Developing High Quality Data Models book cover

Developing High Quality Data Models

A multitude of problems is likely to arise when developing data models. With dozens of attributes and millions of rows, data modelers are always in danger of inconsistency and inaccuracy. The development of the data model itself could result in difficulties presenting accurate data. The need to improve data models begins with getting it right in the first place.

Using real-world examples, Developing High Quality Data Models walks the reader through identifying a number of data modeling principles and analysis techniques that enable the development of data models that both meet business requirements and have a consistent basis. The reader is presented with a variety of generic data model patterns that both exemplify the principles and techniques discussed and build upon one another to give a powerful and integrated generic data model. This model has wide applicability across many disciplines in government and industry, including but not limited to energy exploration, healthcare, telecommunications, transportation, military defense, transportation, and more.

Audience

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.

Paperback, 408 Pages

Published: December 2010

Imprint: Morgan Kaufmann

ISBN: 978-0-12-375106-5

Reviews

  • "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


Contents

  • Preface

    Chapter 1- Introduction

    Chapter 2- Entity Relationship Model Basics

    Chapter 3- Some types and uses of data models

    Chapter 4- Data models and enterprise architecture

    Chapter 5- Some observations on data models and data modeling

    Chapter 6- Some General Principles for Conceptual, Integration and Enterprise Data Models

    Chapter 7- Applying the principles for attributes

    Chapter 8- General principles for relationships

    Chapter 9- General principles for entity types

    Chapter 10- Motivation and overview for an ontological framework

    Chapter 11- Spatio-temporal extents

    Chapter 12- Classes

    Chapter 13- Intentionally constructed objects

    Chapter 14- Systems and system components

    Chapter 15- Requirements specifications

    Chapter 16- Concluding Remarks

    Chapter 17- The HQDM Framework Schema

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