Data Analytics Systems and SME type - a Design Science Approach
Faculty/Professorship: | Controlling |
Author(s): | Becker, Wolfgang ; Ulrich, Patrick ![]() |
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 |
Abstract: | 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 |
URI: | https://fis.uni-bamberg.de/handle/uniba/44647 |
Year of publication: | 31. October 2018 |

originated at the
University of Bamberg
University of Bamberg