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IRM UK | Mastering Data Modelling Techniques
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This course explores the more advanced techniques for Data Modelling. In addition, techniques will be taught on how (and when) to create Data Models for non-relational solutions including Big Data together and the uses for data models beyond Relational DBMS development.

3/15/2018 to 3/16/2018
When: 15 - 16 March 2018
Thursday and Friday
Where: etc.venues Marble Arch
Garfield House
86 Edgware Rd
London W2 2EA
United Kingdom
Presenter: Chris Bradley
Contact: +44 (0)20 8866 8366

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Mastering Data Modelling Techniques

Use code AEA10 to receive 10% AEA member discount when registering!!

Register On-line:
15 - 16 March 2018, London

Seminar Fee 
£1,245 + VAT (£249) = £1,494


This course explores the more advanced techniques for Data Modelling. In addition, techniques will be taught on how (and when) to create Data Models for non-relational solutions including Big Data together and the uses for data models beyond Relational DBMS development.

In the modern era, the volume of data we deal with has grown significantly. As the volume, variety, velocity and veracity of data keeps growing, the types of data generated by applications become richer than before. As a result, traditional relational databases are challenged to capture, store, search, share, analyse, and visualize data. Many companies attempt to manage big data challenges using a NoSQL (“Not only SQL”) database and may employ a distributed computing system such as Hadoop. NoSQL databases are typically key-value stores that are non-relational, distributed, horizontally scalable, and schema-free.

Many organisations ask, “do we still need data modelling today?” Traditional data modelling focuses on resolving the complexity of relationships among schema-enabled data. However, these considerations do not apply to non-relational, schema-less databases. As a result, old ways of data modelling no longer apply.

This course will show Data modelling approaches that apply to not only Relational, but also to Big Data, NoSQL, XML, and other formats. In addition, the uses of data models beyond simply development of databases will be explored.

Learning Objectives

At the end of the course, delegates would have gained the following:

Practical Application:

  • Build conceptual and logical data models, and know about compromises for physical design;
  • How to discover requirements for robust data models;
  • Understand where abstraction is valuable (and where it is risky);
  • Where industry data models can provide a kick start;
  • How (and where) to apply standard solutions to well-known data modelling business scenarios.

Level Set Understanding & Terminology:

  • Learn about the need for and application of Data Models in Big Data and NoSQL environments
  • See the areas where Data modelling adds value to Data Management activities beyond Relational Database design
  • Understand the critical role of Data models in other Data Management disciplines particularly Master Data Management and Data Governance.

Pragmatic Learning

  • Learn the best practices for developing Data models for Big Data and NoSQL environment
  • Understand how to create data models that can be easily read by humans
  • Recognise the difference between Enterprise, Conceptual, Logical, Physical and Dimensional Data models
  • Through practical examples, learn how to apply different Data modelling techniques

Course Outline

Data Modelling Recap

  • Data modelling basics
  • major constructs
  • identifying entities
  • Data model types, and the linkage between them

Levels of Models  

  • Enterprise, Conceptual, Logical & Physical
  • What is the purpose of each, do we need all of these in a Big Data world
  • Where does Dimensional modelling fit in?

Data Modelling – Back to the Future?

  • Data Modelling didn’t start with relational! This may be a surprise to many people, but the first uses of data models were well before Relational data bases became the norm.  The techniques are applicable to many of the modern non-relational formats we see today.
  • Modelling in the pre-relational days.  We didn’t have RDBMS’s.  We had Flat files, Sequential, VSAM, Hierarchical DBMS’s, Network DBMS’s, Inverted Architecture DBMS’s.
  • The techniques that were developed for these are directly appropriate to the NoSQL and Big Data world of today.

Data Modelling for Big Data & NoSQL

  • What has to change when we are developing data models for a Hadoop or other Big Data environment?
  • Do modelling tools support Big Data technologies, what are the restrictions and considerations?
  • What data modelling techniques are applicable when targeting a Big Data platform?
  • Does normalisation still have a place in the Big Data world?
  • Where’s our metadata in the model now?
  • In the age of big data, popular data modeling tools (eg ER/Studio, ERWin, PowerDesigner) continue to help us analyze and understand our data architectures by applying hybrid data modelling concepts. Instead of creating pure a relational data model, we now can embed NoSQL submodels within a relational data model. In general, data size and performance bottlenecks are the factors that help us decide which data goes to the NoSQL system.
  • Key Value Pairs: A common misconception is that using data structures like JavaScript Object Notation (JSON) prevents us from needing a data model; THIS IS WRONG.  We’ll show several examples & conclude that a set of JSON files can be just as complicated as a 100 table 3rd Normal Form data model.
  • NoSQL & Hadoop:  How the 4 types of NoSQL databases still need data models, and how the ACID vs BASE paradigm affects this.

