Document Type : Research Article


Technical University of Munich, Chair for Information Systems (i17), Boltzmannstr. 3, 85748 Garching by Munich


Data Virtualization (DV) has become an important method to store and handle data cost-efficiently. However, it is unclear what kind of data and when data should be virtualized or not. We applied a design science approach in the first stage to get a state of the art of DV regarding data integration and to present a concept matrix. We extend the knowledge base with a systematic literature review resulting in 15 critical success factors for DV. Practitioners can use these critical success factors to decide between DV and Extract, Transform, Load (ETL) as data integration approach.


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