Real-Time Enterprise Data Harmonization Using Graph Neural Networks for Cross-System Integration and Customer Intelligence

Abstract

Enterprise organizations increasingly operate within highly distributed digital environments that integrate enterprise resource planning platforms, customer relationship management systems, human capital management solutions, cloud-native analytics stacks, and real-time streaming infrastructures. This architectural fragmentation introduces persistent challenges in achieving consistent, accurate, and continuously synchronized representations of enterprise entities and customer identities. This study proposes a novel framework for real-time enterprise data harmonization using graph neural networks to enable adaptive cross-system integration and scalable customer intelligence generation. The proposed approach models heterogeneous enterprise datasets as dynamic relational graphs, capturing both structural dependencies and evolving semantic relationships across operational systems. By embedding real-time ingestion pipelines, streaming graph construction, and iterative graph neural network inference, the framework supports continuous entity resolution, contextual attribute alignment, and trust-aware data consolidation. Empirical evaluation across representative enterprise integration scenarios demonstrates substantial improvements in harmonization precision, latency reduction, and downstream analytical enrichment when compared to traditional deterministic matching and probabilistic reconciliation techniques. Furthermore, the framework introduces a governance-oriented feedback loop that ensures traceability, auditability, and policy-conformant data propagation across interconnected systems. The findings establish graph neural networks as a foundational paradigm for next-generation enterprise integration architectures, enabling organizations to achieve resilient interoperability, enhanced customer intelligence, and sustained data quality in real-time operational contexts.