Enterprise transitions to SAP S/4HANA present significant data migration challenges, particularly when organizations must manage large volumes of transactional and master data across complex and heterogeneous system landscapes. As digital transformation initiatives accelerate, ensuring data integrity, migration speed, and business continuity has become a critical success factor in SAP modernization programs. Traditional migration approaches, including file-based Extract–Transform–Load (ETL) processes, Legacy System Migration Workbench (LSMW), and Business Application Programming Interfaces (BAPIs), often face limitations related to scalability, transformation complexity, reconciliation effort, and real-time synchronization requirements. This article examines SAP Landscape Transformation (SLT) as a comprehensive framework for object-based data migration, enabling selective, rule-driven replication with near-zero downtime capabilities during enterprise-wide SAP S/4HANA transitions. The study presents a structured five-phase implementation methodology encompassing discovery, configuration, build, mock migration cycles, and production cutover. In addition, the research analyzes SLT’s three-tier architecture, including Change Data Capture (CDC) mechanisms, transformation engines, and Mass Transfer ID (MTID) objects that support real-time replication and controlled migration execution. Evidence from a multi-country SAP S/4HANA transformation program in Latin America demonstrates that the SLT framework achieved throughput rates ranging from 25,000 to 40,000 records per hour for complex business objects, reduced cutover windows to less than 12 hours per country deployment wave, and improved master data completeness from below 80% to above 95%. Comparative evaluation against SAP BusinessObjects Data Services, SAP Migration Cockpit, and LSMW highlights SLT’s superior capabilities in handling real-time delta synchronization, selective object migration, and multi-wave deployment strategies. Furthermore, integration with SAP Master Data Governance (MDG) emerged as a critical quality multiplier, ensuring sustained data consistency, governance, and compliance beyond the migration lifecycle. The findings suggest that SLT provides a scalable, reliable, and governance-oriented approach for large-scale SAP S/4HANA transformation initiatives.
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