Bibliometric analysis as a tool for analysing scientific production

Abstract

The growth of Big Data has emerged as a disruptive phenomenon that is revolutionising the manner in which both public and private organisations manage information. This new paradigm in understanding and utilising data has arisen in parallel with an insatiable demand for data storage capacity (Rico et al., 2021). This demand not only pertains to the ability to store vast quantities of data but also enables the consolidation of diverse informational units and the acceleration of real-time analysis, thereby enhancing the efficiency of business processes with the aim of achieving tangible results (Pasipuleti & Purra, 2017).

In light of this reality, the tools and methods traditionally employed for information processing and storage—particularly within the field of research—have become relatively insufficient to meet the demands imposed by Big Data (Hernández, Duque & Moreno, 2017). The use of conventional techniques, once well-established, has become obsolete in light of the increasing magnitude and complexity of contemporary data. Indeed, the volume of information generated globally continues to rise. According to the report by Reinsel, Gantz, and Rydning (2020), it is projected that by 2025, 175 zettabytes of data will be produced worldwide, representing an exponential increase compared to 2018. Within this context, efficient data management and analysis have become critical determinants of organizational success.