Modelling for Hierarchic Systems & XML

  • What must change when developing data models for XML & Hierarchic systems?

Services Oriented Architecture (SOA)

  • Why data models are essential for success.

Massively Denormalised Files

  • Is modelling needed?
  • How do we create data models for Data lakes?

Dimensional Data Models

  • How do we create a dimensional model?
  • Converting an ER model to Dimensional.
  • Slowly changing dimensions, what types and when are they applicable.
  • Beyond the basics with conformed dimensions, bridges, junk dimensions & fact less facts.

Application Packages & Data Models

  • Do we need to develop data models when implementing a COTS package?
  • Uses and benefits.

Using Data Models for Data Integration & Lineage

  • How to exploit data models for design of data integration approaches and in data lineage.

Top Down Requirements Capture

  • When is it appropriate
  • What are the limitations.

Bottom Up Requirements Synthesis

  • When this works, where is it appropriate.
  • How do we cope with existing DBMS’s and systems.

How to Capture Requirements for Both Data and Process Needs

  • What comes first Data or Process – we’ll show the answer.
  • The critical importance of understanding processes to get your data models right (and vice versa).
  • Interaction between process and data models.
  • Approaches for capturing Process AND Data Requirements.

Checking the Data vs the MetaData; Why Does it Matter?

Use of Standard Data Model Constructs and Pattern Models

  • Understanding the Bill of materials (BOM) construct.  Where can it be applied, why it’s one of the most powerful modelling constructs.
  • Party; Role; Relationship:  Why mastering this construct can provide phenomenal flexibility.
  • Mastering Hierarchies:  Different approaches for modelling hierarchies.

Different Data Modelling Notations & a Comparison Between Them


  • Progressing beyond 3NF.  4NF, 5NF Boyce-Codd, and why, and when to use them

Who It's For

Practitioners who will need to read, consume or create data models, particularly for Big Data and non-RDBMS environments.  Users who wish to gain a better understanding of data during Information Management initiatives including:

  • Data Modellers
  • Business Intelligence & Data Warehouse Developers & Architects
  • Data Architects
  • Data Analysts
  • Enterprise Architects
  • Solution Architects
  • Application Architects
  • Information Architects
  • Business Analysts
  • Developers
  • Database Administrators
  • Project / Programme Managers
  • IT Consultants
  • Data Governance Managers
  • Data Quality Managers
  • Information Quality Practitioners


Attendance at the Data Modelling Essentials class OR 3+ years of practical Data Modelling experience



Information Strategist, Data Management Advisors Ltd.

Christopher Bradley has spent 35 years in the forefront of the Information Management field, working for leading organisations in Information Management Strategy, Data Governance, Data Quality, Information Assurance, Master Data Management, Metadata Management, Data Warehouse and Business Intelligence.   Chris is an independent Information Strategist & recognised thought leader.  Recently he has delivered a comprehensive appraisal of Information Management practices at an Oil & Gas super major, Data Governance strategy for a Global Pharma, and Information Management training for Finance & Utilities companies.  Chris guides Global organizations on Information Strategy, Data Governance, Information Management best practice and how organisations can genuinely manage Information as a critical corporate asset.  Frequently he is engaged to evangelise the Information Management and Data Governance message to Executive management, introduce data governance and new business processes for Information Management and to deliver training and mentoring.  Chris is Director of the E&P standards committee “DMBoard”, an officer of DAMA International, an author of the DMBoK 2.0, a member of the Meta Data Professionals Organisation (MPO) and a holder at “master” level and examiner for the DAMA CDMP professional certification. Chris is an acknowledged thought leader in Data Governance, author of several papers and books, and an expert judge on the annual Data Governance best practice awards. Follow Christopher on Twitter @inforacer.

Register On-line:
15 - 16 March 2018, London

Seminar Fee 
£1,245 + VAT (£249) = £1,494

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