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IRM UK | Business-Oriented Data Modelling
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This workshop introduces entity-relationship modelling from a non-technical perspective, and explores contextual, conceptual, and detailed modelling techniques that maximise user involvement.

10/9/2017 to 10/10/2017
When: 9 - 10 October 2017
Monday and Tuesday
Where: etc.venues Marble Arch
Garfield House
86 Edgware Rd
London W2 2EA
United Kingdom
Presenter: Alec Sharp
Contact: +44 (0)20 8866 8366

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  IRM UK                                              

Business-Oriented Data Modelling: A Business-Oriented Approach to Entity-Relationship Modelling

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

Register On-line:
9-10 October 2017, London

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


Data modelling is critical to the design of quality databases, but is also essential to other requirements specification techniques such as workflow modelling, use cases, and service definition because it ensures a common understanding of the things – the entities – that processes and applications deal with.  This workshop introduces entity-relationship modelling from a non-technical perspective, and explores contextual, conceptual, and detailed modelling techniques that maximise user involvement.

Data modelling was originally developed as a tool for improving database design, but has become a fundamental requirements definition technique for all business analysts, whether they are primarily concerned with data structures, application logic, user interface behavior, or business processes.

A key driver is that applying data modelling early in requirements definition allows analysts and clients to develop a common understanding of the business entities (e.g., Customer, Order, Product, Part, etc.) that business processes and information systems deal with, their interrelationships, and the rules that govern them.  This eliminates the problems of inconsistent terminology and conflicting assumptions that otherwise plague application development, package selection and implementation, system integration, and process redesign projects.

This workshop introduces entity-relationship modelling from a non-technical perspective, thoroughly covering the basic components of a data model – entities, relationships, attributes, and identifiers.  In addition to showing how and when to use these components in developing a data model, it includes far more advice on the process of developing a data model than other courses, including specific methods for getting subject matter experts involved and maintaining their commitment. The content is presented within the context of a clearly-defined, three-phase data modelling methodology that supports progressive detail and precision.

  • This workshop is packed with practical tips, techniques, “scripts,” checklists, and guidelines for the analyst. All of the material is based on years of project experience; abstract theory is avoided.
  • The emphasis is on “business-friendly” techniques which support and encourage the full involvement of non-technical subject matter experts, which is essential for quality data models

Learning Objectives

  • Apply a variety of techniques that support the active participation and engagement of business professionals and subject matter experts
  • Use entity-relationship modelling to depict facts and rules about business entities at different levels of detail, including conceptual (overview) and logical (detailed) models
  • Use top-down and bottom-up approaches to initiating development of a data model
  • Recognise the four basic patterns in data modelling, and when to use them
  • Effectively use definitions and assertions (“rules”) as part of data modelling
  • Use an intuitive approach to data normalisation within an entity-relationship model
  • Apply various techniques for discovering and meeting additional requirements
  • Read a data model, and communicate with specialists using the appropriate terminology 

Course Outline

Essentials of Data Modelling
  • What really is a data model?
  • Essential components – entities, relationships, and attributes
  • Hands-on case study – how data modelling resolved business issues, and supported other business analysis techniques
  • The basics of diagramming – Entity-Relationship Diagrams (“ERDs”)
  • The narrative parts of a data model – definitions and assertions
  • Group exercise – getting started on a data model, then refining it
  • Common misconceptions about data models and data modelling
  • The real purpose of a data model
  • Three types of data models – different levels of details for different purposes
  • Contextual, Conceptual, and Logical Data Models – purpose, audience, definition, and examples
  • How data models help in process improvement, requirements definition, and reporting
  • Forward- and reverse-engineering uses of data modelling
  • Overview of a three-phase methodology for developing a data model
  • References – books and useful web sites

