Developing High Quality Data Models

Uses examples to show you how to identify a number of data modeling principles and analysis techniques that enables you to develop data models that consistently meet business requirements. This title includes principles and techniques that are applicable in government and industry.

Autorentext
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

Klappentext

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.

Zusammenfassung
A multitude of problems is likely to arise when developing data models. Using real-world examples, this title 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.

Inhalt

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