Information Noise in Marketing Analytics: Implications for Financial Reporting and Economic Decision-Making

Abstract

This study explores the phenomenon of information noise as a critical impediment to the accuracy and reliability of marketing analytics and economic decision-making in data-intensive environments. The research is economically oriented, addressing the adverse effects of informational distortions on the quality of forecasts, strategic planning, and the financial efficiency of market-oriented enterprises. Information noise is examined as a multidimensional construct that arises from data redundancy, irrelevance, ambiguity, contradictions, and temporal incon-sistencies. The paper develops a comprehensive methodological framework for identifying, measuring, and mitigating information noise through the integration of statistical analysis, mathematical modeling, semantic diagnostics, and artificial intelligence tools. The model reveals that trust in data sources, content redun-dancy, and channel fragmentation significantly affect the reliability of forecasts, highlighting the need for information quality control in fi-nancial planning. The findings emphasize the importance of structured data filtering, entropy-based anomaly detection, and adaptive noise management strategies for improving economic performance in digital markets.