Financial Data is becoming a vital part of the financial institution's process when it comes to making credit decisions, monitoring transactions, regulatory reporting and business analysis. With the mounting volume of customer data, account data, transaction data, risk data and external market data, financial data governance issues have become more apparent, such as inconsistent standards, fluctuating data quality, disjointed data systems, and increased compliance requirements. Literature review and inductive analysis are used in this study to analyze the financial data governance framework and financial data quality evaluation. The authors conclude the study by asserting that financial data governance should encompass data access, standard unification, storage integration, data quality control, compliance security and application feedback. The stages are designed to solve issues like data sources being spread across, indicator definitions not being consistent, missing and duplicated data, and challenges with data use across systems and unclear security responsibilities. Data quality evaluation shouldn't just be about scoring results. Rather, it should focus on a variety of business use cases like credit approval, fraud detection, risk monitoring, and regulatory reporting, and should measure data quality across the dimensions of completeness, accuracy, consistency, timeliness, uniqueness, security, and traceability. The study identifies that financial data governance is still poorly coordinated between governance processes and quality evaluation indicators, as well as there are few evaluation indicators based on scenarios and feedback mechanisms are not complete. All financial future data governance should focus more on data issues in business applications, so that the results of data quality evaluation can be the basis for continuous improvement of data standard and system interface and data responsibility mechanism and data security management.

Jiaqing Zhang. Research on Financial Data Governance Framework and Data Quality Evaluation in the Big Data Environment. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 2: 51-60. https://doi.org/10.38007/IJBMET.2026.070206.

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