Abstract
Big Data Analytics (BDA) usage in the industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision-making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in the organizational domain. Big Data has been defined in various ways and, the past literature about the classification of BDA and its capabilities is explored in this research. We conducted a literature review using PRISMA methodology and integrated a thematic analysis using NVIVO12. By adopting five steps of the PRISMA framework—70 sample articles, we generate five themes, which are informed through organization development theory, and develop a novel empirical research model, which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.
Highlights
Organizations today continuously harvest user data [e.g., data collections] to improve their business efficiencies and practices
The design review process is directed by the research question: what are the consistent definitions of Big Data Analytics” (BDA), unique attributes, objections, and business revolution, including improving the decision-making process and organization performance with BDA? The below table is created using the outcome of the search performed using Keywords- Organizational Big Data Analytics Capabilities (BDAC), Big Data, BDA (Table 3)
[59] “Big data analytics is defined as a process to analyze the large data volumes to capture value for the businesses and employees” (p. 229)
Summary
Organizations today continuously harvest user data [e.g., data collections] to improve their business efficiencies and practices. Significant volumes of stored data or data regarding electronic transactions are used in support of decision making, with managers, policymakers, and executive officers routinely embracing technology to transform these abundant raw data into useful, informative information. But one data-handling method, “Big Data Analytics” (BDA)—the application of advanced analytic techniques, including data mining, statistical analysis, and predictive modeling on big datasets as new business intelligence practice [1]—is widely applied. BDA uses computational intelligence techniques to transform raw data into information that can be used to support decision-making. Because decision-making in organizations has become increasingly reliant on Big Data, analytical applications have increased in importance for evidence-based decision making [2]. The need for a systematic review of Big Data stream analysis using rigorous
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