Enterprise AI systems can access enormous amounts of information, but access alone does not guarantee understanding.
Large language models, AI assistants, and intelligent agents still need context to understand what enterprise data represents, how important concepts relate to one another, and which definitions matter within a particular business.
Many organizations already have valuable portions of that context captured in conceptual and logical data models.
Data models describe entities, attributes, relationships, and definitions that represent how an organization understands its information. In the context of AI, these structures can begin to resemble what is often described as an ontology: a representation of important concepts and the relationships between them.
In this on-demand DM Radio discussion, host Eric Kavanagh and Jamie Knowles, Product Director at ER/Studio, explore why established data modeling practices are becoming newly relevant to Enterprise AI.
The session examines how Entity-Relationship modeling, metadata, and existing information architecture can help provide business context for LLMs and intelligent agents. Jamie also demonstrates how ER/Studio can help bridge traditional enterprise information architecture with emerging AI technologies.
Rather than starting an AI initiative from scratch, organizations may be able to build on models, metadata, and definitions they already trust.
This on-demand session is designed for data architects, enterprise architects, data modelers, data governance leaders, metadata professionals, AI and machine learning leaders, information architects, data engineers, analytics leaders, and technology executives exploring how existing enterprise information assets can support AI initiatives.
Eric Kavanagh
Host, DM Radio
ジェイミー・ノウルズ
Product Director, ER/Studio
Watch this recorded discussion to learn how data models, metadata, and established information architecture can provide the structured business context required for trustworthy Enterprise AI.
This webinar explores why data models, metadata, and ontologies are becoming increasingly important as organizations deploy LLMs, AI assistants, and intelligent agents.
An ontology represents important concepts within a domain and the relationships between those concepts. In enterprise environments, many elements of this business meaning may already be represented in conceptual or logical data models.
Data models describe business entities, attributes, relationships, and definitions. This structured information can help provide context that allows AI systems to better interpret enterprise data.
Metadata provides additional information about data, including definitions, meaning, ownership, relationships, and structure. This context can help connect raw enterprise information with business meaning.
Existing models can provide a useful source of business definitions and relationships. Rather than recreating this knowledge entirely for AI, organizations may be able to build on information architecture investments already in place.
The webinar argues the opposite: as AI systems consume more enterprise information, structured definitions and relationships can become increasingly valuable for helping those systems understand business context.
ER/Studio helps organizations create, manage, document, and share data models and related metadata. These capabilities can support efforts to connect established data architecture with modern AI initiatives.
The session is relevant to data architects, modelers, governance professionals, metadata teams, information architects, AI leaders, and anyone responsible for connecting enterprise data with AI systems.
Connect with our team to explore how ER/Studio can help your organization build on trusted data models, metadata, and information architecture as you develop Enterprise AI initiatives.
