Skills 101: erwin

In this fast-paced Skills 101 session, discover how erwin’s portfolio accelerates AI initiatives and strengthens enterprise data readiness. If your organization is investing in AI but facing challenges around data trust, governance, or modeling complexity, this session will show how erwin connects the pieces to deliver trusted, AI-ready data.
We’ll explore:
• What’s new in erwin Data Intelligence 15, including automated AI model certification, enhanced data value scoring, and generative AI for streamlined stewardship
• How the unified erwin Data Management platform connects data quality, metadata, governance, and modeling into one AI-ready foundation
• AI-powered enhancements in erwin Data Modeler, including automated reverse engineering to reduce manual effort and improve accuracy
• Real-world use cases like building trust-scored data products and accelerating AI time-to-market
• How organizations leverage DI and DM together to reduce silos and scale AI with confidence

erwin Data Modeler has been evolving quickly. With an accelerated development cycle, erwin DM 15.0 was rapidly followed by 15.1 and 15.2, introducing enhancements focused on usability, visibility, and modeling efficiency. In this Skills 101 session, we’ll highlight some of the most impactful updates across the erwin DM 15.x releases and demonstrate the features that are already resonating with modeling teams and data architects.

What we’ll explore:
• Client UI enhancements that improve navigation and day-to-day modeling efficiency
• Data Modeler improvements that simplify modeling tasks and strengthen design workflows
• ER/studio 360 updates that expand visibility and collaboration around models
• erwin Mart and Portal enhancements for managing and sharing models across teams
• Conceptual modeling capabilities that help communicate data structures more clearly
• New AI-powered features designed to accelerate modeling and reduce manual effort

Whether you're already working in erwin DM 15.x or planning your upgrade, this session will highlight practical improvements that can help your team model faster and get more value from your data architecture.

Building robust models requires moving seamlessly between Conceptual, Logical, and Physical layers- and knowing how to maintain integrity across them. In this session, we’ll show you how to derive Logical models from Conceptual foundations, enrich them with attributes, and produce Logical/Physical models across multiple data structures. You’ll learn techniques for handling relationships, managing large models, and scaling design efforts for enterprise environments. By the end, your layered models will be as elegant and precise as a well-oiled DevOps pipeline.
Topics that will be discussed include:

• Designing Conceptual models that scale logically
• Deriving Logical models with attributes and relationships intact
• Transitioning to Physical models efficiently
• Handling multiple data structures without losing your mind

Speaker: Andy McGovern, Sr. Principal Consultant