Construction and Practice of a Governance System for High-Quality Enterprise Credit Datasets

To address the challenges of multi-source heterogeneity, insufficient timeliness, inconsistent quality, and scattered security management in enterprise credit data under high-timeliness business scenarios, this paper proposes a full-link real-time data governance framework integrating data quality, timeliness, security, and AI empowerment. The framework adopts a priority-based incremental multi-source fusion engine to achieve precise entity alignment and conflict resolution. Based on a Kappa hybrid architecture, it coordinates real-time streaming and enhanced batch processing to meet graded timeliness requirements from minute level to hour level. A unified data service layer with embedded security capabilities ensures compliant data supply. Taking the enterprise credit data platform of China Economic Information Service as a validation scenario, the practice shows that the constructed high-quality enterprise credit dataset covers more than 100 core dimensions (industry, commerce, judiciary, operations, etc.). The data processing cycle is shortened from T+1 to within 3 hours, and the accuracy of key data exceeds 99%. The framework provides high-reliability, high-timeliness, and high-security data support for financial credit, industry supervision, and business decision-making, and offers a reusable engineering methodology for the governance of similar high-quality datasets.