Abstract
Big data analytics requires technologies to efficiently process large quantities of data. Moreover, especially in decision making, it not only requires individual intellectual capabilities in the analytical activities but also collective knowledge. Very often, people with diverse expert knowledge need to work together towards a meaningful interpretation of the associated results for new insight. Thus, a big data analysis infrastructure must both support technical innovation and effectively accommodate input from multiple human experts. In this chapter, we aim to advance our understanding on the synergy between human and machine intelligence in tackling big data analysis. Sensemaking models for big data analysis were explored and used to inform the development of a generic conceptual architecture as a means to frame the requirements of such an analysis and to position the role of both technology and human in this synergetic relationship. Two contrasting real-world use case studies were undertaken to test the applicability of the proposed architecture for the development of a supporting platform for big data analysis. Reflection on this outcome has further advanced our understanding on the complexity and the potential of individual and collaborative sensemaking models for big data analytics.
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Lau, L., Yang-Turner, F., Karacapilidis, N. (2014). Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective. In: Karacapilidis, N. (eds) Mastering Data-Intensive Collaboration and Decision Making. Studies in Big Data, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-02612-1_3
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