Data Analytics Systems and SME type - a Design Science Approach

Faculty/Professorship: Controlling 
Author(s): Becker, Wolfgang ; Ulrich, Patrick  ; Reitelshöfer, Eva; Fibitz, Alexandra; Schuknecht, Felix
Editors: Howlett, Robert J.; Toro, Carlos; Hicks, Julia; Jain, Lakhmi C.
Title of the compilation: Knowledge-Based and Intelligent Information & Engeneering Systems ; Proceedings of the 22nd International Conference, KES 2018, Belgrad, Serbia
Corporate Body: 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, 2018, Belgrad
Publisher Information: Amsterdam [u.a.] : Elsevier Ltd.
Year of publication: 2018
Pages: 1162-1170
Series ; Volume: Procedia Computer Science ; 126
Language(s): English
DOI: 10.1016/j.procs.2018.08.054
Although the current literature on large amounts of data and data analysis is growing rapidly, profound assumptions and practical tasks are still missing in some areas. This paper focuses on SMEs and their approach to data analysis issues and their implementation. With the methodology of design research we use characteristics of SME types and link them with the corresponding requirements of the compatible data analysis system to increase performance. We derive a framework that serves as a starting point for further research. The results indicate that there are several requirements that companies need to consider when realigning or restructuring their internal database. Depending on the type of SME, we choose a performance grid analysis based on the characteristics and requirements of data analysis systems.
Keywords: Design Science Research; Data Analytics Systems; Cycle; Big Data; Decision Making
Peer Reviewed: Ja
International Distribution: Ja
Type: Conferenceobject
Year of publication: 31. October 2018