Conclusion
This paper provides an analysis of the existing academic literature in the domain of big data and cognitive computing. In this study, we have followed the approach of systematic literature review and we have used digital databases like Scopus, Web of Science and DBLP to extract the information. The knowledge base on big data analytics and cloud computing has been extended by some notable research studies such as (Irani, Ghoneim, & Love, 2006, 2017; Lal & Bharadwaj, 2016; Sivarajah, Kamal, Irani, & Weerakkody, 2017). The broad challenges of big data can be grouped into three main categories, based on the data life cycle: data, process and management challenges (Sivarajah et al., 2017). Verma and Bhattacharyya (2017) found that the major reason behind big data analytics non-adoption is that the firms do not realize the strategic value of big data analytics. Firm managers are also not prepared to bring the changes because of technological, organizational and environmental difficulties. On the other side relative advantage, security concern, top management support, technology readiness, competitive pressure and trading partners’ pressure were found as the critical drivers of cloud computing adoption (Lal & Bharadwaj, 2016) The review of prior literature shows that the domain of big data has been fairly explored but the domain of cognitive computing is in its nascent stages of development. As there has been an explosion in creation of data and thereby it is virtually impossible for a human to kept a tab on all the latest developments for decision making process.