Establish the Initial Conceptual Data Model

  • Top down vs. bottom up approaches to beginning a data model – when is each appropriate?
  • Advantages of a bottom-up approach
  • A bottom-up approach focusing on collecting and analyzing terminology
  • A structure for sorting terms and discovering entities
  • Exercise – developing an initial conceptual data model
  • Entities – what they are and are not
  • Guidelines for naming and defining entities
  • Three questions to help you quickly develop clear, useful entity definitions
  • Five criteria that entities must satisfy, and four common errors in identifying entities
  • Exercise – identifying flawed entities
  • Identifying relationships
  • Fundamental vs. irrelevant or transitive relationships
  • Good and bad relationship names
  • Multiplicity or cardinality – 1:1, 1:M, and M:M relationships, and useful facts about each
  • Common errors and special cases – recursive, multiple, and supertype-subtype relationships
  • Attributes – guidelines and types
  • Attributes in conceptual models vs. logical models

Develop the Initial Logical Data Model By Adding Rigour, Structure and Detail

  • What’s involved in developing a logical model – shifting the focus from entities to attributes
  • Multi-valued, redundant, and constrained attributes, with simple patterns for dealing with each
  • An understandable guide to normalisation – first, second, and third normal forms
  • Higher order (fourth and fifth) and Boyce-Codd normal forms
  • Guidelines for a smooth progression from conceptual to logical
  • Exercise – developing the initial logical data model
  • Four types of entities – kernel, characteristic, associative, and reference
  • Guidelines and patterns for dealing with each type of entity
  • How to draw your E-R Diagram for maximum readability and correctness
  • Optional and mandatory relationships
  • Considering time and history when looking at relationships
  • Six questions to ask whenever a data range appears in a data model
  • Identifying and dealing with transitive relationships – clues and proof

Refine and extend the logical data model by discovering and meeting new requirements

  • Attribute granularity – definitions of non-atomic and semantically overloaded attributes
  • Guidelines for making non-atomic attributes atomic
  • The perils of semantic overload, and what to do about it
  • Dealing with derived attributes, and when to show them on the model
  • A classword-based approach to attribute naming
  • Typical attribute documentation
  • A common source of confusion and disagreement – primary keys
  • What primary keys are, what they’re really for, and three essential criteria
  • Alternate and foreign keys
  • Why meaningless primary keys are used, and guidelines for creating them
  • Guidelines for reference data
  • Pulling it together – key techniques and guidelines covered in the class so far
  • Using event analysis to discover additional requirements
  • Exercise – using event analysis and extending a data model
  • Presentation by teams of their solutions
  • How data modelling relates to process modelling, use cases, and services
  • A layered framework for business analysts
  • How other techniques (e.g., workflow modelling) support data modelling
  • A three-step procedure for meeting new requirements
  • Advice on extending the model in an orderly fashion
  • Exercise – meeting new requirements on the data model
  • Recap – contextual, conceptual, and logical data models
  • Different skills and participants for conceptual vs. logical modelling
  • How the modeler’s/analyst’s role changes as a project progresses
  • A little philosophy for effective data modelling
  • The four Ds of data modelling – definition, dependency, detail, and demonstration
  • Wrap-up – the approach we followed throughout the class

Who It's For

  • New or experienced Data Modellers, Data Analysts, and DBAs will benefit from the workshop’s practical methods and guidelines.
  • Business Analysts and Application Designers/Developers who need to understand data modelling and how it supports requirements definition or process analysis.
  • Business Professionals and Managers who need to understand how this technique can uncover and resolve inconsistency in business terminology, policy, and rules.


Sr. Consultant, Clariteq Systems Consulting Ltd.

Alec Sharp has managed his consulting and education business, Clariteq Systems Consulting Ltd., for 35 years. Serving clients worldwide, Alec’s expertise includes business architecture, data modelling, project recovery, and, of course business process change. In addition to his consulting practice, he conducts top-rated workshops and conference presentations on four or five continents a year. Alec is the author of “Workflow Modeling, second edition” (Artech House, 2009,) which is widely used as a consulting guide and university text, and is a best seller in the field with a “5 star” rating. He was also the sole recipient of DAMA’s 2010 Professional Achievement Award, a global award for contributions to the Data Management field.

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

Register On-line:
9-10 October 2017, London

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