Abstract
The amount of data to be produced and analyzed is increasing year by year. As a result, the concept of big data gained interest among researchers and practitioners. However, a plethora of challenges and potentials require the attention from researchers and practitioners to enhance the future development. Apart from the pure processing of the data and its occurring obstacles, also other dimensions need to be considered in this context. This includes the technical planning of the related systems as well as the human interaction with them. When it comes to the strategic design, development, deployment and use of big data systems, especially the aspect of potential issues is often underestimated and less researched. Hence, in this contribution a comprehensive investigation on the various dimensions of big data under a quality assurance perspective is performed. Consequently an overview about the current state of the art and promising solutions are presented, providing a foundation for the future work of practitioners and researchers.
Recommended Citation
Staegemann, Daniel; Volk, Matthias; Jamous, Naoum; and Turowski, Klaus, "Understanding Issues in Big Data Applications – A Multidimensional Endeavor" (2019). AMCIS 2019 Proceedings. 19.
https://aisel.aisnet.org/amcis2019/strategic_uses_it/strategic_uses_it/19
Understanding Issues in Big Data Applications – A Multidimensional Endeavor
The amount of data to be produced and analyzed is increasing year by year. As a result, the concept of big data gained interest among researchers and practitioners. However, a plethora of challenges and potentials require the attention from researchers and practitioners to enhance the future development. Apart from the pure processing of the data and its occurring obstacles, also other dimensions need to be considered in this context. This includes the technical planning of the related systems as well as the human interaction with them. When it comes to the strategic design, development, deployment and use of big data systems, especially the aspect of potential issues is often underestimated and less researched. Hence, in this contribution a comprehensive investigation on the various dimensions of big data under a quality assurance perspective is performed. Consequently an overview about the current state of the art and promising solutions are presented, providing a foundation for the future work of practitioners and researchers.