Data Quality Dimensions and Practical Applications in Data Governance

In the era of digital economy and data-driven decision-making, data quality hasbecome the fundamental pillar of effective data governance. Poor data quality maylead to misleading strategic judgments, increased operational risks, regulatoryviolations, and unnecessary economic costs, while high-quality data withstandardized governance can support reliable business analysis, compliant regulatoryreporting, and sustainable value creation for organizations. Based on the classic dataquality theoretical framework proposed by Wang / Strong (1996) and the industryauthoritative standards including DAMA-DMBOK2, IBM data governance guidelinesand ISO 8000 data quality series, this paper takes the author’s patent-pending CVA /UTC Color-Coded Data Quality Framework (patent application accepted by the ChinaNational Intellectual Property Administration) as the core research object,systematically sorts out six core data quality dimensions, and innovatively classifiesthem into two interrelated categories: single-record oriented dimensions (CVA) andcross-system oriented dimensions (UTC) with warm and cool color coding. The paperelaborates on the exclusive color mapping, memory rules and practical attributedefinition of each dimension in the framework, and further verifies the applicabilityand operability of the framework through eight typical application scenarios coveringfinancial services, professional services, public administration and industrialoperations. By integrating theoretical definitions with practical applicationverification, this study demonstrates the application value of the patent-pending CVA/ UTC Color-Coded Data Quality Framework as a measurable indicator systemthroughout the entire data governance lifecycle. This research not only clarifies theinternal logic of data quality management based on the original framework, but alsoprovides a structured and visual learning and application tool for practitioners andcertification candidates in the data governance and data management field. Theresearch aims to enhance the conceptual clarity of data quality dimensions, improvethe practical application ability of the framework, and lay a foundation for thepopularization and application of this original patent achievement in modernorganizational data governance.