Business Intelligence Overview

Principles

Definition

Business intelligence is an academic field and methodological approach focused on leveraging data, technology, and analytical techniques to transform raw information into actionable insights that inform strategic and operational decision-making within organizations. It investigates the systems, processes, and practices necessary for collecting, integrating, analyzing, and presenting business data to improve performance and gain competitive advantage.

Ontological type

Core Methods

Data Infrastructure

Strategic Applications

Key developments

Key Figures

Knowledge-Driven Real-Time BI era

Hugh J. Watson [1], a leading figure in Knowledge-Driven Real-Time BI, worked with the University of Georgia [3] during this era. His contributions include articulating the current state of business intelligence [4] and detailing real-time BI best practices at Continental Airlines [5], which together charted a path for integrating analytics with enterprise knowledge and decision workflows in this era. Jeffrey Kreulen [2], whose work in this era spanned academic and industry settings, contributed to bridging business intelligence and knowledge management [6]. This integration of BI and knowledge management [6] helped establish knowledge flows and governance patterns that aligned analytics with organizational learning, addressing the knowing-doing gap central to Knowledge-Driven Real-Time BI.

Integrated BI Maturity era

Aleš Popovič [1] is an established scholar in this era with affiliations at Cardiff University [3] and Universidade Nova de Lisboa [4]. His research on maturity and culture in BI, as illustrated by the 2010 Conceptual Model of Business Value of Business Intelligence Systems [8] and the 2012 Towards business intelligence systems success: Effects of maturity and culture on analytical decision making [7], clarified how organizational readiness and governance shape analytical decision making and BI value, a finding central to why BI is treated as a repeatable organizational capability in the Integrated BI Maturity era. Raymond Y.K. Lau [2] is affiliated with City University of Hong Kong [5] and Queensland University of Technology [6], reflecting cross-institutional collaboration characteristic of this era. His 2015 paper Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix [9] contributes by clarifying how big data analytics can inform marketing mix decisions within BI, underpinning the analytics-driven decision support central to Integrated BI Maturity.

Industry 4.0 BI Maturity era

Robert Pellerin [1] is associated with Polytechnique Montréal [3] and Arts et Métiers [4] during Industry 4.0 BI maturity (2017-2023). Robert Pellerin [1] contributed with the paper Identification of critical success factors, risks and opportunities of Industry 4.0 in SMEs [7], highlighting critical factors and risks that drive analytics adoption, governance, and predictive process monitoring in SMEs. Aleš Popovič [2] is affiliated with Universidade Nova de Lisboa [5] and University of Ljubljana [6] during Industry 4.0 BI maturity. Aleš Popovič [2] authored Understanding the determinants of business intelligence system adoption stages [8], The role of compatibility in predicting business intelligence and analytics use intentions [9], and Justifying business intelligence systems adoption in SMEs [10], collectively clarifying adoption determinants, compatibility, and justification that support interoperable BI architectures and organizational absorptive capacity in this era.