Authors: Gabriel Watson, Lily Henderson, Jacob Phillips, Chaitanya Srinivas, Aneesha Raj
Abstract: The increasing complexity of enterprise information systems, stringent regulatory requirements, and the rapid growth of distributed data environments have made data lineage and traceability essential components of modern data governance and compliance strategies. Organizations oper-ating across financial services, healthcare, telecommunications, government, and other highly regulated industries must demonstrate complete visibility into the origin, transformation, move-ment, and usage of enterprise data to satisfy regulatory mandates, support audit readiness, and ensure data integrity. This paper presents a comprehensive enterprise data lineage framework designed to strengthen regulatory compliance and organizational audit preparedness through integrated metadata management, automated data discovery, end-to-end traceability, governance policies, and intelligent monitoring. The proposed framework combines data lineage repositories, metadata catalogs, master data management, data quality validation, business process mapping, and role-based governance into a unified architecture that enables transparent tracking of enter-prise data throughout its lifecycle. Furthermore, the framework incorporates artificial intelligence and machine learning techniques to automate lineage discovery, identify inconsistencies, detect compliance risks, optimize metadata relationships, and improve impact analysis across complex enterprise ecosystems. Advanced security mechanisms, including encryption, access control, audit logging, and continuous compliance monitoring, are embedded throughout the architecture to protect sensitive information and support regulatory standards while enhancing organizational resilience. By integrating cloud-native technologies, real-time analytics, intelligent automation, and governance-driven processes, the proposed framework enables enterprises to improve operational transparency, accelerate audit activities, strengthen regulatory compliance, reduce business risk, and establish trusted, traceable, and high-quality data ecosystems that support sustainable digital transformation and informed organizational decision-making.
DOI: http://doi.org/10.5281/zenodo.21944476
