7 - 8 March 2019 Canberra · 2018-11-08 · II | Data Modeling Zone Asia Pacific 2019. DATA MODELING ZONE ASIA PACIFIC 2019. STEVE HOBERMAN PETER AIKEN. DEBORAH HENDERSON. Data Blueprint.

Sponsors:

7 - 8 March 2019

Canberra

QT Canberra 1 London Circuit, Canberra ACT, Australia

II | Data Modeling Zone Asia Pacific 2019

DATA MODELING ZONE ASIA PACIFIC 2019

STEVE HOBERMAN DEBORAH HENDERSONPETER AIKEN Data Blueprint

Data Modeling Master Class

Data Governance as a Communications Program

Putting Data First: Why Johnny can’t data and what we need to do about it,

Pre-conference tutorial: Monday through Wednesday, March 4-6 (3 days)

Pre-conference tutorial: Monday through Wednesday, March 4-6 (3 days)

Pre-conference tutorial: Tuesday and Wednesday, March 5-6 (2 days)

Thursday, March 7

Innovations (Room 1)

Case Studies (Room 2)

Tools (Room 3)

CDMP

7:00-9:00 Breakfast in the Wisconsin Ballroom

8:30-10:00 Data Modeling Fundamentals

Steve Hoberman, Steve Hoberman & Associates, LLC

Page 14

Using DMBoK to Bootstrap your Data Management and Governance

Andy Peyton, IP Australia

Page 11

Document-based data modeling session coming shortly!

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10:30-12:00 Business-friendly data models

Graham Witt, Ajilon

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Enterprise architecture session coming shortly!

Introduction to Graph Modeling and Graph Databases

Joshua Yu, Neo4j

Page 3

12:00-1:15 Lunch in the Wisconsin Ballroom

1:15-2:30 KEYNOTE: Data and Ethics in the Digital Enterprise,

Deborah HendersonPage 2

2:30-3:00 Afternoon Snacks

3:00-4:30 Exorcising the Seven Deadly Data Sins

Peter Aiken, Data Blueprint

Page 9

Introduction to Data Vault: A data practitioner’s view

John Giles, Country Endeavours

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Fact-Based Data Integration: Matching and Transformation

Dr. Graeme Port, Factil

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III | Data Modeling Zone Asia Pacific 2019

Friday, March 8

Innovations (Room 1)

Case Studies (Room 2)

Tools (Room 3)

CDMP

7:00-9:00 Breakfast in the Wisconsin Ballroom

7:30-8:00 To be announced by Dec 1 To be announced by Dec 1 To be announced by Dec 1

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8:30-10:00 Become a data designer: What if your boss is an algorithm?

Andrew Smailes, President of DAMA Australia

Page 4

Partner session coming shortly!

A Quick Data Management Maturity Assessment Method Using the DMBOK2

Deborah Henderson

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10:30-12:00 Layering Business Logic on your Data Vault

Roelant Vos, Allianz Worldwide Partners

Page 10

Modern Data Management Practices

Selva Murugesan, ACT Government, Canberra, Australia

Page 13

How to Grade a Data Model

Steve Hoberman, Steve Hoberman & Associates, LLC

Page 12

12:00-1:15 Lunch in the Wisconsin Ballroom

1:15-2:30 KEYNOTE: KEYNOTE: Leadership, Value & Strategy

Peter Aiken, Data Blueprint

2:30-3:00 Afternoon Snacks

3:00-4:30 Dynamic Data Modelling

Graham Witt, Ajilon

Page 6

The devil’s in the details, or the devil is the details: Why, when & how top-down, big-picture modelling may save the day (and also improve your Data Vault journey)

John Giles, Country Endeavours

Page 7

Neo4j Graph Database Hands-on Workshop

Joshua Yu, Neo4j

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DATA MODELING ZONE ASIA PACIFIC 2019

IV | Data Modeling Zone Asia Pacific 2019

1 | Data Modeling Zone Asia Pacific 2019

DATA GOVERNANCE AS A COMMUNICATIONS PROGRAM

DEBORAH HENDERSON

Data Governance reach can be across the organization and the messaging must be tailored and sticky!

