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