A STUDY ON AI AND BIG DATA PRACTICES AND DATA-DRIVEN DECISION-MAKING

Abstract

The widespread proliferation of data analytics, involving big data and artificial intelligence, is viewed to bring about radical changes in business offerings and operations. As a result, organisations continue investing heavily in data analytics technologies to gain competitive performance gains and enhance revenue growth. However, despite the hype and investments, most firms encounter significant challenges in fully harnessing the potential advantages of big data. Besides, while many firms initiate AI and big data projects, they often find it challenging to maintain them in production effectively. This situation tends to be much more pronounced for developing nations due to their less sophisticated computing infrastructure, poor data quality, and constrained budgets. Thus, compelled by the mixed outcomes of analytical initiatives, academicians and industry practitioners are concerned about understanding the necessary conditions and mechanisms under which these technologies create value for businesses. In this light, this study suggests diving deep into how human-intensive efforts, analytical activities, and processes contribute to value creation. In doing so, this study first conducts a qualitative study in the context of a developing country to explore barriers hindering the effective implementation and scaling of AI and big data artefacts. It presents explicit insights from fifteen managerial and technical executives from multiple industries. Analysis of the interview data revealed twenty-seven barriers across the three dimensions of technology, organisation, and environment. These barriers highlight complexities in AI and big data practices in emerging countries. Further, the findings from the qualitative study propose a comprehensive list of twenty-one solutions to overcome the identified barriers.