Abstract
The article examines the theoretical foundations of business analytics as a key instrument for supporting managerial decision-making in the context of digital transformation and the increasing complexity of the business environment. The study systematizes contemporary scientific approaches to the interpretation of business analytics and substantiates its evolution from traditional reporting tools to an integrated information and analytical system that transforms corporate data into actionable knowledge for strategic, tactical, and operational management. The paper clarifies the conceptual essence of business analytics as an interdisciplinary system that combines data collection, integration, processing, analysis, modeling, forecasting, visualization, and interpretation. It is demonstrated that business analytics provides the informational basis for evidencebased managerial decision-making by enabling organizations to evaluate business performance, identify hidden patterns, forecast future developments, and justify managerial alternatives under conditions of uncertainty. Particular attention is devoted to the functional architecture of business analytics. The study systematizes four interrelated analytical levels—descriptive, diagnostic, predictive, and prescriptive analytics—and demonstrates that they form a continuous cycle of analytical support for enterprise management. A comparative analysis of Business Intelligence, Business Analytics, and Artificial Intelligence in Business reveals their complementary roles within a unified enterprise management framework. While Business Intelligence focuses on data integration and reporting, Business Analytics provides advanced analytical and forecasting capabilities, whereas Artificial Intelligence in Business enhances decision-making through intelligent algorithms, machine learning, natural language processing, and automated analytical support. The study argues that the convergence of business analytics with Artificial Intelligence, Generative AI, machine learning, cloud technologies, and Robotic Process Automation significantly expands the capabilities of enterprise analytical systems by enabling real-time data processing, predictive analytics, intelligent decision support, and continuous business process monitoring. It is concluded that such technological integration provides the methodological foundation for Data-Driven Management and the development of the Intelligent Enterprise, contributing to higher managerial effectiveness, organizational adaptability, sustainable competitiveness, and long-term enterprise resilience.