Data Quality Governance in the Financial Industry: Challenges, Frameworks, and Practical Implementation

In recent years, national regulators have increasingly tightened business, technology, and data supervision for financial institutions, issuing guidelines such as the "Commercial Bank Internal Control Guideline" and the "Banking Technology Risk Management Guideline" that raise data quality requirements.

The article outlines five key aspects of data quality governance in finance: background of data quality control, challenges faced, construction of a data quality management system, data‑driven governance practices, and data‑asset quality assessment.

1. Data Quality Control Background

Financial institutions are driven by both external regulatory demands and internal needs for accurate data, prompting the adoption of stricter standards and compliance checks.

Regulatory documents emphasize data quality as a primary driver, and recent incidents of large banks suffering losses due to poor data quality highlight the urgency of robust governance.

2. Current Problems

Common issues include unclear strategic goals, ambiguous organizational structures, insufficient leadership attention, incomplete data, and mismatches between data services and business changes.

These problems lead to long resolution cycles, high difficulty, numerous quality issues, unclear responsibility, time‑consuming root‑cause analysis, and insufficient emphasis on data quality.

3. Data Quality Management System Construction

3.1 DAMA Framework

The DAMA framework places data quality at the core of data management, providing theoretical guidance and practical methods for implementation.

3.2 Organizational Structure

A four‑layer structure—management, business, technology, and execution—clarifies roles and responsibilities across the bank.

3.3 Management Methodology

Based on the PDCA (Plan‑Do‑Check‑Act) cycle, the methodology defines planning, rule setting, issue detection, remediation, and continuous improvement.

3.4 Management Modes

Overall planning and periodic assessment mode (waterfall‑style, long‑term).

Special‑governance and rapid execution mode (targeted, regulatory‑driven).

User‑driven mode (bottom‑up problem discovery and resolution).

4. "Four‑Carriage" Approach to Solving Data Quality Issues

Planning (提术) : Prioritize issues using a quadrant model based on importance and difficulty.

Linkage (联术) : Leverage metadata, data lineage, standards, and master data to improve overall quality.

Intelligence (智术) : Build knowledge bases for problem data and solutions to accelerate issue resolution.

Technology (技术) : Employ big‑data platforms such as Hive and Spark to handle large‑scale data quality tasks efficiently.

5. Data‑Asset Quality Evaluation

The China Quality Certification Center evaluates data assets on six dimensions: normative, completeness, accuracy, consistency, applicability, and economics, issuing certification for compliant enterprises.

Empirical results show that organizations that conduct data‑asset inventories, governance, and quality management achieve higher scores and certification rates.

6. Practical Data Governance Practices

Data‑driven quality management uses model‑generated validation rules, lineage analysis, and metadata construction to detect and resolve issues.

Master data management standardizes fields across systems, enabling consistent quality checks.

Advanced monitoring techniques—including intelligent composition, analysis, distribution, and early‑warning—are applied to ensure end‑to‑end data quality.

Overall, the article provides a comprehensive view of data quality governance in the financial sector, offering frameworks, methodologies, and practical tools to enhance data reliability and support digital transformation.