Big data analytics (BDA) is deﬁned as a holistic approach to managing, processing and analyzing the 5V data-related dimensions (i.e. volume, variety, velocity, veracity and value) to create actionable insights for delivering sustained value, measuring performance and establishing competitive advantages (Fosso Wamba et al., 2015). BDA has captured the imagination of both practitioners and scholars for its high operational and strategic potentials across various industries including marketing, financial services, insurance, retailing, healthcare and manufacturing. For example, manufacturing firms including GE, Rolls Royce and Ford have been successfully using BDA for maintenance (e.g. engine failures) and supplier risk management (Jobs et al., 2015). BDA has also improved business intelligence on the behaviour of customers as well as consumer profiling (European Commission, 2013). As such, the extant literature identifies big data as the “next big thing in innovation” (Gobble, 2013, p. 64), “the fourth paradigm of science” (Strawn, 2012), or “the next frontier for innovation, competition, and productivity” (Manyika et al., 2011, p. 1).