Data Quality Issues and Their Impact on the Accuracy of Analytical Models
Abstract: This study examines data quality issues and evaluates their impact on the accuracy of analytical models in the context of the digital economy. Particular attention is given to defining data quality metrics (accuracy, completeness, consistency, timeliness, and relevance), analyzing the antecedents that contribute to quality degradation, and assessing the effect of these factors on the predictive performance of analytical systems. A literature review revealed the mechanisms through which data defects negatively affect the accuracy of analytical model forecasts. Additionally, integrated strategies for improving data quality were explored, including automated data cleansing, the use of modern data storage solutions, stream processing, and organizational oversight. The findings of this study have practical implications for optimizing data - driven decision - making processes and serve as a foundation for further empirical research in this field. The insights presented in this article will be of interest to researchers in machine learning, data analysis, and statistics, as they highlight critical relationships between the quality of input data and the accuracy of predictive analytical models. Furthermore, the results will be valuable for professionals in strategic management, information technology, and business analytics, providing theoretically grounded approaches for optimizing big data processing algorithms to enhance decision - making efficiency.
Keywords: data quality, analytical models, forecast accuracy, big data, artificial intelligence, machine learning, automated data cleansing
How to Cite?: Ansu Mathai Samuel, "Data Quality Issues and Their Impact on the Accuracy of Analytical Models", Volume 14 Issue 7, July 2025, International Journal of Science and Research (IJSR), Pages: 569-573, https://www.ijsr.net/getabstract.php?paperid=SR25701092036, DOI: https://dx.doi.org/10.21275/SR25701092036