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Chapter 6, “Subject Area Modeling,” focuses on three types of subject area models,
each being a very powerful validation and scoping tool. The Business Clean Slate
model helps us understand a business area independent of any applications. The Ap-
plication Clean Slate builds on the Business Clean Slate and focuses only on what is
important for the application. The Early Reality Check compares the new application
with an existing application architecture to assess overlap and impact.
Chapter 7, “Logical Data Analysis,” presents four powerful data element capture and
validation tools. The Data Element Family Tree captures data element name, alias, def-
inition, business purpose, default value, transformations, format, and nullability. The
Data Element Grain Matrix captures the relationships between the facts and their re-
porting levels. The Data Quality Capture Template contains the criteria and compari-
son information between data and meta data, and the Data Quality Validation Tem-
plate provides proof that the data quality of each data element has been properly
reviewed.
Chapter 8, “The Normalization Hike and Denormalization Survival Guide,” presents
the tools for normalizing and denormalizing your data requirements. The Normaliza-
tion Hike is a set of rules and guidelines that can lead you to the summit of com-
plete understanding for your application. The Denormalization Survival Guide is a
question-and-answer approach to determining where to denormalize your data
model. When you are done asking these questions for each relationship, you will have
a physical data model at the appropriate level of denormalization.
Chapter 9, “The Abstraction Safety Guide and Components,” helps minimize the im-
pact future data requirements will have on our model and the resulting database de-
sign. The Abstraction Safety Guide is a set of three questions that will help you deter-
mine where, if at all, you need to abstract on your data models. The Abstraction
Components tool is a collection of the most useful of these abstraction building blocks.
The components I discuss in this chapter are those you can use in your own applica-
tion models to replace sections of your models that need more flexibility. These com-
ponents can exist at the entity, relationship, or data element levels.
Chapter 10, “Data Model Beauty Tips,” takes your design beyond the immediate ap-
plication requirements, by focusing on tips for improving the visual appearance of the
logical and physical data models. These tips are offered at the entity, relationship and
data element levels of detail.
Chapter 11, “Planning a Long and Prosperous Career in Data Modeling”, focuses on
my advice, which I follow as a data modeler. This advice consists of what I have
learned over the years, either from my own experiences and from the successes and
failures of those around me. Following this advice can help you become a more suc-
cessful data modeler. This advice is phrased in the format of a Top Ten list.
Notation and Structure in This Book
I have used several consistent formats to show information throughout the chapters.
xviii
INTRODUCTION