Organizational Change Management (OCM) and the Communications Program together are at the heart of any successful Data Governance Program.

Marketing folks know the tricks and we need to use them too to build a data-governed community.

This workshop will help you check the boxes for a successful governance program through a communications lens.

Proactive Change Management • Focusing and planning the change • Why change fails • Catalysts for effective change • Barriers to change • How people experience change

Key messaging Audiences and various methods for

handling - theory and practice

Subjective opinions, how to handle them and why they are important

Interpreting results; adjusting the plan

Self-assessment

Understanding your existing organization and cultural norms

• Do you have enough of a governance program to launch a sustained Communications effort?

• Organizational structure and Interaction patterns

Developing your scope Gaps and filling them

Planning

Channels and Tone Communicate ahead or after the fact? Checking-in with the plan

Tracking

Measuring Adoption Showing value

Roadmap

Refreshing the program When all fails Keeping it simple

has over 30 years in data and information management, consulting to many sectors across North America, and coordinating experts across the globe in best community practices in IT.

Program Manager, Contributing Author and Senior Editor for the global standard reference resource in Data Management DAMA-DMBOK2

Primary author for IT Strategy and Governance for the IEEE ITBOK standard reference

Consulted on responding and conforming to laws on data retention, data handling, and reporting strategies

Designed and deployed Data Governance operating models and processes across diverse businesses

She is passionate about how data supports business excellence.

DEBORAH HENDERSON,

B.Sc., MLS, PMP, CDMP Inaugural Fellow, CDP


A QUICK DATA MANAGEMENT MATURITY ASSESSMENT METHOD USING THE DMBOK2

DEBORAH HENDERSON

Data Management Maturity can be assessed with less time and money. Using the DMBOK2 is a fresh way of approaching a self-assessment. This session will walk you step by step through the method.

DMBOK2 as an Assessment Framework - pluses and minuses

Developing your scope

Setting the plan

Checklists!

Subjective opinions, how to handle them and why they are important

Interpreting results; getting to an action plan

DATA AND ETHICS IN THE DIGITAL ENTERPRISE

DEBORAH HENDERSON

When were we so innocent, or were we just not looking?

Ethical handling of data has historically been considered by the IT community to be the concern of public policy. Governments have assumed that the IT community was largely self-policing and the issues where …well… techy and complex. Then the use of personal data to influence an election (USA) and a referendum (UK) was first hinted at and then uncovered.

Biased information and Fake News get traction due to the growing lack of critical thinking in our society, self-selection into certain channels for information and active modern propaganda machines. Facebook turned a blind eye to their dataset uses; Cambridge Analytica and others carried on as they pleased. Surely there were people like us, data analysts, DBAs, data scientists on these unethical projects?

How should we respond to these issues as the data management professionals on the front line? What does ethical handling of data look like? What are our responsibilities now?

has over 30 years in data and information management, consulting to many sectors across North America, and coordinating experts across the globe in best community practices in IT.

Program Manager, Contributing Author and Senior Editor for the global standard reference resource in Data Management DAMA-DMBOK2

Primary author for IT Strategy and Governance for the IEEE ITBOK standard reference

Consulted on responding and conforming to laws on data retention, data handling, and reporting strategies

Designed and deployed Data Governance operating models and processes across diverse businesses

She is passionate about how data supports business excellence.

DEBORAH HENDERSON,

B.Sc., MLS, PMP, CDMP Inaugural Fellow, CDP


INTRODUCTION TO GRAPH MODELING AND GRAPH DATABASES

JOSHUA YU, NEO4J

We start off with an overview to graph modeling, followed by an explanation of graph databases and their roles within our organizations. Learn the top use cases for graph databases, along with best practices in graph modeling.

NEO4J GRAPH DATABASE HANDS-ON WORKSHOP

JOSHUA YU, NEO4J

Bring your laptop and practice building graph databases with Neo4j. After covering installation and configuration, we will build a graph database and use the Cypher graph query language to manipulate and access data. Learn how to traverse graphs via relationships, aggregate results, leverage the meta graph model, apply indexes and constraints, and import data.

