Evaluating Data Quality: Comparative Insights on Standards, Methodologies, and Modern Software Tools

Abstract

In an era of exponential data growth, ensuring high data quality has become essential for effective, evidence-based decision making. This study presents a structured and comparative review of the field by integrating data classifications, quality dimensions, assessment methodologies, and modern software tools. Unlike earlier reviews that focus narrowly on individual aspects, this work synthesizes foundational concepts with formal frameworks, including the Findable, Accessible, Interoperable, and Reusable (FAIR) principles and the ISO/IEC 25000 series on software and data quality. It further examines well-established assessment models, such as Total Data Quality Management (TDQM), Data Warehouse Quality (DWQ), and High-Quality Data Management (HDQM), and critically evaluates commercial platforms in terms of functionality, AI integration, and adaptability. A key contribution lies in the development of conceptual mappings that link data quality dimensions with FAIR indicators and maturity levels, offering a practical reference model. The findings also identify gaps in current tools and approaches, particularly around cost-awareness, explainability, and process adaptability. By bridging theory and practice, the study contributes to the academic literature while offering actionable insights for building scalable, standards-aligned, and context-sensitive data quality management strategies.