数据建模实践：OOAD与工具解析

数据建模

OOAD

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"数据建模OOAD是一种将业务需求转化为结构化数据模型的过程，它在软件开发中的重要性不言而喻。数据建模有助于确保系统设计满足业务需求，提高数据质量和系统的可维护性。本文深入浅出地介绍了数据建模的概念、方法以及在实际项目中的应用。数据建模首先涉及理解什么是数据模型。数据模型是描述数据结构和关系的工具，它包括实体、属性、关系等核心元素。实体代表现实世界中的对象，属性则描述实体的特征，而关系定义了实体之间的相互作用。例如，在一个银行系统中，客户（实体）可能有账户（属性），账户之间可能存在关联（关系）。 OOAD（面向对象分析与设计）是数据建模的一种方法，它强调用对象来表示业务实体，并通过类和对象的关系来描绘业务流程。在OOAD中，数据模型与对象模型是紧密相关的，对象模型描述了对象的结构和行为，而数据模型关注静态结构。数据建模的存储位置通常是在数据建模工具中，如DataModeler’s Workbench，这是一个专门用于创建和管理数据模型的平台。这些工具提供了图形化界面，便于设计、审查和交流模型。它们还支持各种数据建模方法，如概念数据模型、逻辑数据模型和物理数据模型，每个阶段都有其特定的目的和受众。全局关系是数据建模中的另一个关键点，它涉及到不同模型间的连接和一致性。在大型系统中，多个局部模型可能需要整合成一个全局模型，确保所有数据的一致性和完整性。这需要对数据的全面理解和良好的协调机制。《DataModeler’s Workbench》一书获得了业界专家的高度评价，书中提供了实用的技术和技巧，帮助读者理解如何有效地使用数据建模工具和方法。作者Steve Hoberman通过真实案例展示了如何解决数据建模过程中遇到的问题，并强调了数据建模作为人类过程的一面，即需要沟通、协作和迭代。数据建模和OOAD是软件开发中的关键步骤，它们帮助我们构建能够准确反映业务逻辑的系统，提升系统的稳定性和扩展性。通过持续学习和实践，无论是初学者还是经验丰富的从业者，都能在这个领域中不断进步，提高自己的专业技能。"

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us in capturing, modeling, and validating data requirements to improve the speed, ac-

curacy, flexibility, and consistency of data warehouse and operational applications.

This book has two main purposes:

1. To provide you with a set of data modeling tools that you can start using today

on your data model designs. This book will take you through a number of data

modeling tools, starting with several tools that need to be applied before any

analysis or modeling work takes place and ending by properly arranging the data

elements on the model.

2. To stimulate your imagination to create your own tools or variations of those in

this text. Because I could not possibly discuss all data modeling tools, you should

be able to think up your own that are most appropriate for your assignments, or-

ganizations, and industries after reading about the tools in this book.

I wrote this book because I believe using data modeling tools that already have been

proven to improve designs can save data modelers lots of time, effort, and trial and er-

ror in creating their own data modeling tools. This book fills a very important niche in

our industry, because many data modeling books exist that deal with formal data

modeling “how to” or are specific to a type of application, such as data warehousing.

What is lacking is a book on practical tools to help with the data modeling process and

data modeling deliverables. These practical tools are ones that can be applied to any

type of application, regardless of whether they are reporting or operational in nature

and regardless of your industry or application methodology.

I know that at certain points during the reading of this book you might ask yourself,

“Are these data modeling techniques for data warehouse designs or for operational

designs?” The answer is that most of the techniques presented in this text are for all

types of applications. The answers or results of some of these tools might vary, de-

pending on whether it is a reporting or operational application; however, the tools can

be used for both. There are a few exceptions to this general rule, which I will mention

when I discuss the specific tools in the text.

How This Book Is Organized

This book is organized into three parts, according to when these tools should be ap-

plied in the data modeling process. For example, the Subject Area Family Tree in

Chapter 5 should always precede the Data Element Family Tree in Chapter 7. Al-

though I recommend reading the text in the order in which it appears, once you have

read Chapters 2 and 4, you will have a very good understanding of the sequence for

the rest of the text. Then you can jump to the sections that most interest you. I refer

back to previous tools in each chapter, however, making it more logical to read the text

from beginning to end.

Here is an overview of each of the three parts:

■■

Part 1, Building the Foundation. Chapters 1 through 4 offer several tools to help

you as the data modeler utilize the data modeling education and communication

process.

xvi

INTRODUCTION

@Team-FLY

■■

Part 2, Analyzing the Requirements. Chapters 5 through 7 focus on capturing

and validating the subject area requirements for an application, as well as on the

detailed data elements that will provide the inputs to our logical data modeling

phase.

■■

Part 3, Modeling the Requirements and Some Advice. Chapters 8 through 10 fo-

cus on creating the optimal logical and physical data models for our application.

