Neuro-Symbolic Data Warehousing: Bridging AI Reasoning with Enterprise Analytics

Abstract

The rapid adoption of artificial intelligence in the enterprise has dramatically changed data-driven decision-making. Nevertheless, traditional data warehousing designs mostly support statistical and descriptive analytics, with no facilities for symbolic reasoning or elicitable intelligence. This weakness makes it difficult to integrate the state-of-the-art AI reasoning with the enterprise analytics infrastructure. Neuro-symbolic artificial intelligence, which seeks to integrate neural learning with symbolic reasoning, is believed to be a promising solution for improving the capabilities of analytical systems through interpretability, logical inference, and knowledge representation. This paper discusses the meaning of neuro-symbolic data warehousing as a new construct that combines neural network learning models with symbolic reasoning processes within an enterprise data warehouse. The research paper explores the relevance of such architectures for improving enterprise analytics by providing explainable insights, enabling semantic querying, and supporting decision-making with knowledge. A conceptual architecture for neuro-symbolic data warehousing is proposed, with a discussion of the interaction among conventional data warehouse constituents, knowledge graphs, and AI reasoning engines. The paper also covers implementation issues, such as data integration, knowledge representation, system scaling, and governance. According to the findings, neuro-symbolic methods have the potential to significantly enhance enterprise analytics, making decision support systems more transparent, interpretable, and context-aware. Having machine learning and symbolic reasoning in data warehousing can help organizations achieve even more powerful analytical processing while preserving transparency and trust in AI-driven decision-making.