Deep Graph Learning for Autonomous Data Reconciliation Across Heterogeneous Enterprise Systems

Data integrity in modern enterprises is critically undermined by the fragmentation of heterogeneous systems, such as ERP, CRM, and custom applications. Traditional data reconciliation, which relies on static ETL rules, is brittle and fails to manage continuous process and schema drift, leading to costly data divergence. This paper introduces a novel Autonomous Data Reconciliation (ADR) framework based on Deep Graph Learning (DGL). We propose a method to model the entire enterprise data ecosystem, including SAP and Salesforce objects, as a dynamic Knowledge Graph. This graph is then monitored by a Graph Attention Network (GAT) trained to learn complex relational patterns and autonomously detect both structural and value-based anomalies in real-time. Our conceptual experiments demonstrate that this DGL-ADR framework achieves a superior F1-score for anomaly detection (0.93) compared to traditional rule-based (0.65) and tabular ML (0.79) baselines. The framework provides a robust, scalable, and proactive solution to ensure data integrity, thereby reducing operational risk and enabling reliable business intelligence.