Cognitive Data Fabric: A Foundation Model Architecture for Self- Organizing Enterprise Knowledge, Autonomous Schema Evolution, and Zero-Query Insight Discovery in Retail Analytics

Abstract

Enterprise retail analytics is increasingly limited by fragmented knowledge, unstable schema relationships, and the dependence of conventional intelligence systems on explicit user queries. Data fabric, knowledge graph, and foundation-model research has shown that enterprise intelligence improves when distributed data assets can be semantically aligned, adapt to structural change, and support richer representational reasoning across structured and mixed-source information. The main gap is the lack of architectures that unify self-organizing knowledge formation, autonomous schema evolution, and zeroquery insight discovery within one retail analytics framework. This matters because many high-value retail patterns remain undiscovered until analysts explicitly search for them, by which time operational impact may already be visible. This article presents Cognitive Data Fabric, a foundation model architecture that continuously organizes enterprise retail knowledge, absorbs semantic drift through autonomous schema adaptation, and surfaces proactive insights without requiring manual query initiation. The results show improvements in knowledge graph coherence, schema adaptation quality, semantic alignment, and cross-source linkage stability across update cycles, while also increasing zero-query insight discovery across multiple retail insight categories. The study demonstrates that a cognitive data fabric can provide a stronger foundation for semantically adaptive and proactively intelligent retail analytics.