Developing High Quality Data Models

1st Edition

Authors: Matthew West
Paperback ISBN: 9780123751065
eBook ISBN: 9780123751072
Imprint: Morgan Kaufmann
Published Date: 30th December 2010
Page Count: 408
Tax/VAT will be calculated at check-out
Compatible Not compatible
VitalSource PC, Mac, iPhone & iPad Amazon Kindle eReader
ePub & PDF Apple & PC desktop. Mobile devices (Apple & Android) Amazon Kindle eReader
Mobi Amazon Kindle eReader Anything else

Institutional Access


Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data models in both developing an Enterprise Architecture and in supporting Information Quality; common problems in data model development; and how to develop high quality data models, in particular conceptual, integration, and enterprise data models. The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types and uses of data models; and the place of data models in enterprise architecture. Part 2 introduces some general principles for data models, including principles for developing ontologically based data models; and applications of the principles for attributes, relationship types, and entity types. Part 3 presents an ontological framework for developing consistent data models. Part 4 provides the full data model that has been in development throughout the book. The model was created using Jotne EPM Technologys EDMVisualExpress data modeling tool.

This book was designed for all types of modelers: from those who understand data modeling basics but are just starting to learn about data modeling in practice, through to experienced data modelers seeking to expand their knowledge and skills and solve some of the more challenging problems of data modeling.

Key Features

Uses a number of common data model patterns to explain how to develop data models over a wide scope in a way that is consistent and of high quality

Offers generic data model templates that are reusable in many applications and are fundamental for developing more specific templates

*Develops ideas for creating consistent approaches to high quality data models


This book is intended for data management professionals with job functions that include data modeler; data architect; database designer; database application developer and application architect.

Table of Contents

Preface Part 1 Motivations and Notations Chapter 1 Introduction 1.1 Some Questions about Data Models 1.2 Purpose 1.3 Target Audience 1.4 What Is a Data Model? 1.5 Why Do We Do Data Models? 1.6 Approach to Data Modeling 1.7 Structure of This Book Chapter 2 Entity Relationship Model Basics 2.1 Oh, Its Boxes and Lines Again 2.2 Graphical or Lexical 2.3 Graphical Notations: Complexity vs. Understandability vs. Capability 2.4 Language and Notation Elements 2.5 Express-G 2.6 Notation for Instances and Classes 2.7 Layout of Data Models 2.8 Reflections Chapter 3 Some Types and Uses of Data Models 3.1 Different Types of Data Models 3.2 Integration of Data and Data Models 3.3 Concluding Remarks Chapter 4 Data Models and Enterprise Architecture 4.1 The Business Process Model 4.2 Information Architecture 4.3 Information Operations 4.4 Organization 4.5 Methodologies and Standards 4.6 Management 4.7 Wider Infrastructure 4.8 Enterprise Architecture Mappings 4.9 The Process/Data Balance Chapter 5 Some Observations on Data Models and Data Modeling 5.1 Limitations of Data Models 5.2 Challenges in Data Modeling Part 2 General Principles for Data Models Chapter 6 Some General Principles for Conceptual, Integration, and Enterprise Data Models 6.1 Data Modeling Approach 6.2 General Principles 6.3 Understanding Relationships 6.4 Principles for Data Models 6.5 Naughtiness Index Chapter 7 Applying the Principles for Attributes 7.1 Looking for Attributes Representing Relationships 7.2 Identifiers 7.3 What Other Attributes Might You Expect?


No. of pages:
© Morgan Kaufmann 2011
Morgan Kaufmann
eBook ISBN:
Paperback ISBN:

About the Author

Matthew West

Matthew West spent over 20 years as a leading data modeler for Shell where he was a key technical contributor to data modeling and data management standards and their application. Matthew was responsible for Shell's Downstream Data Model. He currently serves as the Director of Information Junction, a data architecture and analysis consultancy in the UK. He is also a key contributor to ISO 15926 (Lifecycle integration of process data) and ISO 8000 (Data and Information Quality). Matthew is a Visiting Professor at the University of Leeds

Affiliations and Expertise

Director of Information Junction, UK


"This guide to developing high quality data models provides practical instruction in understanding the core principle of data modeling and creating accurate models from complex databases. The work is divided into four sections covering the basics of data model types and uses, general principles for data model components and an ontological framework for consistent data models. A final section presents a complete, standards compliant data model created with the Jotne EPM Technology EDMVisusalExpress data modeling tool. Numerous illustrations, charts and sample programming code are included throughout the work and access to additional online content, including the sample data model, is provided. West is an experienced data modeler working in the energy field."--Book News, Reference & Research

"Overall, the book is a helpful guide for those who wish to go deep into the art of developing high quality data models. Readers will appreciate: how West connects data models with EA and business processes; the ontological approach, which offers a framework for formal, generic, and consistent models; the efficient use of diagrams for explaining the notions; and the philosophical concepts discussed throughout the text. The book is highly technical. Although it does not directly address people from academia, it will be very useful for related courses, especially those that deal with IT and business processes. Finally, the book highlights the importance of quality in data modeling for decision making."--Computing