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. In contrast, well-governed, high-qualitydata supports reliable analysis, compliant reporting, and sustainable value creationfor organizations. Based on the classic data quality framework proposed by Wang &Strong (1996), as well as widely accepted industry standards includingDAMA-DMBOK2, IBM data governance guidelines, and ISO 8000 data quality series,this paper systematically reviews six core data quality dimensions and classifies theminto two interrelated groups: single-record oriented dimensions and cross-systemoriented dimensions. Furthermore, this paper elaborates on eight representativeapplication scenarios that are commonly adopted in financial services, professionalservices, public administration, and industrial operations. By integrating theoreticaldefinitions with realistic practices, this study demonstrates how data qualitydimensions serve as measurable indicators throughout the data governance lifecycle.The research not only helps clarify the internal logic of data quality management butalso provides a structured learning framework for candidates preparing forprofessional certification examinations, especially those focusing on data governanceand data management competencies. The study aims to enhance conceptual clarity,improve application ability, and strengthen the understanding of how data qualitydimensions operate in modern organizational governance.