Abstract
Business intelligence (BI) and data analytics are crucial tools that organizations can leverage to improve their business operations, decision-making processes, and overall performance. These two disciplines involve collecting, analyzing, interpreting, and presenting data to provide valuable insights and inform strategic actions. This study aims to explore how BI and data analytics can leverage business. Provide organizations with the tools to extract valuable insights from their data, leading to better decision-making, operational efficiency, customer satisfaction, and competitive advantage. Integrating these disciplines into business processes can drive growth, innovation, and success in today's data-driven business landscape. Based on the above, it is intended to systematically review the bibliometric literature on how business intelligence and data analytics can leverage business in the Scopus database with the analysis of 75 academic and/or scientific documents.
TopMethodological Approach
The researcher conducted a bibliometric systematic literature review to identify relevant sources and synthesize findings on how businesses use BI and data analytics. This methodology provides a robust approach to comprehensive and structured analysis of existing research.
Given the rapidly evolving nature of BI and data analytics in today's data-driven business environment, this methodology allows for the systematic identification and analysis of a vast body of literature. It allows the researcher to identify key trends, emerging themes, and the evolution of research in this field, enabling practitioners to gain insights into the current state of knowledge, research gaps, and future directions.
The bibliometric systematic literature review (LRSB) provides a comprehensive overview of the academic landscape by systematically identifying, collecting, and analyzing relevant academic publications, thereby allowing the researcher to identify key trends, emerging research themes, and gaps in the literature.
In contrast to conventional literature reviews, LRSB uses a replicable, scientific, and transparent process that thoroughly searches for both published and unpublished literature on the study topic (Rosário & Dias, 2023a, b, Rosário et al. 2023). Additionally, the researcher offers an audit trail that enables readers to evaluate the research's procedures, conclusions, and the quality of the synthesized studies.
In order to verify the veracity and accuracy of the data presented, the LRSB screens and chooses information sources in a process that consists of three phases and six steps (Rosário, & Dias, 2023a, b; Rosário et al., 2023), (Table 1).
Key Terms in this Chapter
Data-Driven: A programming paradigm in which program instructions describe the data to be matched and the processing required, rather than defining a sequence of steps to be performed.
Big Data: Is an area of knowledge that studies how to treat, analyze and obtain information from very large data sets.
Business Intelligence: Refers to capabilities that enable organizations to make better decisions, take informed actions, and implement more-efficient business processes.
Key Performance Indicators: They are management tools for measuring and measuring the resulting level of performance and success of an organization or a certain process, focusing on the “how” and indicating how well the company's processes are doing, allowing its objectives to be achieved.
Machine Learning: Focused on building computer systems that learn from data.
Artificial Intelligence: Multidisciplinary field of study that covers several areas of knowledge.
Internet of Things: Is a concept that refers to the digital interconnection of everyday objects with the internet, the connection of objects more than people.
Data Analytics: Is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing lessons, and supporting decision-making.