Joshua has almost 20 years of experiences in IT, and has been working as architect, designer, and developer in various industries like finance, manufacturing, retail and government. Joshua has a passion in anything about data, esp. data mining and visualization. He is also very active in children’s education in STEM related subjects esp. programming. Joshua now lives in Sydney, Australia.

JOSHUA YU Neo4j


BECOME A DATA DESIGNER: WHAT IF YOUR BOSS IS AN ALGORITHM?

ANDREW SMAILES, PRESIDENT OF DAMA AUSTRALIA

Interest in data management is growing. Organisations are creating data management functions, Chief Data Officers appointed, data governance and quality processes initiated, conceptual data models developed, data catalogues built, and data lakes implemented.

However, the fourth industrial revolution is already here and the crux of data management is different. Digital transformation and open data has shifted the idea of data ownership to an ecosystem where data is shared to support ubiquitous virtual services. Transformation is being driven by a generation growing up with aspirations of sharing,

co-creation and cloud culture. Their products and services are to be called upon whenever and will provide information fuelled by understanding the relationships within multidimensional and constantly evolving networks.

With more and more information available in a computable way, and people building knowledge faster and in ways not previous available, how does our approach to data management respond?

How do we design data services to filter and interpret information to make it useful and meaningful to us?

This presentation challenges the current prescriptive approach to data management and proposes a design-based approach leveraging artificial intelligence to assess the effectiveness of any organisations data management function.

Andrew is currently the President of DAMA Australia, an association providing a forum for exchange of information relating to information resource management and to discuss challenges, ideas, experiences, resources and questions.

Previously President of the DAMA Canberra Chapter, Andrew has 28 years’ experience working in many facets of Government ICT design, delivery and support. His principal area of expertise is data management, data warehousing and business intelligence within the Federal public sector.

ANDREW SMAILES President of DAMA Australia


FACT-BASED DATA INTEGRATION: MATCHING AND TRANSFORMATION

DR. GRAEME PORT, FACTIL

Fact-Based Modeling is an effective approach for defining the structure and semantics of stored data. Data integration involves combining data residing in disparate sources into meaningful and valuable information. Data integration faces two key challenges: matching records from different sources corresponding to the same real-world entity, and transforming the associated data so that the combined information is meaningful. We show that a fact-based integration language can be tightly coupled with a data matching system and compiled to data transformation code for data integration.

We will apply this approach in two scenarios:

a Data Vault data warehouse environment, and

a project for sharing information nationally for child safety

We will show that automated integration code generation from a fact-based integration language reduces cost, contains fewer errors, and supports change and agility.

Our approach can be used to generate code directly, or as a conceptual front-end to traditional data management tools.

INTRODUCTION TO DATA VAULT: A DATA PRACTITIONER’S VIEWJOHN GILES, COUNTRY ENDEAVOURS

This session is intended to be a primer for those who haven’t yet encountered the wonderful world of Data Vault. Topics covered are to include:

The “sales pitch” for Data Vault The why, where, when of using Data Vault

Positioning Data Vault with regard to Inmon and Kimball data warehouses

The Data Vault building blocks: Hubs Links Satellites

There’s more than Raw Data Vault Business Data Vault • Why the distinction is important • Point In Time tables • Bridge tables

Virtual data marts Operational Data Vault: a variation on the

central theme

Common data modeling challenges, including Transactions Reference tables Duplicates and Same-As-Links Hierarchical links The Flip-Flop effect

Data Vault 2.0

Where to next?

Dr. Graeme Port has been an innovator and leader in data architecture and enterprise software product development for over 30 years. Graeme was co-founder, head of engineering and CTO at ManageSoft, which built market-leading products in business intelligence, application development and application deployment. Graeme has consulted extensively in data architecture in government and commercial sectors. Graeme received his PhD from the University of Melbourne in the field of Logic Programming.

John Giles is an independent consultant, with a passion for seeing ideas taken to fruition. For 2 decades his focus has been on enterprise information modelling, enterprise information integration and enterprise information architecture. Over the last few years he has also gained international recognition in Data Vault modelling.

John is primarily a practitioner, having been responsible for leading teams to successful delivery of IT solutions across a wide diversity of industries. However, his pragmatic focus is backed up by a solid appreciation of the underlying theory, having presented internationally, and published widely, including in his book titled The Nimble Elephant: Agile delivery of data models using a pattern-based approach.

