To create high-quality ERP system **data** and increase rigor, we develop requirements for our **data** based on prior work. We follow Baader et al. [4], who find several requirements for both their developed **data** generator and the resulting data. We also draw additional requirements from previously conducted design science research that aims to develop a fraud detection system in the ERP domain. Here, Fuchs et al. [7] aggregate requirements for detection systems that are able to highlight fraud in ERP systems. Some of their requirements describe design decisions that need to be respected during implementation of the fraud detection approach and are unaffected by the studied **data** (e.g. requiring adaptable or intelligent logic). Other requirements, however, describe scenarios in which fraud detection approaches should yield satisfying performance (e.g. detection of outliers in values). We argue that **data** should be created such that the performance of fraud detection approaches can be validated in these scenarios, and therefore identify these requirements as directly relevant for our **data** generation process. Figure 1 gives an overview of the requirements in the preliminary work of Baader et al. [4] as well as the requirements we identify as relevant to our **data** generation process from Fuchs et al. [7]. We additionally note the resulting measures we take in our proposed **data** generation scheme to satisfy these requirements.