Data Modelling Masterclass 2014

Learning Objectives

Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side (including with NoSQL), through two case studies
Validate any data model through the Data Model Scorecard®
Apply requirements elicitation techniques including interviewing and prototyping
Practice finding structural soundness issues and standards violations
Recognize situations where abstraction would be most valuable and situations where abstraction would be most dangerous, and where industry data models can prove an important head start
Use a series of templates for capturing and validating requirements, and for data profiling
Express how to write clear, complete, and correct definitions
Leverage the Grain Matrix, Data Vault, and enterprise data model for a successful enterprise architecture