the quality of event logs used to derive BPS models via process mining techniques. Although data quality issues in event logs, such as missing, incorrect, or inconsistent data, have been extensively discussed in the context of process mining, their specific impact on BPS remains underexplored. This study systematically investigates the intersection of event log data quality and BPS by reviewing 22 relevant papers. We systematically investigate which event log data quality issues have been addressed in existing studies and assess how these issues have been managed, particularly in the context of BPS. Specifically, we identify the types of data imperfections that have been explored in the literature, determine which quality issues relevant to BPS remain insufficiently studied, and analyze the approaches used to detect and mitigate these problems. Through this analysis, the study provides a structured foundation for understanding current research coverage and highlights opportunities for advancing data quality support tailored to BPS needs.