Data Quality and Master Data Management for Finance Data on Cloud

This paper explores the critical intersection of data quality and master data management (MDM) within cloud-based financial data systems. Financial institutions are increasingly migrating to the cloud for scalability and cost-efficiency, but this transition introduces challenges in maintaining data integrity and consistency. A robust data governance framework is essential to define roles, establish standards, and implement policies for data access and security. The paper examines best practices for data profiling, cleansing, validation, and monitoring, highlighting the importance of data security and privacy in the cloud. Furthermore, the study investigates MDM tools and technologies, emphasizing cloud-based solutions that offer data integration, cleansing, matching, and governance capabilities. Data quality metrics, including accuracy, completeness, consistency, timeliness, validity, and uniqueness, are discussed in the context of financial data. Integration strategies, such as real-time and batch data integration, data virtualization, and API-based integration, are also explored. The paper presents a comparative analysis of cloud tools, data quality metrics, and data size limits, along with case studies that illustrate the successful implementation of data quality and MDM initiatives in financial institutions. Future trends, such as AI/ML, blockchain, serverless computing, federated learning and data mesh architecture are also discussed.