DR. GRAEME PORT FACTIL

JOHN GILES Country Endeavours


DYNAMIC DATA MODELLING

GRAHAM WITT, AJILON

Most data models are static, in that they represent the properties of, and relationships between, business entities at a point in time. However, for a system to properly function over time, its data model must be designed to support data update in response to changes in the real world. Dynamic Data Modelling covers not only static data structures but update policies, by considering issues such as

what real-world changes must be captured in the database?

what are the requirements for preserving a record of the historic state of the attributes and relationships of any entity?

why must changes in attributes and changes in relationships be dealt with differently?

do we also need to record changes in our state of knowledge of the real world?

what aspects of the time dimension need to be taken into account?

This presentation provides an overview of the Dynamic Data Modelling toolkit, with which experienced data modellers can effectively support projects delivering BI or operational data resources with a significant time-variant component.

Graham has over 30 years of experience in delivering effective data solutions to the government, transportation, finance and utility sectors. He has specialist expertise in business requirements, architectures, information management, user interface design, data modeling, database design, data quality and business rules. He has spoken at conferences in Australia, the US and UK and delivered data modeling and business rules training in Australia, Canada and the US. He has written two textbooks published by Morgan Kaufmann: “Data Modeling Essentials” (with Graeme Simsion) and “Writing Effective Business Rules”, and has written two series of articles for the Business Rule Community (www.brcommunity.com).

GRAHAM WITT Ajilon


THE DEVIL’S IN THE DETAILS, OR THE DEVIL IS THE DETAILS: WHY, WHEN & HOW TOP-DOWN, BIG-PICTURE MODELLING MAY SAVE THE DAY (AND ALSO IMPROVE YOUR DATA VAULT JOURNEY)

JOHN GILES, COUNTRY ENDEAVOURS

Models are only ever a means to an end. Sometimes car designs are modelled in clay – you can’t drive the models, but you can get valuable feedback on the visual impression of the proposed concept. Sometimes scale models of aircraft are tested in wind tunnels – you can’t fly them, but you can evaluate their aerodynamics. Models are cheaper to build than the real things, and serious consideration of design alternatives can be debated without breaking the bank.

Likewise, data models are only ever a means to an end, but if they drive open discussion on design alternatives, and contribute to better solutions that meet real business needs, they will be highly valued. And, unlike clay models of cars or scale models of aircraft, given the right environment, you might be able to press what I call the “big green Go button” and turn the data model into a real software deliverable.

Data modelling used to be seen by many as a strictly technical exercise, aimed at physical

implementation. Increasingly people are referring to information modelling, and that’s all about the business. So here’s the warning – if data modelers can’t or won’t engage with the business to deliver value in a timely manner, at best they will be undervalued, and at worst shunned.

There are times big-picture top-down models may not only be sufficient for today’s urgent needs, but in some cases are preferable to a more detailed, rigorous bottom-up model. We will:

Look at why, when and where top-down models can be developed to deliver business value, then, more controversially, challenge questionable reasons given as to why some data modellers may still be developing bottom-up, detailed models.

Briefly touch on how top-down models can be developed in a timely manner, then introduce some free, “open” resources to help you.

Note some of the many ways a top-down model can be changed from worthless shelf-ware into applied business value, then dive into one such application – Data Vault design.This presentation provides an overview of the Dynamic Data Modelling toolkit, with which experienced data modellers can effectively support projects delivering BI or operational data resources with a significant time-variant component.

John Giles is an independent consultant, with a passion for seeing ideas taken to fruition. For 2 decades his focus has been on enterprise information modelling, enterprise information integration and enterprise information architecture. Over the last few years he has also gained international recognition in Data Vault modelling.

John is primarily a practitioner, having been responsible for leading teams to successful delivery of IT solutions across a wide diversity of industries. However, his pragmatic focus is backed up by a solid appreciation of the underlying theory, having presented internationally, and published widely, including in his book titled The Nimble Elephant: Agile delivery of data models using a pattern-based approach.

