Real-Time Enterprise Data Harmonization Using Graph Neural Networks for Cross-System Integration and Customer Intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it