The book is the result of the author’s long professional experience in data modeling, and more generally in computers and their business applications. West has worked for more than 20 years on complicated and large-scale problems, having leading roles as a data modeler in large enterprises. The book has three main purposes: help readers understand the usefulness of data models in enterprise architecture (EA); address problems in the development of data models; and transfer knowledge and experience to data modelers on how to develop consistent, generic, and high-quality data models.

The style of the book is mostly technical, the level is quite advanced, and the target audience includes professionals, programmers, and software engineers who are familiar with at least the basics of data modeling. It would also be useful for readers to have some background in object-oriented programming, unified modeling language (UML), and data structures. The book’s aim is clear: to show how data models can improve the quality of information in decision making. The key notion of “quality” is defined and used practically as “fit for purpose,” while the whole data modeling approach is based on ontologies.

The book consists of 17 chapters, divided into four parts.

Part 1 provides motivations, notations, different uses of data models (especially for EA), and their limitations and challenges. Chapter 1 is a smooth introduction to the motivations and purposes of the book, while chapter 2 presents the fundamental principles and notations for representing data. It also covers the basics of the modeling language EXPRESS, which is used throughout the book. Chapter 3 identifies different types of and uses for data models, and explores the data integration architecture and its requirements and challenges. Chapter 4 connects data models with EA under the general framework of information technology (IT) in business processes. Chapter 5 makes some observations on data models and data modeling, and specifically discusses the models’ limitations and challenges in modeling, emphasizing the importance of the ontological approach.

Part 2 presents general principles for data models--especially from the ontological approach--and their applications to attributes, relationships, and entity types. Chapter 6 starts by discussing the differences between normalization (the elimination of repeated patterns in data) and the ontological approach that is adopted throughout the book. Then, it presents general principles for conceptual, integrated, and enterprise data models, regarding rules, statements, definitions, the naming of entity types, and relationships. Chapters 7 through 9 are devoted to the application of principles to attributes, relationships, and entity types.

Part 3 addresses issues related to the ontological framework for developing consistent data models. Chapter 10 offers an interesting overview of the motivations and foundations of an ontological framework using philosophical and logical concepts. Chapter 11 deals with space and time issues, covering spatiotemporal extents; chapter 12 discusses classes under a set theory perspective; and chapter 13 is about intentionally constructed objects, such as agreements and signs. Chapter 14 looks at systems and their components, and chapter 15 considers procurement processes and requirement specifications. Chapter 16 concludes this part with some final remarks.

Part 4 provides a full data model, the high-quality data model (HQDM) schema, using both EXPRESS-G diagrams and lexical EXPRESS to describe specifications for various types of objects. Finally, the appendix provides a mapping between the HQDM schema and the ISO 15926-2 model.

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.