The Australian Government Public Data Policy Statement requires Government entities to publish high-quality data that's is easy to use, with descriptive metadata and using agreed open standards.
Within data management, Data Modeling is concerned with discovering, analysing, and documenting the concepts, relationships, constraints, and operations on data. Even if you’re a 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.
The data model provides:
- the core vocabulary for communication between business and IT regarding the structure and semantics of data,
- the foundation of all data management disciplines including data governance, data architecture, database development (relational and NoSQL), data integration, data warehousing and data quality, and
- the necessary meta-data for sharing and using data within an organisation, for managing the data received by your agency, and for making open data useful.
Data models provide the conceptual backbone for all data management disciplines by which Open Data is published, linked and shared cost-effectively to support collaboration between public, private and research sectors.
For more of an explanation as to why data modeling is so important to understanding data and application development, please see Steve Hoberman's video 'The Power of Data Modeling,'
Data Modelling Master Class
The Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard®. You will know not just how to build a data model, but how to create a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects
Top 10 Objectives
- Explain data modeling 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, artifact 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
- Recognize 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
A complete description of the topics covered in the course and testimonials can be found here.
Steve Hoberman taught his first data modeling class in 1992 and has trained more than 10,000 people since then, spanning every continent except Africa and Antarctica. Steve is the author of ten books on data modeling, including the bestseller Data Modeling Made Simple.
His latest book, Data Modeling for MongoDB, presents a streamlined approach to data modeling for NoSQL solutions. 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 Conference, recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award, and highest rated workshop presenter at Enterprise Data World conferences in 2014 and 2015.
For more information on Steve, please see stevehoberman.com.
This course assumes no prior data modeling knowledge and, therefore, there are no prerequisites. This course is designed for anyone with one or more of these terms in their job title: “data”, “analyst”, “architect”, “developer”, “database”, and “modeler”.
Meals and refreshments
The cost of the workshop includes breakfast, morning tea, lunch, and afternoon tea. Please email email@example.com if you have dietary requirements.
A discount is available for bookings of three of more registrations. Please email firstname.lastname@example.org for more details.