Enterprise Data Harmonization: A Multi-Layer Technical Architecture for Cross-Domain Integration

Abstract

Enterprises produce heterogeneous data in various technology stacks (e.g., relational databases, log files, sensors, and Environmental, Social, and Governance (ESG) disclosures), and while syntactical and structural heterogeneities are covered, semantic heterogeneities remain under-explored. We present an architecture for semantic harmonization, specified in terms of three formal layers. The architecture distinguishes schema mapping, entity resolution at the instance level, and data fusion from multiple data sources. Architecture-level ontology-driven hybrid matching pipelines, provenance-preserving conflict resolution, and adaptive feature engineering are used to enable analytically consequential operating risk management, financial forecasting, and regulatory compliance use cases by allowing predictive models to consume cross-domain feature combinations that are structurally inaccessible within siloed systems while still preserving the required provenance and conflict evidence for audit trail compliance and other high-importance analytic use cases. Innovations include modular ontology design, materiality-weighted Environmental, Social, and Governance (ESG) harmonization, schema on demand, and eventual consistency modeling for real-time analytics. The data architecture enabled a clear separation between a new form of cross-domain data harmonization and customary data warehousing, master data management, and data fabric platforms. The data is transformed from disparate operational, financial, and external data silos into semantically consistent analytic substrates for enterprise decision intelligence.