Collaboration for Big Data Analytics

Big data analytics (BDA) is increasingly transforming organizations by augmenting agility, innovation, and competitive performance. However, the potentials of BDA face several challenges, which are usually grounded in the data itself, in the related processes, and in the management of BDA. Research indicates that managerial issues even outweigh the technological issues, which draws attention to the human side of BDA. Indeed, there is evidence that the working relationship between data science experts and business users, who have to collaborate for BDA, is troubled which may impede BDA project success. This dissertation investigates how the human side of BDA can be characterized and managed, specifically fostering the relationship and collaboration between data science experts and business users. Building on four empirical research projects, it contributes to theory by transferring knowledge from a traditional research object (business IT alignment) to a current one, by finding theoretical explanations for observed phenomena, by revealing solution levers, and by detecting interrelations between different levels of analysis. For practitioners, this dissertation provides a toolbox of how to support collaboration for BDA. Moreover, it outlines cornerstones of a BDA change management program, and discovers promising organizational and technological arrangements to foster the human side of BDA.