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 informa-tional 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. These distortions hinder the interpretability of marketing data, reduce the validity of econometric analysis, and contribute to suboptimal business decisions. 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. A particular focus is placed on the economic consequences of noise, such as resource misallocation, increased forecasting error, and de-creased return on marketing investments. The empirical section presents a correlation-regression model based on data from a real enterprise, which quantifies the impact of specific noise factors on sales forecast accuracy. 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. The study contributes to the theoretical conceptualization of information noise in marketing economics and offers practical recommendations for enhancing the quality of decision-making through intel-ligent data preprocessing. As a result, it provides a foundation for the development of more resilient and analytically sound business models in the context of digital transformation and informational complexity.
Iashchenko et al. (Wed,) studied this question.