Analytical tools and information processing technologies for business efficiency assessment

Abstract

In the contemporary business environment, analytics is increasingly essential for improving managerial efficiency, optimising resources, and supporting sustainable enterprise development amid rapid digitalisation and growing data volumes. This paper systematises analytical tools by functional purpose (typical, diagnostic, predictive, and evaluative) and by the degree of automation, contrasting manual approaches (e.g., spreadsheets) with automated Business Intelligence (BI) systems. It also outlines four levels of analytics—descriptive, diagnostic, predictive, and prescriptive—as a decision support logic that moves from recording facts to explaining causes, forecasting outcomes, and formulating recommendations. The study summarises key approaches to analytical data processing, including statistical methods (correlation, regression, factor and cluster analysis), economic-mathematical methods (optimisation, modelling, game theory, sensitivity analysis), and financial analysis (ratio analysis, DuPont model, trend and cash-flow analysis). Particular attention is given to BI platforms as integrative technologies that consolidate data from internal and external sources, automate aggregation, generate interactive dashboards, and enable real-time monitoring and predictive analytics. Overall, the paper argues that the combined use of analytical tools and BI technologies strengthens evidence-based management, increases organisational flexibility and competitiveness, and supports the transition toward a data-driven management model.