DATADRIVEN INFORMATION AND ANALYTICAL SUPPORT FOR GOVERNMENT REGULATION OF THE DIGITALISATION OF THE NATIONAL ECONOMY: ARCHITECTURE AND OPERATING MECHANISM

Анотація

The chapter substantiates the theoretical and methodological foundations of information and analytical support for data-driven state regulation of the digitalization of the national economy.It is argued that the increasing complexity, dynamism, and data intensity of modern economic systems necessitate a fundamental transformation of traditional regulatory approaches, shifting from fragmented and expert-driven decision-making toward a systematic, data-based governance paradigm.The concept of datadriven state regulation is defined as an integrated management model that ensures the formation, implementation, and adjustment of regulatory decisions based on structured data, analytical models, and formalized procedures for their interpretation.It is emphasized that the key distinguishing feature of this approach lies not merely in the use of data, but in the existence of a closedloop system that enables a consistent transition from data to regulatory impact through analytically grounded decision-making.The study systematizes existing scientific approaches in the field of data-driven governance, including evidence-based policymaking, decision support systems, indicator-based assessment models, and open data ecosystems.Despite the significant development of these areas, a critical gap is identified between analytical outputs and their transformation into concrete regulatory actions.This gap is conceptualized as a structural imbalance between the "data-analytics" and "decision-impact" stages of the regulatory process.To address this limitation, the chapter proposes a comprehensive architecture of information and analytical support, structured as a multi-level system that includes the data layer, indicator system, integral index, analytical modules, decision-making level, and regulatory impact level.The architecture ensures the integration of heterogeneous data sources, their transformation into meaningful indicators and aggregated indices, and their application within analytical procedures such as forecasting, scenario modeling, and impact assessment.Particular

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