Gartner predicts that by 2030, semantic layers will be critical infrastructure, right alongside your data platform and cybersecurity. Most data professionals aren’t yet sure what that means for them. The answer starts with a simple observation: the data problems you face every day are problems of meaning. Data silos, data disparity, integration complexity, data distrust, and confident-but-wrong AI answers may look like technical problems, but their root causes lie in inconsistent names, definitions, interpretations, and assumptions across systems, databases, and use cases. 

The Semantic Data Layers: Design and Implementation course addresses those root causes directly. A semantic data layer is, first and foremost, an architectural construct: a design approach that supports the representation, interpretation, and use of data with consistent meaning across systems and silos. 

Led by Dave Wells, a leading voice in data management and data architecture strategy, this two-day, live online course begins with the conceptual, logical, and architectural perspectives of semantics, including five kinds of semantic layers, business-oriented modeling of meaning, and building semantics into data architecture. From there, you’ll explore four practical implementation patterns and see how semantics are governed and applied to data governance, AI governance, and AI context. 

Through lectures, examples, and multiple hands-on breakout exercises, you’ll discover how to: 

• Trace common data management challenges to their semantic root causes 
• Model meaning with ontologies, taxonomies, and knowledge graphs 
• Place enterprise, domain, integration, enrichment, and consumption layers in your data architecture 
• Turn designs into working software with Metadata-Only, Graph-Based, Federation/Virtualization, and API/Contract-Based patterns 
• Govern semantics and apply them to data governance, AI governance, and AI grounding 

This course provides a practical framework for moving semantics from an abstract idea to a working part of your data management architecture. 

How the Course Works 

This two-day, live online workshop combines instructor-led sessions, Q&A, and four practical breakout exercises led by Dave Wells. Each segment builds on the last, moving from concepts and modeling on day one to architecture, implementation, and application on day two. 

All content and materials are hosted in the DATAVERSITY Training Center (DVTC), where you’ll also join the live Zoom sessions. 

Schedule: 

• Dates: Tuesday – Wednesday, June 8 - 9, 2027 
• Time: 11 AM – 6 PM Eastern / 8 AM – 3 PM Pacific 
• Length: 2 Days 
• CEUs: 12 

Pricing: 

• $1000 Early Bird through May 21, 2027 
• $1200 Regular Price after May 21, 2027 

Registration Deadline: Sunday, June 6, 2027 

During the live sessions, you’ll experience: 

• Interactive lectures covering semantic concepts, modeling, architecture, and implementation 
• Multiple breakout sessions: current state assessment, semantic data modeling, semantic data architecture, and implementation planning 
• Live Q&A and instructor feedback throughout 
• Continued access to recordings and resources after the course 

Program Highlights 

Over two days of immersive learning, you’ll: 

• Explore the what and why of data semantics, including ontology and taxonomy 
• Assess your organization’s current state across data management, data governance, and AI context 
• Learn the five kinds of semantic data layers and the architectural and implementation components of each 
• Practice semantic data modeling through concept, terminology, and taxonomy analysis 
• Rethink architecture around domains and data products and decide where each layer belongs 
• Compare four implementation patterns and how they combine in hybrid designs 
• Build an implementation plan with priorities, dependencies, and sequencing 

By the End of This Course, You’ll Be Able To: 

• Explain what data semantics is and the roles it plays in data management, data governance, and AI 
• Describe the architectural roles and purposes of semantic data layers 
• Distinguish the five kinds of semantic layers and the purpose of each 
• Determine where and how to place semantic layers in a data management architecture 
• Select among four implementation patterns to turn semantic layer designs into working software 
• Frame semantic governance and explain why semantics need to be governed 
• Apply semantic layers to data governance, AI governance, and AI contextualization 

Why Choose This Course 

• Led by Dave Wells, a respected authority on data management, analytics, and architecture 
• Connects semantics, data architecture, and governance in one cohesive learning experience 
• Covers both design and implementation, from modeling meaning to choosing a working pattern 
• Addresses emerging needs in AI governance and AI context and grounding 
• Four breakouts produce a current state assessment, semantic model, architecture, and implementation plan you can take back to your organization 

Is This Course Right for You or Your Team? 

This course is ideal for: 

• Data and enterprise architects designing for consistent meaning across systems 
• Data modelers, engineers, and analysts working with ontologies, taxonomies, or knowledge graphs 
• Data governance and AI governance professionals responsible for definitions, trust, and context 
• Managers and project leaders evaluating semantic layer initiatives or AI readiness 

No prior semantic modeling experience is required, just familiarity with organizational data and an interest in making data consistently understood and trusted. 

Private Team Training 

Organizations can host private two-day sessions to design semantic layers within their own architecture and data landscape. Corporate packages include instructor facilitation, customized examples, and group exercises. 

Contact us to explore corporate training options or bulk licensing. 

Backed by Industry Leaders 

Delivered through the DATAVERSITY Training Center (DVTC), this course brings together data architecture, data modeling, and governance practice around a single goal: consistent meaning. 

It equips professionals to treat semantics as critical infrastructure, building a foundation for integration, trust, and reliable AI.