JOHN GILES Country Endeavours


BUSINESS-FRIENDLY DATA MODELS

GRAHAM WITT, AJILON

If a system is to support an enterprise’s business information requirements, a necessary part of the design process is effective review of the design by appropriate business stakeholders. For such review to be effective, the design documentation provided to those stakeholders (“the business model”) must

be understandable

be complete, i.e. depict all information in which business stakeholders are interested

not depict any information in which business stakeholders have no interest (“noise”) which distracts or confuses reviewers, reducing review effectiveness.

Logical data models do not meet these criteria, yet most conceptual data models are degenerate logical data models including much noise. This presentation details what should be included in and excluded from a data model to be reviewed by business stakeholders.

PUTTING DATA FIRST: WHY JOHNNY CAN’T DATA AND WHAT WE NEED TO DO ABOUT IT

PETER AIKEN, DATA BLUEPRINT

Organizations are repeatedly frustrated when attempting to do more with their data. Not even really certain what this means or how to do it, they still repeatedly spend too much, take too long, and deliver far less than planned as they try to employ data in support of their strategic objectives. This 2-day workshop begins by describing the root causes of why Johnny and organizations in general can’t better leverage data - focusing on its inherent architectural and engineering roots - foundations that are almost totally lacking in formal university programs and training curricula. Three subsequent half day sessions then focus on requisite leadership criteria for success; how to express these needs in a form that management will understand - a focus on the ‘why’ - a data strategy; and monetizing data management in order to make it relevant to management. After two days, delegates will be able to better prepare their organizations to better employ data to support their objectives.

Day 1 (morning) As-is/Cause for concern: Disclaimer/Bad data decisions spiral

Data Management practices hierarchy structure

Cost of the lack of architecture/engineering capabilities

Self-assessment/Root cause analysis

Day 1 (afternoon) Leadership/Necessary (but insufficient) prerequisites: Dedicated solely to data asset leveraging

Unconstrained by an IT project mindset

Reporting directly to the business

Implementation of data-centric thinking

Day 2 (morning) Data Strategy/A focus on the ‘why’: Data management practices hierarchy and foundational elements

A data strategy is necessary for effective data governance

Effective data strategy prerequisites Data strategy development phase II–iterations

Graham has over 30 years of experience in delivering effective data solutions to the government, transportation, finance and utility sectors. He has specialist expertise in business requirements, architectures, information management, user interface design, data modeling, database design, data quality and business rules. He has spoken at conferences in Australia, the US and UK and delivered data modeling and business rules training in Australia, Canada and the US. He has written two textbooks published by Morgan Kaufmann: “Data Modeling Essentials” (with Graeme Simsion) and “Writing Effective Business Rules”, and has written two series of articles for the Business Rule Community (www.brcommunity.com).

GRAHAM WITT Ajilon


LEADERSHIP, VALUE & STRATEGY


Management concerns fall into broad categories:

Implementing change that effectively improves performance

Processing lots of information without gaining appreciable insight

Securing real returns from technology investments

Data, of course, is at the heart of all of these and other organizational complaints. A triple play of investments in data as an organizational asset is 1) a necessary prerequisite and 2) a primary enabler - permitting management to address these troubling issues. Transformation may require some organizational discomfort. The first step is to recruit, qualified organizational talent. The second step is to significantly overhaul the manner by which most organizations seek to obtain informational value. The third step is to implement a strategy that integrating organizational functions with IT. By approaching data in this different way, organizations can begin to gain the leverage that they seek. This talk will address each of these topics - illustrating how this triple play of new leadership skills, revised value propositions, and a repositioning of strategic investments can alleviate the concerns.

EXORCISING THE SEVEN DEADLY DATA SINS


The difficulty of implementing a new data strategy often goes under-appreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This talk will discuss these barriers—the titular “Seven Deadly Data Sins”—and in the process will also:

Elaborate upon the three critical factors that lead to strategy failure

Demonstrate a two-stage data strategy implementation process

Explore the sources and rationales behind the “Seven Deadly Data Sins,” and recommend solutions and alternative approaches.

