The Dynamics of Knowledge Growth in Business Intelligence and Analytics: A Bibliometric Investigation

Abstract

Business Intelligence and Analytics (BI&A) has emerged over the last thirty years as a vital domain for driving organizational performance, digital transformation, and competitive advantage. As the field continues to expand rapidly, especially with the rise of AI-augmented analytics, understanding the structure and direction of existing research becomes critical. Without a clear map of intellectual progress and thematic shifts, both academic and practical efforts risk becoming disconnected and misaligned. This study provides a systematic bibliometric investigation of the evolution of knowledge within Business Intelligence and Analytics. The motivation behind this analysis is to bring clarity to a complex and growing body of literature and to identify strategic pathways that can inform future research, innovation, and practice. By uncovering the foundations, trends, and turning points in the field, we support a more informed and impactful development of analytics strategies across sectors. We examine a dataset of 2,361 peer-reviewed articles indexed in Scopus, applying a combination of citation network analysis, bibliographic coupling, co authorship exploration, and keyword co-occurrence mapping. Following the PRISMA guidelines and employing advanced text mining, we extract key thematic clusters and trace the emergence of influential research streams. The results reveal a strong progression from early technical focus to more integrated, strategic, and domain-specific applications. This transition highlights the importance of cross-disciplinary collaboration and the alignment of analytics with real-world business needs. By capturing the dynamic evolution of BI and Analytics, this study offers a robust foundation for scholars, industry leaders, and policymakers to align academic inquiry with technological advancement and organizational priorities.