Data Quality Issues and Their Impact on Business Analytics Results

This article explores the theoretical understanding of data quality in the context of business analytics, based on an interdisciplinary approach integrating data management, information economics, and systems analysis. Data quality is defined by its suitability for specific business processes through criteria of accuracy, completeness, consistency, currency, reliability, and coherence. Quality issues are analyzed, including technical, organizational, and procedural aspects, and comprehensive measures are proposed, such as the implementation of data management frameworks, the use of machine learning for validation, and corporate cultural transformation, emphasizing the need for synergy between technology, processes, and the human factor to minimize the negative impact on business results.