Peter Aiken is an acknowledged Data Management (DM) authority. As a practicing data consultant, professor, author and researcher, he has studied DM for more than 30 years. International recognition has come

from assisting more than 150 organizations in 30 countries including some of the world’s most important. He is a dynamic presence at events and author of 10 books and multiple publications, including his latest on Data Strategy. Peter also hosts the longest running and most successful webinar series dedicated to DM (hosted by dataversity.net). In 1999, he founded Data Blueprint, a consulting firm that helps organizations leverage data for profit, improvement, competitive advantage and operational efficiencies. He is also Associate Professor of Information Systems at Virginia Commonwealth University (VCU), past President of the International Data Management Association (DAMA-I) and Associate Director of the MIT International Society of Chief Data Officers.

PETER AIKEN Data Blueprint


LAYERING BUSINESS LOGIC ON YOUR DATA VAULT

ROELANT VOS, ALLIANZ WORLDWIDE PARTNERS

The Data Vault methodology provides elegant concepts to develop your Data Warehouse - the various required Data Warehouse mechanics are organised in a way that allows for a flexible solution.

By and large, it is fair to say the pattern archetypes such as Hubs, Links and Satellites are sufficiently understood by the broader community.

Indeed, there have been many great examples of how metadata (model) driven approaches simplify delivery. These approaches, both open-source and commercial, leverage the templates and insert the required information from repositories or domain-specific languages.

When this foundation is in place and a baseline for rapid development, deterministic refactoring and deployment has been established, the dynamic shifts to aligning the data with the business’ expectations.

This session discusses the concepts that are available to apply business logic on a Data Vault (and where to do this), as well as examples how this can be implemented.

Contents:

Separation of concerns approach for Data Vault; what is already in place ‘out of the box’?

Different approaches towards capturing transformation (business logic) metadata

Incorporating business logic in ETL generation

Abstracting levels of design; how far can we go?

Tips to keep your solution manageable

The role of ‘time’ in delivering outputs the business can work with

During the day I work as General Manager - Enterprise Data Management (which roughly covers Data Governance, BI and Analytics) and at night I try to improve the status quo in the data community through open source development and framework collaboration. With this I hope to contribute to the delivery of enterprise wide data management that is fast, flexible, future proof and easy to manage.

To me, working with data is endlessly varied and finding more ways to further simplify (‘automate’) data management continues to be a source of inspiration.

Because of its automation potential and clearly defined patterns I have been a fan of Data Vault and similar hybrid approaches for Data Warehouse design for almost 15 years. I have written many articles around this theme on www.roelantvos.com/blog and also delivery my own Data Vault implementation and automation classroom training.

I am a practical person with many interests and a strong technical background and would like to get (and stay) in touch with like-minded professionals. Please reach out on [email protected] or participate on www.roelantvos.com/blog to see where we can continue to improve our data management solutions.

ROELANT VOS Allianz Worldwide Partners


USING DMBOK TO BOOTSTRAP YOUR DATA MANAGEMENT AND GOVERNANCE

ANDY PEYTON, IP AUSTRALIA

Over the past 15 years there has been a move away from the concept of “corporate” data management to a model based on “project” data management. Project data management has usually meant doing only what was necessary to get a project over-the-line without a lot of consideration of the longer-term needs of the enterprise. This model is driven by project cost, resources, and deliverable timeframes.

The result of this change has been a loss of standardised processes to ensure that data is managed as a corporate asset. We may now have situations where projects have done their own thing and there is no centralised data dictionary explaining our data and perhaps little knowledge of where our data actually is. We may also have data with an unknown level of quality even though this drives customer interactions and business insight. Recreating corporate data management and governance processes is a daunting task.

We also have to work in a new world where there is little desire for expensive documentation and bureaucratic processes.

However, the DAMA Body of Knowledge gives us a way of:

Explaining the data management and governance problem to senior management,

prioritising problem areas,

identifying roles and accountability,

progressively building capability,

re-using existing material, and

approaching the problem using industry standards.

This presentation will give you a brief overview of the DAMA Body of Knowledge (DMBoK) and how this can be used to kick-start a lean corporate data management and governance process. Lessons learnt from going through this process at IP Australia will be used as examples.

