Enterprise Data Architectures into a Unified and Secure Platform: Strategies for Redundancy Mitigation and Optimized Access Governance

Abstract

Enterprises accumulate heterogeneous data architectures over time as legacy systems, cloud-based applications, and departmental data marts expand without coordinated strategy. Fragmented data introduce redundant datasets, inconsistent semantics, and opaque access controls, resulting in elevated storage costs, delayed analytics, and compliance vulnerabilities. This research investigates strategies for unifying fragmented enterprise data environments into a cohesive, secure platform emphasizing reduction of redundant data storage, standardized metadata, and advanced access governance. Through a simulated test environment representing a mid-sized enterprise, data redundancy declined from an initial average of 42% to below 15%, average query latency improved from 3.8 seconds to 2.2 seconds, data quality scores rose from 71 to 91 out of 100, and alignment of access entitlements with documented policies reached 95% accuracy. The approach integrated semantic harmonization, deduplication techniques, virtualization layers, and role-based access control policies to establish a controlled but flexible environment. Results highlight the quantitative gains possible from systematic data unification, streamlined data lineage tracking, and consistent authorization mechanisms. Concluding analysis affirms the operational and compliance benefits of cohesive data architectures, enabling organizations to improve trust in analytics, reduce risk, and swiftly adapt to regulatory or market shifts.