Getting real about Big Data: Applying critical realism to analyse Big Data hype

Purpose: – An emerging application of Big Data is the addition of sensors and other micro‐electronic devices to engineer‐to‐order (ETO) goods such as one‐of‐a‐kind buildings and ships. The addition of micro‐electronic devices can enable the setting up and operation of smart buildings and smart ships. The purpose of this paper is to provide a critical realist analysis of Big Data hype. This is necessary to determine what challenges will need to be met before project businesses can achieve informational effects and transformational effects from Big Data technologies. Design/methodology/approach: – A critical realist study informed by reference to predictive theory and findings from action research. The predictive theory is concerned with the three different types of business effects that can come from information and communication technologies (ICTs): automational, informational, and transformational. Findings: – Critical realist analysis reveals that hype about Big Data underplays many challenges in achieving informational and transformational effects. Practical implications:
– Many inter‐related non‐trivial factors need to be taken into account when considering investing in Big Data initiatives. These factors range from the planning of data sampling rates, through the robust fixing of sensors, to the implementation of data mining algorithms and signal models. Originality/value:
– The originality of this paper is that critical realism is used in analysis of Big Data hype. The value of this paper is that it reveals a causal mechanism and causal context for project business Big Data application. This type of critical realist analysis can be applied to enable better understanding of necessary causal mechanisms and causal contexts for other ICT innovations.