Andy Peyton is a Senior Solutions Architect for IP Australia. IP Australia is responsible for the issue and management of Patents, Trade Marks, Designs, and Plant Breeder Rights within Australia. Andy has worked for many years in various data management roles for

different government departments and is currently leading the team in the design and development of the new database environment that will underpin IPA’s systems for the next 20 years.Andy has previously worked in organisations such as Centrelink, Defence, Health & Ageing, Defence Housing Authority, Immigration, and the ATO. As a result he has a keen understanding of the need for designing databases that meet the long-term needs of government departments where “applications come and go, but the data goes on forever”.Andy has a Bachelor of Science degree from the University of Sydney and a Master of Management Economics from the University of NSW. He is a senior member of the Australian Computer Society and a member of DAMA Canberra.

ANDY PEYTON IP Australia


HOW TO GRADE A DATA MODEL

STEVE HOBERMAN, STEVE HOBERMAN & ASSOCIATES

I have been using the Data Model Scorecard® to validate data models for over 15 years. Over the past year, I have built a free tool that will help you assess and score your own models. This tool takes the form of a decision tree, where over 150 questions are asked to “score” a model from Poor to Excellent. This session covers the ten Scorecard categories, the decision tree required to review a model (and which is embedded in the tool), and the “Top 5” questions that can make or break a model. You will then grade a data model using the online tool.

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. 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 conferences, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award.

STEVE HOBERMAN Steve Hoberman & Associates


MODERN DATA MANAGEMENT PRACTICES

SELVA MURUGESAN, ACT GOVERNMENT, CANBERRA, AUSTRALIA

Many organisations are investing heavily to maximise the value of their data assets. Data analytics is touted as a golden key to unlock the potential of the organisational data assets. Organisations tend to invest in new technology tools (data lake, cloud services), data analytics platforms (machine learning, AI), and people capabilities. These initiatives tend to focus on data science projects that have high value and impact.

Digital transformation and artificial intelligence are now emphasising the need to make it easy for people to deal with organisations by making services to be simple, clear and fast. Many consulting firms cited that many data initiatives fail as the underlying data does not have the quality and integrity to support automated processes. Now, organisations are realising that having consistent data management practices across the organisation, and with external stakeholders, is essential to improving the level of trust in the data.

Thus data management practices need to evolve to build greater organisational trust when sharing data.

This talk covers:

Why is data sharing so important

What are the principles that sharing should be based on

What is the appropriate data governance framework

How to implement data management principles

How technology platforms can be leveraged for implementation

Selvaraaju Murugesan is currently working as Senior Manager, Innovation and Data Analytics at Transport Canberra and City Service Directorate, ACT Government. He received his PhD degree in computational mathematics from LaTrobe University, Melbourne in 2014. His interests are in data management practices and data analytics. He is a committee member of DAMA Canberra chapter looking after membership and marketing.

SELVA MURUGESAN ACT Government, Canberra, Australia


DATA MODELING MASTER CLASS

Steve Hoberman’s Best Practices Approach to Developing a Competency in Data Modeling


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 build 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

1. Explain data modeling components and identify them on your projects by following a question-driven approach

2. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book

3. Validate any data model with key “settings” (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard®

4. Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing

5. Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions

6. Practice finding structural soundness issues and standards violations

7. Recognize when to use abstraction and where patterns and industry data models can give us a great head start

8. Use a series of templates for capturing and validating requirements, and for data profiling

9. Evaluate definitions for clarity, completeness, and correctness

10. Leverage the Data Vault and enterprise data model for a successful enterprise architecture

DATA MODELING FUNDAMENTALS


Assuming no prior knowledge of data modeling, we start off with an exercise that will illustrate why data models are essential to understanding business processes and business requirements. Next, we will explain data modeling concepts and terminology, and provide you with a set of questions you can ask to quickly and precisely identify entities (including both weak and strong entities), data elements (including keys), and relationships (including subtyping). We will discuss the three different levels of modeling (conceptual, logical, and physical), and for each explain both relational and dimensional mindsets.

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. 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 conferences, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award.

STEVE HOBERMAN Steve Hoberman & Associates

7 - 8 March 2019 Canberra · 2018-11-08 · II | Data Modeling Zone Asia Pacific 2019. DATA MODELING ZONE ASIA PACIFIC 2019. STEVE HOBERMAN PETER AIKEN. DEBORAH HENDERSON. Data Blueprint.

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