A Business-oriented Approach to Data Modelling

About Data Modelling

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 maximize 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.

The data modelling workshop

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.

Two points are worth emphasizing:

• 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.

Objectives

On workshop completion, participants will be able to:

• 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
• Recognize 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 normalization 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.

Prerequisites

None. However, business analysts who expect to do extensive workflow modelling will find that some understanding of information systems concepts may be helpful in establishing context.

Target Audience

New or experienced data modelers, data analysts, and DBAs will benefit from the workshop’s practical methods and guidelines. The workshop is also very popular with business analysts and application designers/developers needing to understand data modelling and how it supports requirements definition or process analysis. As well, it’s suitable for business professionals and managers needing to understand how this technique can uncover and resolve inconsistency in business terminology, policy, and rules.

Course Topics

• Overview of data modelling: terminology, types of models, and key concepts
• The essential data model components – entities, relationships, attributes, and identifiers
• A three-phase approach to completing a data model
• Initiating a conceptual data model using a bottom-up approach
• Four common errors in identifying entities, and how to avoid them
• Eliminating confusion and misunderstanding with well-structured entity definitions
• Four entity types, and rules and guidelines for dealing with them
• Adding detail and rigor – evolving the conceptual model into a logical data model
• Patterns for common situations – multi-valued attributes, redundant data, and reference data
• The world’s simplest guide to normalization
• Primary and foreign keys in logical data models
• Meaningless primary keys – rationale and limitations
• Specifying assertions and constraints – rules that can’t be shown on the E-R diagram
• Drawing the Entity-Relationship Diagram for maximum readability
• Techniques for discovering, assessing, and meeting new requirements
• Wrap-up – summaries and resources.

At the top of this page you can download the PDF brochure of this workshop.