Enterprise Data Models have often been associated with large, complex, multi-year initiatives.
That reputation has led some organizations to question whether enterprise modeling is still practical in a world of rapidly evolving platforms, distributed data ownership, agile delivery, and AI.
But the underlying problem Enterprise Data Models were designed to solve has not disappeared.
Organizations still need to agree on the meaning of important business concepts. Customer, Product, Revenue, Account, Employee, and other core terms often appear across multiple systems and domains. When those definitions diverge, the impact can spread into reporting, governance, integration, analytics, and AI.
In this on-demand webinar, Donna Burbank challenges common misconceptions about Enterprise Data Models and explores a more pragmatic approach.
Rather than attempting to model the entire organization before delivering value, teams can develop an Enterprise Logical Data Model incrementally—starting with high-value concepts, domains, and use cases and expanding over time.
Drawing on real-world project experience, Donna explains what successful Enterprise Data Model programs do differently, where teams often struggle, and how a shared logical model can provide consistent business meaning across the enterprise.
This on-demand webinar is designed for enterprise data architects, data modelers, information architects, governance leaders, metadata professionals, analytics leaders, data engineering teams, AI stakeholders, Chief Data Officers, enterprise architects, and organizations looking to create more consistent business meaning across their data environment.
Donna Burbank
Managing Director
Global Data Strategy
Watch this recorded session to learn how to make Enterprise Data Modeling practical, incremental, and relevant to modern data architecture, governance, analytics, and AI.
An Enterprise Data Model provides a shared representation of important business concepts and their relationships across an organization. It can help teams establish consistent definitions independent of individual systems or applications.
An Enterprise Logical Data Model describes business entities, attributes, definitions, and relationships without tying them to a specific physical database implementation.
The webinar challenges that assumption. While traditional approaches may have been overly ambitious, the need for shared business definitions remains relevant across modern architecture, governance, analytics, and AI.
No. Organizations can take an incremental approach by prioritizing important business domains, concepts, or use cases and expanding the model over time.
A shared model can provide consistent definitions and relationships that governance teams can use when managing terminology, metadata, ownership, policies, and standards.
Analytics depends on consistent interpretations of business concepts and metrics. Shared definitions can reduce ambiguity and help teams understand how information relates across systems and domains.
AI systems require context about enterprise concepts and relationships. A consistent logical model can provide structured business meaning that supports broader AI and semantic initiatives.
The session is useful for data architects, enterprise architects, modelers, governance teams, data leaders, analytics professionals, and anyone responsible for creating consistent definitions across enterprise data.
Connect with our team to explore how ER/Studio can support Enterprise Logical Data Modeling, metadata management, governance, and modern data architecture initiatives.
