Data-driven systems and machine learning-based decisions are becoming increasingly important and are having an impact on our everyday lives. The prerequisite for this is good data quality, which must be ensured by preprocessing the data. However, a number of challenges arise in the process. These include the results of the process in terms of data quality, e.g., combating bias and ensuring fairness, and the preprocessing process itself. Here, human involvement and the lack of intelligent solutions and applications for domain experts without in-depth IT knowledge play a major role. This paper summarizes these challenges and provides an overview of the current state of the art. It proposes the design of a holistic tool, along with the necessary tasks to overcome these challenges and to support data preprocessing.