Data Analytics Systems and SME type - a Design Science Approach

  • 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.

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Institutes:Fakultät Sozial- und Wirtschaftswissenschaften / Lehrstuhl für Betriebswirtschaftslehre, insbesondere Unternehmensführung und Controlling
Author:Patrick Ulrich, Wolfgang Becker, Alexandra Fibitz, Eva Reitelshöfer, Felix Schuknecht
Title of the journal / compilation (English):Knowledge-Based and Intelligent Information & Engeneering Systems ; Proceedings of the 22nd International Conference, KES 2018, Belgrad, Serbia
Editor:Robert J. Howlett, Carlos Toro, Julia Hicks, Lakhmi C. Jain
Publishing Institution:22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, 2018, Belgrad
Place of publication:Amsterdam [u.a.]
Publisher:Elsevier Ltd.
Year of publication:2018
Pages / Size:Seite 1162-1170
Collections:Procedia Computer Science ; 126
Keywords:Design Science Research; Data Analytics Systems; Cycle; Big Data; Decision Making
Document Type:Conference publications
Peer Review:Ja
Internationale Verbreitung:Ja
Release Date:2018/10/31
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International