International Journal of Science, Engineering and TechnologyAuthors: Hiroshi Amano
Abstract: Data integration in enterprise systems plays a critical role in enabling seamless information flow across diverse applications, databases, and organizational units. In modern enterprises, data is generated from multiple heterogeneous sources such as legacy systems, cloud platforms, customer relationship management (CRM) tools, enterprise resource planning (ERP) systems, and external data services. This study examines the methodologies, architectures, and technologies used for effective data integration in enterprise environments. It explores key approaches such as Extract, Transform, Load (ETL), Extract, Load, Transform (ELT), data virtualization, and middleware-based integration. The research also highlights the importance of real-time data integration, data consistency, and interoperability in supporting business intelligence and decision-making processes. Additionally, it discusses challenges such as data silos, schema mismatches, data quality issues, latency, and security concerns. Emerging solutions including cloud-based integration platforms, APIs, microservices, and event-driven architectures are analyzed for their effectiveness in modern enterprise systems. The findings emphasize that robust data integration strategies are essential for achieving unified data access, improved analytics, operational efficiency, and digital transformation in organizations.
DOI: http://doi.org/
