Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature

Abstract

The importance of data science and big data analytics is growing very fast as organizations are gearing up to leverage their information assets to gain competitive advantage. The flexibility offered through big data analytics empowers functional as well as firm-level performance. In the first phase of the study, we attempt to analyze the research on big data published in high-quality business management journals. The analysis was visualized using tools for big data and text mining to understand the dominant themes and how they are connected. Subsequently, an industry-specific categorization of the studies was done to understand the key use cases. It was found that most of the existing research focuses majorly on consumer discretionary, followed by public administration. Methodologically, a major focus in such exploration is in social media analytics, text mining and machine learning applications for meeting objectives in marketing and supply chain management. However, it was found that not much focus was highlighted in these studies to demonstrate the tools used for the analysis. To address this gap, this study also discusses the evolution, types and usage of big data tools. The brief overview of big data technologies grouped by the services they enable and some of their applications are presented. The study categorizes these tools into big data analysis platforms, databases and data warehouses, programming languages, search tools, and data aggregation and transfer tools. Finally, based on the review, future directions for exploration in big data has been provided for academic and practice.