Geek Sync Webcast : ER/Studio Enterprise Team Edition

Subtyping is important for data modeling because it allows representing the generalization and specialization relationships between entities, improving communication and enforcing additional rules in conceptual and logical data models. This technique helps in creating more accurate and efficient data models by capturing the common attributes and behaviors of related entities, while also representing the unique characteristics and relationships of individual subtypes. Subtypes are essential for data modeling. They help capture the relationships between entities more accurately, reduce redundancy, improve data integrity, and enhance the flexibility and maintainability of the data model.

By using subtyping, data modelers can:

• Reduce redundancy: Subtyping allows for representing the shared attributes and relationships in a parent entity (that is, a super-type), reducing redundancy and simplifying the data model.
• Improve data integrity: By enforcing rules and constraints specific to each subtype, subtyping helps maintain data integrity and ensure accurate representation of the data.
• Enhance flexibility: Subtyping enables representing the unique characteristics and relationships of individual subtypes, allowing for greater flexibility and adaptability to changing business requirements.
• Improve maintainability: A well-structured data model with subtypes and supertypes can be easier to maintain and update, as changes to common attributes or relationships can be made in the parent entity without affecting the individual subtypes.

How often do you use subtyping on your data models? Subtyping is a fantastic tool for improving communication and for enforcing additional rules on your conceptual and logical data models. Learn the terminology of supertypes and subtypes, and explore how different modeling notations represent subtyping. The subtle variations in subtyping meanings that exist between Information Engineering (IE) and IDEF1X notations will be covered as well. Explore the use cases for subtyping and practice reading the subtyping structure during this fun and interaction session.

Speaker: Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conferences, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award.

A good data modeler should have the following characteristics:

• Strong communication skills: The ability to communicate effectively is important in working with supervisors, coworkers, and clients.
• Empathy: Great enterprise modelers have empathy for team members and partners and are able to work collaboratively with them.
• Attention to detail: Good data modelers have a keen eye for detail and are able to maintain data quality.
• Technical expertise: Data modelers should have a strong technical background and an understanding of database design and development.
• Analytical skills: Good data modelers should have the ability to analyze business processes and translate them into data models.