Simulations consist of a series of individual steps in which different types of data will be consumed or produced. The dependencies among these types of data, the execution time and the algorithms are complex. In order to obtain exact and reliable output data, the quality of the input data must be guaranteed to an acceptable degree. Unacceptable data quality in a simulation could yield under-expected results, leading to a waste of human effort and computing resources. Hence, it is necessary to detect data quality problems as early as possible in order to optimize the execution. Therefore, we have developed an approach for data-quality-aware workflow management. We have studied a list of data quality metrics that can be used in data-quality-driven simulation workflows to trigger the automatic selection of suitable software or hardware components, to control the simulation flow, and to inform the user promptly about data quality-related simulation problems. Based on this information, users and workflow management systems can intervene during the execution of simulations by rearranging simulation steps, changing data sets used, or replacing software as well as hardware components.