Data analysis is important for creating a competitive advantage, but the amount of data is already massive and increasing rapidly. Practitioners are looking for general models for different use cases in deciding whether to virtualize data or not and when it is applicable. However, there is a research gap in such models. Thus, in this study, we applied a design science approach in a further step to develop an IT artifact. It is derived from 15 critical success factors, building the foundation for a heuristic individual decision support on data virtualization. In addition, we calculate a final score that recommends extract transfer and load (ETL), hybrid, or data virtualization. The score adapts flexibly to business needs and helps practitioners make decisions. This IT artifact extends the knowledge base by a new methodology for decision support in data virtualization.