Webcast : ER/Studio Enterprise Team Edition

Data model abstraction refers to simplifying and representing complex data structures and relationships within a data model. It allows stakeholders to focus on the essential aspects of the data without being overwhelmed by the details. Data model abstraction can be achieved through various levels of data models, such as conceptual, logical, and physical models, each providing a different abstraction. Data model abstraction is crucial for effective communication, alignment with business objectives, and adaptability to changes in requirements.

Conceptual data models offer a high-level overview of the dominant entities, their relationships, and key attributes, without specifying data types or storage details. Logical data models provide more detail on the structure of the data, including data types and constraints, while still abstracting away from the physical implementation. Physical data models represent the actual implementation of the data within a specific database or storage system.

Abstraction is all about designing your data model to support changing requirements. In this IDERA Live session, learn about abstraction through examples such as “Party” and “Document”. Determine whether or not to abstract or leave the terms in a more concrete state by applying the three critical questions covering: (1) Commonality, (2) Value, and (3) Effort. Learn the good and the bad of abstracting, and of course, the Star Wars connection. Lastly, see how IDERA’s harvesting tools can pull newly created terms and relationships between Terms from Logical Data Models to seed Business Glossaries, kick starting the creation of the Data Governance project.

Speaker: Steve Hoberman has been a data modeler for over 30 years, and thousands of business and data professionals have completed his Data Modeling Master Class. Steve is the author of nine books on data modeling, including The Rosedata Stone and Data Modeling Made Simple. Steve is also the author of Blockchainopoly. 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, creator of the Data Modeling Institute’s Data Modeling Certification exam, Conference Chair of the Data Modeling Zone conferences, director of Technics Publications, lecturer at Columbia University, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award.