Think Big with Big Data : Identifying Suitable Big Data Strategies in Corporate Environments

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Ebner, Katharina ; Bühnen, Thilo ; Urbach, Nils:
Think Big with Big Data : Identifying Suitable Big Data Strategies in Corporate Environments.
In: Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS). - Waikoloa, USA , 2014
ISBN 978-1-4799-2505-6

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Abstract

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 Map Reduce techniques – is the strategy most often valuable for companies pursuing Big Data analytics.