Optimization of managerial decision-making through business analytics

this article explores the specific mechanisms of optimizing managerial decision-making by employing business analytics tools. Amid the radical complexity of the modern business environment and the overload of decision-makers with heterogeneous information flows, enhancing both the precision and timeliness of managerial actions becomes critically important. The relevance of this topic lies in the necessity of integrating business analytics not as an auxiliary function but as a strategic core that enables the identification of critical interdependencies and the prediction of complex organizational behavior. The aim of the study is to identify effective approaches for improving decision-making processes through the integration of analytical tools into the operational and strategic practices of enterprises. The scientific literature reveals significant divergences in the interpretation of BI system autonomy, in balancing algorithmic and expert-based procedures, and in assessing the applicability of analytical models to small and medium-sized enterprises. This paper systematizes both conceptual and applied approaches, outlines the main trajectories of analytical function transformation, and highlights emerging directions for the development of decision support systems based on hybrid models. The author argues that sustainable outcomes are achieved through the combination of adaptive analytical algorithms with configurable visual interfaces integrated directly into decision-making frameworks. A structured sequence of stages for building an analytical infrastructure is proposed, and areas requiring further academic inquiry are identified. This study is intended for mid- and senior-level managers, BI solution developers, and researchers specializing in management systems and corporate transformation.

analytical infrastructure, business analytics, digital management, decision support, hybrid models, information technologies, optimization