BRIDGING SYSTEMS IN M&A: A SCALABLE FRAMEWORK FOR DATA INTEGRATION AND LEGACY DECOMMISSIONING

Abstract

Post-merger integration of IT systems is one of the most complex and risk-prone aspects of M&A. Without a unified and scalable approach, organizations often face operational disruptions, technical debt, data redundancy, and compliance issues. This research presents a comprehensive framework to streamline data integration and legacy system decommissioning through phased, policy-driven strategies. The framework comprises four layers: (1) Integration Orchestration for API mediation, ETL, and real-time sync; (2) Data Governance to ensure lineage, consistency, and compliance; (3) System Rationalization enabling phased decommissioning through impact scoring; and (4) Monitoring and Audit for tracking, exception handling, and rollback. Built for modularity and interoperability, the framework supports hybrid integration — allowing legacy and modern platforms to coexist. It introduces two decision tools: Legacy Risk Index (LRI) and Integration Readiness Score (IRS) to guide priorities. A simulated M&A case involving mismatched ERP, CRM, and identity systems illustrates practical application. Effectiveness is assessed using metrics like data mapping accuracy, system redundancy reduction, mean time to integration (MTTI), and cost savings. The paper concludes with emerging trends such as AI-driven sequencing, event-based microservices, and zero-trust security for future M&A integrations.