Het programma start om 09.30 uur en duurt tot 17.00 uur. Registratie is mogelijk vanaf 08.30 uur.

[Indien Online format] U ontvangt van ons ruim vooraf de documentatie, online meeting instructies en uiteraard ook de uitnodiging met hyperlink voor toegang tot de virtuele sessie. Het programma start om 13:30 uur en duurt tot 17:00 uur. Log tijdig in en controleer vooraf uw geluid- en video instellingen.

Onderwerpen

Essentials of Data Modelling

• What really is a data model or concept model?
• Essential components – entities, relationships, attributes, and rules
• Hands-on case study – how data modelling resolved business issues, and supported other business analysis techniques
• Guidelines for comprehension – how to lay out 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
• Contextual, Conceptual, and Logical Data Models – purpose, audience, definition, and examples
• Overview of a three-phase methodology for developing a data model

Establishing the initial conceptual data model

• Top down vs. bottom up approaches to beginning a data model – when is each appropriate?
• 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
• Exercise – identifying flawed entities
• Six criteria that entities must satisfy, and four common errors in identifying 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

Developing the initial logical data model by adding rigor, structure, and detail

• Transition to the 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
• 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
• 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
• The four Ds of data modelling – definition, dependency, detail, and demonstration
• E-R Diagramming – symbol sets and their problems, rules for readability and comprehension

Correctly handling attributes

• Granularity – dealing with non-atomic and semantically overloaded attributes
• Dealing with reference data and the “types vs. instances” problem
• Three attributes that always need a qualifier
• Vector modelling – entity or attribute?

Interesting structures – generalisation, recursion, and the two together

• Generalisation (subtyping) – when to use it, and when not to
• Generalisation with and without specification
• Guidelines for using recursive relationships
• Generalisation and recursion working hand-in-hand as a cure for literalism
• Recognizing lists, trees, and networks, and modelling them with recursive relationships
• Modelling difficult rules by combining generalisation (subtyping) and recursion
• Staying clear on generalisation vs. roles, states, and aggregation

Modelling time, history, and time-dependent business rules

• Historical vs. audit data, and when to show them on a data model
• Thanks, Sarbanes-Oxley! Why we need “as-of reporting” and how to model data corrections
• “Do you need history?” – how to tell when your client is misleading you
• Modelling time – special considerations for recording past, present, and future values
• Four variations on capturing history in a data model
• Seven questions you should always ask when a date range appears

Modelling rules on relationships and associations

• Using multi-way associations to handle complex rules
• “Use your words” – how assertions, scenarios, and other techniques will improve your modelling
• Associative entities – circular relationships, shared parentage, and other issues
• Alternatives for modelling constraints across relationships
• Advanced normal forms – how to quickly recognize potential 4NF and 5NF issues
• A simpler view – why the five normal forms could be reduced to three

Preparing and delivering a data model review presentation

• Context – your audience, and why the model matters to them
• It’s a story, not a data model! Building a storyboard
• Five key techniques for presenting data models or other technical subjects
• The mechanics of the data model review presentation
• A demonstration

Bridging the “E-R vs. Dimensional” divide – the world’s shortest course on dimensional modelling

• The perils of dimensional modelling without understanding the underlying E-R model
• Spotting facts and dimensions – the relationship between dimensional models and E-R models
• Saving time – building a first-cut dimensional model from an ER model