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Top1. Introduction
In the last 30 years, the tools and mechanisms for supporting organizational decision making have been intensively data driven (Chen et. al., 2012). More recently, the integration of data-inference mechanisms from statistics, machine learning, artificial intelligence, mathematics, optimization and databases have converged into a new dynamic business phenomenon (Kesavan & Kushwaha, 2020). An overwhelming amount of web-based, mobile, and sensor-generated data arrive at a terabyte and even exabyte scale and decision support information and insights can be obtained and derived from the highly detailed, contextualized, and rich contents of relevance to any business (Chen et. al., 2012; Mashingaidze & Backhouse, 2017; Ukhalkar et. al., 2020).
The term Big Data, accordingly, refers to data sets whose sizes are beyond the ability of common software tools to capture, curate, manage, process, analyze, and store within a specified elapsed time (Abbasi et al, 2016). Recent studies show that a majority of employers in the market believe that their organization’s need for Big Data skills and support tools will rise in the future (Bharadwaj et al., 2013; Wixom et al., 2013; Ukhalkar et. al., 2020). With the prominent value proposition, Big Data also brought big challenges for businesses and decision makers across all walks of life. More than often businesses are collecting more data than they know what to do with. Successful decision makers must be able to work with the data, make sense of it, and understand the big picture approach to using Big Data to gain insights (Willwhite, 2014; Asllani, 2015; Mashingaidze & Backhouse, 2017).
Due to Big Data, business executives and managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance (McAfee & Brynjolfsson, 2012; Hariri et. al., 2019). Modern business analytics has become their weapon of choice. Business Intelligence and Analytics (BI&A), for instance, have experienced significant growth over the past two decades and have been identified as one of the four major technology trends in the 2010s (IBM Tech Trends Report, 2011). Indeed, organizations have become more competitive through the use of business intelligence and modern analytics in this Big Data era (Asllani, 2015; Ukhalkar et. al., 2020). Motivated by the emerging opportunities and challenges as well as lack of practical transference of applying BI&A in the Big Data era, we conduct a selective review (Glass et al., 2004; Webster & Watson, 2002) on the BI&A evolution, applications, frameworks and emerging trends with the aim to provide a summary of core concepts, a succinct but valuable description of main applications and frameworks, and an account of main recommendations for addressing the Big Data challenges and opportunities. The results of this research can help BI&A researchers to count with an updated and integrative summarization of the evolution of the BI&A, and to executives and managers to count with a set of updated recommendations for coping with Big Data challenges and opportunities.
The remainder of this paper is structured as follows: a background overview of the Big Data challenges and opportunities for BI&A is depicted in section 2; the specification of the selective review research method as well as the research questions are explained in section 3; results and insights derived from the selective review are reported in section 4, including the BI&A evolution phases and their key characteristics from core studies, an investigation of BI&A applications and framework in the Big Data era, and a discussion of implications and insights for addressing the Big Data challenges for BI&A researchers and practitioners; and finally, in section 5, we conclude with the research limitations, recommendations, and conclusions.