Guoxin Securities Data Governance Serviceization: Frameworks, Practices, and Insights

Introduction – The presentation shares Guoxin Securities' approach to data governance, outlining its background, objectives, and the three‑part structure of the talk.

1. Data Governance Serviceization Background – Discusses the evolution of data governance in the securities industry, regulatory milestones since 2011, and the increasing emphasis on data quality, privacy, and compliance driven by Chinese regulators.

2. Vision and Goals – Defines the overarching vision of continuous data governance to protect data assets, and lists three concrete goals: regulatory compliance, privacy and data security, and trustworthy data through standards and quality.

3. Pain Points – Highlights typical challenges: recurring data issues across ~300 systems, low business participation, heterogeneous data models, and limited adoption of data‑asset platforms.

4. Service‑Oriented Data Governance Practice – Describes Guoxin's framework: a governance leadership group, six focus areas (data standards, metadata, architecture, security, quality, modeling), and three supporting platforms (data security operation, data control, data model platform) integrated with DevOps and JIRA.

5. Data Model Management – Explains the establishment of model standards, character‑set checks (UTF‑8), and tool‑driven validation of physical models across MySQL and SQL Server databases.

6. Data Quality Framework – Details system‑level and organization‑level quality management, issue tracking, work‑order flow, and monitoring across five categories (basic, indicator, table‑structure, cross‑system, task monitoring) with dashboards and scoring.

7. Data Standard Framework – Introduces the DG‑SDOM industry model, eight thematic standards, and thousands of indicator definitions supporting regulatory reporting and business analytics.

8. Metadata Framework – Covers the metadata collection system, its integration with standards and quality, impact analysis, and cross‑environment comparison to ensure consistency between production and test environments.

9. Data Security Framework – Outlines the security governance structure, technical controls (database audit, dynamic/static masking, privacy computing), and operational processes for risk monitoring and incident response.

10. Value of Service‑Oriented Governance – Summarizes four benefits: improved data consistency and accuracy, enhanced compliance, data‑asset monetization, and increased operational efficiency.

11. Summary and Outlook – Concludes with plans for DataOps adoption, quantitative data‑asset assessment, and standardization of governance practices to influence industry standards.