Authors: Thomas Ward, Patrick Simmons, Samuel Price, Nicole Bailey, Chaitanya Srinivas, Aneesha Raj
Abstract: Enterprise data modeling plays a critical role in enabling organizations to effectively manage and integrate vast amounts of data across large-scale information systems. As enterprises increasingly rely on distributed architectures, cloud platforms, big data technologies, and heterogeneous data sources, ensuring data consistency, scalability, interoperability, and governance has become a significant challenge. This paper presents a comprehensive framework for enterprise data modeling that integrates conceptual, logical, and physical data models with metadata management, master data management, data governance, and data quality practices to establish a unified and scalable data architecture. The proposed framework supports modern enterprise technologies, including data warehouses, data lakes, distributed databases, microservices, and hybrid cloud environments, while facilitating seamless data integration and standardized information exchange across business domains. It further incorporates governance-driven policies, schema evolution mechanisms, security controls, privacy protection, and regulatory compliance to ensure sustainable data lifecycle management. Additionally, artificial intelligence-assisted metadata enrichment, automated schema discovery, and intelligent data validation techniques are integrated to enhance modeling accuracy, reduce manual effort, and improve operational efficiency. The framework enables organizations to build resilient and flexible enterprise data ecosystems capable of supporting high-volume transactional processing, real-time analytics, and data-driven decision-making. Overall, the proposed approach improves data quality, enterprise interoperability, business intelligence, and organizational agility while providing a practical foundation for digital transformation initiatives and next-generation enterprise information systems.
DOI: http://doi.org/10.5281/zenodo.21428282
