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Think big with big data

Identifying suitable big data strategies in corporate environments
: Ebner, Katharina; Bühnen, Thilo; Urbach, Nils


Sprague, Ralph H. (Ed.) ; Univ. of Hawaii at Manoa, Shidler College of Business, Honolulu/Hawaii; IEEE Computer Society:
47th Hawaii International Conference on System Sciences, HICSS 2014. Vol.5 : Waikoloa, Hawaii, USA, 6 - 9 January 2014; Proceedings
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-2505-6
ISBN: 978-1-4799-2504-9
Hawaii International Conference on System Science (HICSS) <47, 2014, Waikoloa/Hawaii>
Conference Paper
Fraunhofer FIT ()

Businesses increasingly attempt to learn more about their customers, suppliers, and operations by using millions of networked sensors integrated, for example, in mobile phones, cashier systems, automobiles, or weather stations. This development raises the question of how companies manage to cope with these ever-increasing amounts of data, referred to as Big Data. Consequently, the aim of this paper is to identify different Big Data strategies a company may implement and provide a set of organizational contingency factors that influence strategy choice. In order to do so, we reviewed existing literature in the fields of Big Data analytics, data warehousing, and business intelligence and synthesized our findings into a contingency matrix that may support practitioners in choosing a suitable Big Data approach. We find that while every strategy can be beneficial under certain corporate circumstances, the hybrid approach - a combination of traditional relational database structures and MapReduce techniques - is the strategy most often valuable for companies pursuing Big Data analytics.