Torsten Zipf

• Big Data has been among the most innovative topics in literature sources and among organizations for years. Even though only few organizations realized the significant value potentials described by contemporary literature sources, it is widely acknowledged that data assets can provide significant competitive benefits. Given the promises regarding value increases and competitiveness, practitioners as well as academia desire systematic approaches to transform the data sets into measurable assets. This dissertation investigates the current state of literature, conducts an empirical investigation through a structural equation modeling and applies existing theory to develop a model that allows organizations to apply a systematic approach to measure the value of Big Data specifically to their organization. With Business Process Management as the foundation of the model, IT as well as business functions will be able to successfully apply the model. Based on the assumption that Data is acknowledged as a production factor, the developed model supports organizations to justify Big Data investment decisions and thereby to contribute to competitiveness and company value. Furthermore, the findings and the model equip future researchers with a framework that can be adapted for industry-specific purposes, validated in different organizational contexts or dismantled to investigate specific success factors.