Emergence and Evolution of ‘Big Data’ Research: A 30-Year Scientometric Analysis of the Knowledge Field

Abstract

In the ongoing ‘data revolution’, the ubiquity of digital data in society underlines a transformative era. This is mirrored in the sciences, where ‘big data’ has emerged as a major research field. This article significantly extends previous scientometric analyses by tracing the field’s conceptual emergence and evolution across a 30-year period (1993–2022). Bibliometric analysis is based on 17 data categories that co-constitute the conceptual network of ‘big data’ research. Using Scopus, the search query resulted in 70,163 articles and 315,235 author keywords. These are analysed aggregately regarding co-occurrences of the 17 data categories and co-occurrences of data categories with author keywords, and regarding their disciplinary distributions and interdisciplinary reach. Temporal analysis reveals two major development phases: 1993–2012 and 2013–2022. The study demonstrates: (1) the rapid expansion of the research field concentrated on seven main data categories; (2) the consolidation of keyword (co-)occurrences on ‘machine learning’, ‘deep learning’, ‘artificial intelligence’ and ‘cloud computing’; and (3) significant interdisciplinarity across four main subject areas. Scholars can use the findings to combine data categories and author keywords in ways that align scholarly work with specific thematic and disciplinary interests. The findings could also inform research funding, especially concerning opportunities for cross-disciplinary research.