Top 10 Objectives
- Explain data modelling components and identify them on your projects by following a question-driven approach
- Demonstrate reading a data model of any size and complexity with the same confidence as reading a book
- Validate any data model with key “settings” (scope, abstraction, time frame, function, and format) as well as through the Data Model Scorecard®
- Apply requirements elicitation techniques including interviewing, artefact analysis, prototyping, and job shadowing
- Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions
- Practice finding structural soundness issues and standards violations
- Recognise when to use abstraction and where patterns and industry data models can give us a great head start
- Use a series of templates for capturing and validating requirements, and for data profiling
- Evaluate definitions for clarity, completeness, and correctness
- Leverage the Data Vault and enterprise data model for a successful enterprise architecture
This workshop focussed on the Data Modelling discipline within Data Management concerned with discovering, analysing, and documenting the concepts, relationships, constraints, and operations on data. It created a bridge for the non-technical, who isn’t going to be the one creating the agency-wide data models, having an understanding of the basic concepts help both data analysts and the business achieve the best results from data.
Steve Hoberman’s explains why data modelling is so important to understanding data and application development in his video.
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