A Data-Driven Framework for Phased User Migration in Enterprise System Modernization

The process to modernize live enterprise systems requires vital operation to move active users between systems while ensuring operational continuity and ensuring all data is protected. The study shows that phased migration strategies are more successful than big bang strategies but existing research does not provide enough direction on effective user segmentation for various migration phases. The proposed study develops a databased user segmentation system which integrates four essential elements which include feature usage patterns and system complexity levels and data quantity and organizational risk assessment. The system incorporates a system for scoring users based on multiple criteria to advance users between migration stages using threshold values. The simulation with 500 synthetic enterprise users indicates that the complexity of integration has an impact on migration risk but weighting by revenue decreases business disruption. The research shows that thirteen percent of users require early phase migration but seventy three percent require standard migration phases and fourteen percent require special treatment due to their high risk status. The proposed solution links the technical migration models to organizational decision making using a solution that generates measurable and traceable and automated user segmentation for enterprise system modernization.