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The Missing Variable in Big Data for Social Sciences: The Decision-Maker

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  • Michela Arnaboldi

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 20156 Milano, Italy)

Abstract

The value of big data for social sciences and social impact is professed to be high. This potential value is related, however, to the capacity of using extracted information in decision-making. In all of this, one important point has been overlooked: when “humans” retain a role in the decision-making process, the value of information is no longer an objective feature but depends on the knowledge and mindset of end users. A new big data cycle has been proposed in this paper, where the decision-maker is placed at the centre of the process. The proposed cycle is tested through two cases and, as a result of the suggested approach, two operations—filtering and framing—which are routinely carried out independently by scientists and end users in an unconscious manner, become clear and transparent. The result is a new cycle where four dimensions guide the interactions for creating value.

Suggested Citation

  • Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3415-:d:171959
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    References listed on IDEAS

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