This is the fourth edition of the training manual for the Data Modelling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com. The Master Class is a complete course on requirements elicitation and data modeling, containing three days of practical techniques for producing solid relational and dimensional data models. After learning the styles and steps in capturing and modelling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard(r). You will know not just how to build a data model, but also how to build a data model well. Two case studies and many exercises reinforce the material and enable you to apply these techniques in your current projects. By the end of the course, you will know how to: Explain data modeling building blocks and identify these constructs by following a question-driven approach to ensure model precision; Demonstrate reading a data model of any size and complexity with the same confidence as reading a book; Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard; Apply requirements elicitation techniques including interviewing and prototyping; Build relational and dimensional conceptual, logical, and physical data models through two case studies; Practice finding structural soundness issues and standards violations; Recognize situations where abstraction would be most valuable and situations where abstraction would be most dangerous; Use a series of templates for capturing and validating requirements, and for data profiling; Express how to write clear, complete, and correct definitions; Leverage the Grain Matrix, enterprise data model, and available industry data models for a successful enterprise architecture.

Read today's business headlines and you will see that many issues stem from people not having the right data at the right time. Data issues don't always make the front page, yet they exist within every organisation. We need to improve how we manage data -- and the most valuable tool for explaining, vaildating and managing data is a data model. This book provides the business or IT professional with a practical working knowledge of data modelling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives: Know when a data model is needed and which type of data model is most effective for each situation; Read a data model of any size and complexity with the same confidence as reading a book; Build a fully normalised relational data model, as well as an easily navigatable dimensional model; Apply techniques to turn a logical data model into an efficient physical design; Leverage several templates to make requirements gathering more efficient and accurate; Explain all ten categories of the Data Model Scorecard(r); Learn strategies to improve your working relationships with others; Appreciate the impact unstructured data has, and will have, on our data modelling deliverables; Learn basic UML concepts; Put data modelling in context with XML, metadata, and agile development.

This book provides the business or IT professional with a practical working knowledge of data modelling concepts and best practices, along with how to apply these principles with ER/Studio DA. You will build many ER/Studio DA data models along the way, applying best practices to master these ten objectives: You will know why a data model is needed and which ER/Studio DA models are the most appropriate for each situation; You will be able to read a data model of any size and complexity with the same confidence as reading a book; You will know how to apply all the key features of ER/Studio DA; You will be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio DA; You will be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design; You will improve data model quality and impact analysis results by leveraging ER/Studio DAs lineage functionality and compare/merge utility; You will achieve enterprise architecture through ER/Studio DAs repository and portal functionality; You will be able to apply ER/Studio DAs data dictionary features; You will learn ways of sharing the data model through reporting and through exporting the model in a variety of formats; You will leverage ER/Studio DAs naming functionality to improve naming consistency. This book contains four sections: Section I introduces data modelling and the ER/Studio DA landscape. Learn why data modelling is so critical to software development and even more importantly, why data modelling is so critical to understanding the business. You will also learn about the ER/Studio DA environment. By the end of this section, you will have created and saved your first data model in ER/Studio DA and be ready to start modelling in Section II. Section II explains all of the symbols and text on a data model, including entities, attributes, relationships, domains, and keys. By the time you finish this section, you will be able to read a data model of any size or complexity, and create a complete data model in ER/Studio DA. Section III explores the three different levels of models: conceptual, logical, and physical. A conceptual data model (CDM) represents a business need within a defined scope. The logical data model (LDM) represents a detailed business solution, capturing the business requirements without complicating the model with implementation concerns such as software and hardware. The physical data model (PDM) represents a detailed technical solution. The PDM is the logical data model compromised often to improve performance or usability. The PDM makes up for deficiencies in our technology. By the end of this section you will be able to create conceptual, logical, and physical data models in ER/Studio DA. Section IV discusses additional features of ER/Studio DA. These features include data dictionary, data lineage, automating tasks, repository and portal, exporting and reporting, naming standards, and compare and merge functionality.

Did you ever try getting Business and IT to agree on the project scope for a new application? Or try getting the Sales & Marketing department to agree on the target audience? Or try bringing new team members up to speed on the hundreds of tables in your data warehouse -- without them dozing off? You can be the hero in each of these and hundreds of other scenarios by building a High-Level Data Model. The High-Level Data Model is a simplified view of our complex environment. It can be a powerful communication tool of the key concepts within our application development projects, business intelligence and master data management programs, and all enterprise and industry initiatives. Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach. Know how to evaluate toolsets for building and storing your models. Practice exercises and walk through a case study to reinforce your modelling skills.

This book will provide the business or IT professional with a practical working knowledge of data modelling concepts and best practices, and how to apply these principles with PowerDesigner. You will build many PowerDesigner data models along the way, increasing your skills in first the fundamentals and later in the book the more advanced features of PowerDesigner. The book contains six sections: Section I introduces data modelling along with its purpose and variations. Also included is an explanation of the important role of a data modelling tool, the key features required of any data modelling tool, and an introduction to the essential features of PowerDesigner. Section II explains all of the components on a data model including entities, data elements, relationships, and keys, and describes how to create and manage these objects in PowerDesigner. Also included is a discussion of the importance of quality names and definitions for your objects. Section III dives into the relational and dimensional subject area, logical, and physical data models, and describes how PowerDesigner supports these models and the connections between them. Learn how to get information into and out of PowerDesigner, and improve the quality of your data models with a cross-reference of key PowerDesigner features with the Data Model Scorecard. Section IV contains a PowerDesigner workshop designed to consolidate everything for you. Section V focuses on additional PowerDesigner features (some of which have already been introduced) which make life easier for data modellers. Section VI discusses PowerDesigner topics beyond data modelling, including the XML physical model and the other types of model available in PowerDesigner. It also discusses the role of PowerDesigner in data management, using the DAMA Data Management Body of Knowledge (DAMA-DMBOK) framework.