Modern Business Intelligence: Big Data Analytics and Artificial Intelligence for Creating the Data-Driven Value

Abstract

The integration of Artificial Intelligence into Business Intelligence and cloud-native big data platforms represents a paradigm shift in how organizations process, analyze, and derive strategic value from vast data repositories. This article provides a comprehensive, technically rigorous examination of AI-powered Business Intelligence, charting the transition from traditional descriptive analytics to predictive and prescriptive intelligent systems. Grounded in foundational literature across automated machine learning, explainable artificial intelligence, real-time anomaly detection, and natural language processing, this analysis deconstructs the algorithms powering modern analytical features. The architectural underpinnings of cloud-native data platforms utilizing Medallion architectures and automated pipelines are thoroughly explored. Furthermore, the mathematical formulations of core capabilities—such as the Isolation Forest algorithm for multidimensional anomaly detection, SHapley Additive exPlanations for model interpretability, and Auto-Regressive Integrated Moving Average models for time-series forecasting—are detailed to explicate their successful operationalization in enterprise tools like Power BI and Tableau. The analysis concludes by quantifying the economic impact of these technologies, demonstrating how AI-augmented data platforms facilitate smart governance, democratize data access through cross-domain semantic parsing, and deliver substantial organizational returns on investment