Adaptive Data Architecture

Best Practices Approach to Developing a Competency in Data Modeling

29-30 June 2021

Melbourne Business School, Carlton

Asif Gill Data Modeling Workshops are recognized as the most comprehensive in the industry.
Who Should Attend
  • Information and Data Architects
  • Data Analysts, Engineers, Scientists and Stewards  
  • Directors and Head of Departments 
  • Strategists, Policy and Law Experts .
Prerequisites: Participants should have basic awareness of data management. Delivery: This workshop is delivered over 2 days. Participants will work on a model-driven adaptive data architecture project and case studies. The workshop will be presented in an interactive style combining case study examples and exercises. Each participant will be provided with a set of workshop slides.
  • Module 1: Understanding data ecosystem
  • Module 2: Formalizing data strategy
  • Module 3: Designing data architecture
  • Module 4: Developing data architecture roadmap and implementation plan
  • Module 5: Establish data architecture governance and continuous adaptation
  • Conclusion and Assessment
Outcomes  At the end of this course you will be able to:
  • Gain a good practical understanding of the model-driven adaptive data architecture.
  • Plan, analyse and model adaptive data architecture in the broader context of adaptive enterprise architecture.
  • Perform strategic analysis for developing data architecture roadmap, implementation planning, governance and adaptation.
You will also have the strong foundation ton:
  • Module 4: Developing data architecture roadmap and implementation plan
  • Module 5: Establish data architecture governance and continuous adaptation
  • Conclusion and Assessment

2 Day Workshop

$ 2200

2 Day Workshop + DMZ Conference

$ 4360
SAVE $240

Association Package

$ 4140
  • 10% DISCOUNT
SAVE $460

About this Workshop

Data modeling is about understanding the data used within our operational and analytics processes, documenting this knowledge in a precise form called the “data model”, and then validating this knowledge through communications with both business and IT stakeholders. Underlying all successful applications is a robust and precise data model, and similarly, most software development failures are due to a lack of understanding of the data or data requirements.

A data model is, therefore, an essential part of applications development including forward engineering, reverse engineering, and integration efforts. Forward engineering means focusing on business requirements, whereas reverse engineering means modeling existing systems to drive the support, replacement, or customization of applications. Integration projects such as business intelligence efforts, data lakes, and master data initiatives, require a consistent holistic view of concepts such as Customer, Account, and Product.

Data Modeling Fundamentals contains two days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects.

Top 4 Objectives:

  • Understand the value that using data models can deliver in your organisation.
  • Determine how and when to use each data modeling component.
  • Build relational and dimensional conceptual, logical, and physical data models.
  • Incorporate supportability and extensibility features into the data model.

Workshop Pricing

Two Days Workshop $1990

Two Days Workshop Plus DMZ Conference $3400 save $580

Academic registrations are available.

Partners Association Member receive

10% Discount

on DMZ Asia conference and workshop packages.

Day 1

Modeling Basics:

Assuming no prior knowledge of data modeling, we work on our first case study to illustrate four important gaps filled by data models. Next, we explore data modeling concepts and terminology, and with a set of questions. We quickly and precisely build a data model, explaining each component and gain practice reading business rules. We will complete several exercises, including creating a data model based upon an existing set of data. 

You will be able to answer the following questions: 

  • What is a data model, and what characteristics make a data model an essential wayfinding tool?
  • What are critical skills for a data modeler?
  • Why is precision so important?
  • What three situations can ruin a data model’s credibility? 

You will also learn these concepts, terms and skills:

Concepts & Terms

  • Applying the 80/20 rule to data modeling
  • Entities, attributes, and relationships
  • Exclusive and non-exclusive subtypes
  • Candidate, primary, natural, alternate, and foreign keys
  • Surrogate keys
  • Cardinality and referential integrity
  • Recursion


  • Six questions to translate ambiguity into precision
  • How to “read” a data model
  • Asking the most important Questions when reviewing a data model
  • Use different modeling notations
  • Represent subtypes
  • Model hierarchies and networks
Day 2

Understanding conceptual, logical, and physical data models:

The conceptual data model captures a business need within a well-defined scope, the logical data model captures the business solution, and the physical data model captures the technical solution. Relational, dimensional, and NoSQL techniques will be described at each of these three levels.

We will also practice building several data models, and you will be able to answer the following questions:

  • How do relational and dimensional models differ?
  • What are the ten different types of data models?
  • Why are conceptual and logical data models so important?
  • What are four different ways of communicating the conceptual?
  • What are the six conceptual data modeling challenges?
  • What is the lure of NoSQL?
  • What are the advantages and disadvantages of going to “schema-less”?
  • What is MongoDB?

You will also learn these concepts, terms and skills:

Concepts & Terms

  • Concept and Question Templates  
  • Grain, base, and atomic on a dimensional 
  • Transaction, snapshot and accumulating facts  
  • Conformed dimensions 
  • Junk, degenerate, and behavioural dimensions  
  • Outriggers, measureless meters, and bridge tables  
  • A star schema and a snowflake 
  • The Attributes Template 
  • Aggregation and summarisation 
  • Slowly Changing Dimensions 
  • NoSQL and RDBMS 
  • Document, Column, Key-value, and Graph databases 
  • ACID and BASE  
  • Physical and implementation data models 


  • Use the five strategic conceptual modeling questions
  • Build a conceptual data model
  • Capture a program-level view of business questions
  • Navigate a dimensional data model
  • Leveraging the grain matrix
  • Apply the Normalization Hike
  • Use views, indexing, and partitioning to improve performance
  • Subtyping on a physical data model
  • Using denormalisation

About Asif Gil

Dr Asif Gill is a result-oriented academic and practitioner with deep experience in IT across banking, consulting, finance, education, government, non-profit, software and telecommunication.  

He is a senior lecturer and leader of the DigiSAS Lab (P.K.A. COTAR) Research Group at the UTS School of Computer Science. He is also founding director Adapt Inn. His earlier professional experience in agile software development, IT business analysis, solution architecture and program management provided a strong foundation for later work in strategic enterprise architecture and governance. 

 Asif is a recognised thought leader and specialist expert in adaptive enterprise architecture & agile software development. He is a member of the Software & Systems Engineering Committee (IT-015), Standards Australia. He is often invited and involved as a professional speaker, guest editor, conference chair, organiser and reviewer of national and international academic and industry conferences. 


Workshop Times

  • Registration 8.00 am
  • Workshop starts 8.30 am
  • Workshop close 4.3o pm

Lunch and refreshments are provided.

What should I bring?

  • This is a participative workshop. Come prepared to interpret, find meaning in, and critique several data communication products, and perhaps even to tell a data story.  


  • The University Square Public car park is a 5-minute walk from the venue.
  • Casual Entry: $25.00 all day. 

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