Today's enterprises need unified semantic layers that seamlessly connect traditional BI, GenBI applications, and emerging AI workloads. As organizations face challenges in making enterprise data truly AI-ready, the focus must shift to creating universal semantic architectures that support current analytics and future knowledge graph capabilities.
In another installation of the new webinar series, Context Engineering for AI-Readiness: Unified Semantic Layers Unlocking Enterprise Data, brought to you by DBTA and Radiant Advisors—also sponsored by Strategy—John O'Brien, principal advisor and CEO, Radiant Advisors explained how this architectural approach is not just about data modeling; it is a critical necessity for enabling both business users and AI applications to access consistent, contextual enterprise data.
The Radiant Ascent Webinars explore modern enterprise data and analytics architecture, featuring monthly deep dives into emerging technologies and best practices. In each episode, O’Brien guides you through a new technical summit, unpacking complex architectural challenges and revealing proven strategies.
The semantic layer transforms complex data infrastructure into business-friendly interfaces, O’Brien explained. It integrates three core data components: metric store caching, vector databases, and knowledge graphs. It can act as a central business glossary ensuring shared understanding across all users and offer a single abstraction layer for both structured and unstructured data.
There are 7 requirements for building a universal semantic layer, this includes:
- Independence and loosely coupled: This offers a unified data experience with semantics shared across groups.
- Metrics store for data intelligence: Metrics Store sits between data warehouses and business applications as a single source of truth. It defines relationships between facts, dimensions and entities. It ensures everyone gets the same answers everywhere, every time.
- Caching for performance (intelligent) and Caching for modern OLAP: Intelligent and dynamically served metrics on demand. In-memory distributed caching is synchronized for accuracy.
- Semantic “similarity” for context: Vector databases handle multi-dimensional data points for semantic similarity. This is critical for embedding storage and retrieval in RAG systems. The vector database introduces vector embeddings and similarity search.
- Graph for relationships: Knowledge graphs capture relationships between entities for deeper context understanding. It enables reasoning over relationships and complex queries and links related information, providing richer context for AI responses.
- Robust APIs for access: This offers a unified data experience with semantics shared across groups.
- Secure enterprise data
“We’re evolving from data abstraction and unification, which solved distributed data problems,” O’Brien said. “Improving data accessibility is for personas and GenAI. Unified Semantic Layers should be independent of data persistence and data consumers.”
For the full webinar, you can view an archived version of the webinar here.