Year: 2025
Type: article
Abstract: Data migration is a critical component of banking mergers, where the seamless integration of disparate systems is essential to ensure operational continuity and customer satisfaction. This paper explores comprehensive strategies to achieve zero-downtime data migration in the context of banking mergers, addressing the unique technological and regulatory challenges faced by financial institutions. Key migration methodologies, including phased approaches, big bang strategies, parallel runs, and blue-green deployments, are analyzed for their applicability and effectiveness in various scenarios. Technical aspects such as data mapping, integrity validation, schema management, and performance optimization are discussed in detail, providing actionable insights for practitioners. The role of advanced tools, including AI-driven validation, middleware solutions, and real-time monitoring, is emphasized as essential for reducing risk and ensuring data consistency. Real-world case studies from prominent banking mergers, such as those involving Santander, Wells Fargo, and JPMorgan Chase, illustrate the practical application of these strategies. The paper also outlines best practices for planning, stakeholder engagement, testing, and post-migration optimization to ensure a successful transition. Finally, future research opportunities are identified, including the use of emerging technologies like blockchain and AI to further enhance migration processes. This study aims to serve as a comprehensive guide for financial institutions navigating the complexities of zero-downtime data migration.
DOI: 10.55041/ijsrem27522
Authors: Vice President, Lead Software Engineer, Vikas Kulkarni
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4407230781