The course starts at 09.30 am and ends at 5 pm. Registration commences at 08.30 am.

• What really is a data model?
• Essential components – entities, relationships, and attributes
• 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 modeling
• 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 modeling
• Overview of a three-phase methodology for developing a data model
• References – books and useful web sites

• 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

 

Phase 2 – Develop the initial logical data model by adding rigor, 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 normalization – 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 whenver a data range appears in a data model
• Identifying and dealing with transitive relationships – clues and proof

• 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 modeling relates to process modeling, use cases, and services
• A layered framework for business analysts
• How other techniques (e.g., workflow modeling) support data modeling
• 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 modeling
• How the modeler/analysts’s role changes as a project progresses
• A little philosophy for effective data modeling
• The four Ds of data modeling – definition, dependency, detail, and demonstration
• Wrap-up – the approach we followed throughout the class.

Taking part in this two-day workshop will only cost 1305 Euro when registering 30 days beforehand and 1450 Euro per person after the Early Bird period expires (excl. 21% Dutch VAT). This also covers documentation, lunch, tea/coffee.
If you register for the workshop Advanced Data Modelling by Alec Sharp at the same time, you will receive discount and pay € 2479.50 for both workshops and € 2755,– after expiration of the early bird registration.

Members of the DAMA NL, Belux or UK Chapter are eligible for 10 percent discount on the registration fee.

In completing your registration form you declare that you agree with our Terms and Conditions.
Extra discounts
Discounts are available for group bookings of two or more delegates representing the same organization made at the same time. Ten percent off for the second and third delegate and fifteen percent off for all delegates when registering four or more delegates (all delegates must be listed on the same invoice).
This cannot be used in conjunction with other discounts.
Payment
Full payment is due prior to the event. An invoice will be sent to you containing our full bank details including BIC and IBAN. Your payment should always include the invoice number as well as the name of your company and the delegate name.

For Credit Card payment please contact our office by e-mail or through our contact form mentioning your phone number to obtain your credit card information. Never mention your credit card details in our registration form, contact form or in e-mail messages.