Artificial intelligence (AI) depends on data, but having access to enormous amounts of data does not automatically mean an AI system understands what that data means. That distinction helps explain the relationship between two important components of modern AI applications: the data layer and the semantic layer. DATAVERSITY brought this topic to our attention in their article, “Why Your Semantic Layer Will Make or Break Your AI Strategy.“

The data layer is the foundation. It includes the databases, data warehouses, data lakes and other systems where structured and unstructured information is stored, organized and accessed. It answers fundamental questions such as: What data do we have? Where is it located? How is it formatted? How can applications retrieve it?

The semantic layer sits between raw data and the people or applications using it. Its purpose is to provide context and meaning. Through taxonomies, ontologies, knowledge graphs, metadata and relationships, the semantic layer helps systems understand that information may be connected even when it is stored differently or described using different terminology.

This is where the two layers intersect with AI.

An AI application might retrieve thousands of records from the data layer, but without sufficient semantic context, it can struggle to determine which information is relevant or how concepts relate. A semantic layer provides additional structure that can help AI interpret, connect and retrieve information more accurately.

Generative AI has made this relationship increasingly important. Retrieval-augmented generation (RAG), enterprise search, AI agents and knowledge-based assistants all benefit when reliable data is paired with clearly defined meaning and relationships.

The data layer provides the what. The semantic layer helps explain the what it means.

Neither replaces the other. Instead, their intersection creates a stronger foundation for AI applications that need to do more than simply locate information. As organizations move from experimenting with AI to integrating it into business processes, connecting reliable data with reliable meaning may ultimately determine how useful and trustworthy their AI systems become.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, where smarter AI starts with structured, meaningful, well-governed knowledge.