Designing Robust Data Governance Frameworks for Risk Management in Financial Data Ecosystems

Abstract

The increasing complexity and volume of financial data in 2020 demand robust data governance frameworks to ensure effective risk management. This study investigates the design of comprehensive governance models tailored for financial ecosystems, considering regulatory compliance, data quality, and security imperatives. Employing a qualitative research methodology, we synthesized insights from academic literature, industry practices, and case studies. The analysis reveals that a structured framework, incorporating clear ownership, data stewardship, compliance monitoring, and advanced analytics, is crucial for minimizing financial and operational risks. Significant challenges, including organizational resistance and technological constraints, were identified. The findings contribute to both academic discourse and practical strategies, emphasizing the need for dynamic, adaptive governance models that align with the fast-evolving financial landscape.