Developing High Quality Data Models
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Developing High Quality Data Models

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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.
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Product details

  • Paperback | 408 pages
  • 190.5 x 231.14 x 25.4mm | 703.06g
  • Morgan Kaufmann Publishers In
  • San Francisco, United States
  • English
  • Approx. 120 illustrations; Illustrations, unspecified
  • 0123751063
  • 9780123751065
  • 1,776,744

Table of contents

Chapter 1: What are Data Models For?

Chapter 2: Different Sorts of Data Models

Chapter 3: Languages and Notations for Data and Data Models

Chapter 4: Layout of Data Models

Chapter 5: Reviewing and Improving Data Models

Chapter 6: High Quality Data Models

Chapter 7: Principles for Data Models

Chapter 8: A Generic Framework for a Changing World

Chapter 9: Integration of Data Models

Chapter 10: Future Directions
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Review Text

"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 reviews.com
"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 reviews.com
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Review quote

"This book deals with an emerging topic of interest to a large sector of the data modeling community. There is a strong need to explain the development of a generic approach to practitioners in the data modeling community - and this book addresses that need. -- Chris Partridge, Chief Ontologist, BORO Solutions (UK) (Business Object Reference Ontology)

"I expect that application developers will find this book of interest, particularly if they want to grow professionally."--Fred Cummins, Fellow, Hewlett-Packard Enterprise Services
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About 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
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