Data Governance: The Structural Framework and Functional Mechanism of Academic Credit Bank Data Labeling

Taking the Shanghai Academic Transfer and Accumulation Bank for Lifelong Education (SHCB) as an example, this discussion explores the structural framework and functional mechanism of data governance in academic credit banks, emphasizing the construction and application of the data label management platform. As a crucial platform for promoting lifelong learning, academic credit banks encounter numerous challenges in data governance, including diverse data sources, data entry errors and omissions, and lack of unified data standards. To address these, the study emphasizes the importance of data governance in data labeling, aiming to enhance data quality, security, and facilitate sharing and utilization through a comprehensive label management system. Initially, the research reviews the background and theoretical foundations of data governance and academic credit banks. It provides a detailed overview of the data label management platform's construction, including data cleaning, processing, modeling, and label definition. Adhering to the MECE principles, the platform ensures label independence and comprehensiveness, efficiently managing labels through automation and manual methods. Furthermore, the study examines the platform's mechanisms in data quality governance, security assurance, and sharing and utilization. Results indicate that the data label management platform significantly improves data accuracy, consistency, and security of academic credit banks. It promotes efficient data management and utilization, thereby providing a solid foundation for the healthy development of academic credit banks.