This course is designed for individuals who are looking to deepen their understanding of data modelling from a non-technical perspective. It is ideally suited for specialist data modellers, data architects, data analysts, and database administrators. The course is also appropriate for business analysts, business architects, enterprise architects, and application architects. It is also beneficial for application/solution developers, especially those working in Agile teams. Furthermore, the course would be useful to Business Intelligence (BI) professionals, Data Warehouse (DW) professionals, big data specialists, data scientists, analytics specialists, and data lake implementers. Lastly, business professionals, Subject Matter Experts (SMEs), and Project/Programme Managers involved in the analysis, design, and development of a system would also benefit greatly from this course.

Business Oriented Data Modelling course offers an extensive introduction to Concept and Logical Modelling, enabling you to understand and implement the essential components of a data model, including entities, relationships, attributes, and rules. It provides practical guidance, tips, and techniques that encourage active participation and engagement by business professionals. The course provides a clear path to rapidly develop and extend useful models through the application of common patterns in data modelling. It emphasises developing a data model that supports the identification of use cases/user stories and services. Moreover, this course can help you enhance your skills in reading data models, communicating with specialists using appropriate terminology, and drawing Entity Relationship Diagrams (ERDs) for maximum readability.

This course offers a comprehensive approach to data modelling, covering a variety of topics, from the essentials of data modelling to preparing and delivering a data model review presentation. The course consists of a series of interactive sessions, including group exercises, individual tasks, and a hands-on case study. You will learn to develop both conceptual and logical models using repeatable methods for Agile modelling, how to handle attributes, and interesting structures such as generalisation, recursion, and their combination. The course also delves into modelling time, history, and time-dependent business rules, as well as rules on relationships and associations.

Unless stated otherwise, there is no exam included in this course.

Upon successful completion of the course, attendees will receive a certificate of completion from IRM UK.

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