In the context of today’s rapidly developing environment, improving management effectiveness is one of the key tasks not only for public institutions but also for all types of organizations. The need for scientific research in this field has been steadily increasing in order to ensure continuous improvement of management systems. This study proposes a data-driven decision-support model for evaluating managerial performance in local government based on KPI systems and statistical analysis. This article empirically analyzes the factors affecting the effectiveness of managerial staff performance in local government. The study was conducted using the case of the Karshi district administration, and data were collected through a social survey method. The obtained data were processed using the SPSS statistical software through descriptive statistics, factor analysis, and Pearson correlation analysis. The results of the study identified the main factors that negatively affect management effectiveness. During the research process, the central importance of certain KPI indicators was statistically confirmed. Based on the findings, practical recommendations were developed to improve the system for evaluating the performance of managerial staff in local government. The research results indicate that the tasks related to evaluating management effectiveness can be addressed through the implementation of modern and scientifically grounded methods. The combined application of strategic evaluation models such as the Balanced Scorecard and specific measurement tools such as KPI indicators enables an objective assessment of managerial performance. The proposed approach contributes to applied IT by formalizing KPI evaluation into a computational model and enabling its implementation as a digital decision-support system for public administration. This article is based on the author’s dissertation entitled “Improving the Evaluation of the Effectiveness of Management Activities of Managerial Staff in Local Government (Case of Karshi District Administration)” (2025, No. 312, Tashkent State Transport University).
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