Data Governance in Multi-Cloud Environments for Financial Services: Challenges and Solutions
Abstract
In the rapidly evolving landscape of financial services, multi-cloud environments are becoming increasingly popular due to their flexibility, scalability, and cost efficiency. However, the adoption of multiple cloud platforms brings significant challenges in maintaining robust data governance. These challenges include data fragmentation, inconsistent security policies, regulatory compliance complexities, and difficulties in ensuring data integrity and availability. Financial institutions must navigate these hurdles to protect sensitive information and meet stringent regulatory requirements. This article delves into the specific challenges faced by financial services firms when managing data governance across multi-cloud environments. We explore issues such as the lack of standardized data governance frameworks, the complexities of data synchronization across different cloud platforms, and the heightened risk of data breaches. Additionally, we address the difficulties in maintaining audit trails and ensuring real-time data visibility, which are critical for regulatory compliance and operational efficiency. To counter these challenges, we propose several effective solutions. These include implementing unified data governance frameworks that span across all cloud platforms, adopting advanced encryption and tokenization techniques to enhance data security, and utilizing AI and machine learning for real-time monitoring and anomaly detection. We also discuss the importance of continuous training and development for staff to stay updated with the latest data governance practices and technologies. Furthermore, we highlight the role of automation in streamlining data governance processes, reducing manual intervention, and minimizing human errors. By leveraging automated tools and platforms, financial institutions can ensure consistent policy enforcement, efficient data management, and robust compliance with regulatory standards.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 MZ Computing Journal

This work is licensed under a Creative Commons Attribution 4.0 International License.