A series of tools is presented for modeling the data requirements in a flexible

way, while considering the database performance and storage ramifications. As a

bonus, Chapter 11 is included in this part. Chapter 11 concludes this book with

some advice that I have learned over the years and continuously follow as a data

modeler.

Here is a summary of each chapter:

Chapter 1, “Using Anecdotes, Analogies, and Presentations to Illustrate Data Mod-

eling Concepts,” focuses on the most effective ways to clearly communicate facets

of data modeling. Anecdotes are short stories that can work very well to explain a

concept or term. Analogies compare data modeling concepts with something the au-

dience is very familiar with, to highlight some of the data modeling traits. Presenta-

tion steps are the sequence of steps to go through when creating a data modeling

presentation.

Chapter 2, “Meta Data Bingo,” helps define the standard types of meta data for your

project and organization. Meta Data Bingo is a game where the people on your proj-

ect team complete Bingo cards identifying which types of meta data are most im-

portant to capture. These Bingo cards capture meta data at a number of different lev-

els, including project, data model, subject area, entity, and data element levels of

detail.

Chapter 3, “Ensuring High-Quality Definitions,” focuses on a tool called the Defini-

tion Checklist. Examples of applying the Definition Checklist at the subject area, en-

tity, and data element levels are provided.

Chapter 4, “Project Planning for the Data Modeler,” includes four project planning

templates that contain the complete and sequential set of tasks required to successfully

finish data modeling deliverables in a realistic timeframe.

Chapter 5, “Subject Area Analysis,” offers tools to complete the deliverables for iden-

tifying and capturing the subject areas for an application. The Subject Area Checklist

is a complete list of subject areas within the new application, along with their defini-

tions. The Application Subject Area CRUD (Create, Read, Update, and Delete) Matrix

contains the subject area gaps and overlap that can exist between your new applica-

tion and existing applications. This is a powerful tool for scoping your application.

The In-the-Know Template identifies the people and documentation you will need as

resources to complete your data modeling deliverables of this new application. The

Subject Area Family Tree contains the source applications for each subject area

and several other critical pieces of information. The Subject Area Grain Matrix cap-

tures the reporting levels for each measurement or fact subject area using a spread-

sheet format.

xvii

INTRODUCTION

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

NOTE

I show Notes, Warnings, and Tips throughout this text in this format. In this way you

can easily spot these as you read each chapter. Notes provide additional informa-

tion on the topic being discussed, Warnings contain pitfalls and problems you want

to avoid, and Tips are short yet important words of advice.

I use italics to discuss real-life situations you might one day find yourself in. For ex-

ample, in Chapter 7 you will see this story:

You have completed all of the subject area analysis tools for your application, including

the Subject Area Family Tree. Now what? You need to identify the data elements within

each subject area, along with their sources and any transformations. You need to take the

Subject Area Family Tree down to the next level. What you identify at this detailed level

will be the data elements that will be used in the remainder of the development. This

means that data elements we do not identify in this stage will probably not exist in the fi-

nal application. Therefore, it is extremely important to organize these data elements in as

simple and clear a way as possible to maximize capturing the most complete and accurate

information. We need the Data Element Family Tree.

In addition, at the beginning of this Introduction and at the beginning of each of the

chapters is a short story that relates a personal vacation experience to the tools in each

chapter. There are two areas I find very near and dear to my heart: data modeling and

vacation. So why not bring them both into each chapter?

You will also note that each chapter has similar headings:

About This Chapter. Describes in a few paragraphs what is contained in the chapter,

along with the sequence of topics to be discussed and a very short overview of

each of the tools contained in the chapter. Also in this section there usually are a

few sentences about how this chapter relates to the previous chapters in the text.

What Is This Tool? An explanation of the tool, including its goals and usually a few

simple examples. Note that this section and the next two sections are repeated for

each tool in the chapter.

Using the Tool. Includes the steps you would take to apply this tool.

The Tool In Practice. Contains a detailed example of applying the tool, using the

steps described under the section Using The Tool.

Summary. Briefly summarizes the key points from the chapter and relates the chap-

ter to subsequent chapters.

Who Should Read this Book

This book is for you if you have some familiarity with data modeling and data mod-

eling concepts. Included are data modelers and administrators, analysts, designers,

and system and data architects. You should not read this book if you are brand new to

xix

INTRODUCTION

data modeling, because there are many very good introductory texts on data model-

ing, some of which are listed in the suggested reading list at the end of this book. You

should read this book if you have had some practical data modeling experience.

I strongly believe that even those of us with a dozen or more years of data model-

ing experience might find a few useful tools and techniques between the covers of

this book.

If you have less than 3 years’ experience in data modeling, I would highly recommend

reading this book from cover to cover. If you have more than 3 years’ experience, I

would start with Chapter 4, “Project Planning for the Data Modeler,” which provides

a good overview for Chapters 5 through 10 and will help you navigate directly to the

sections of the text that most interest you. If you have a lot of experience, I would rec-

ommend skipping the section Normalization Hike in Chapter 8 and reading only the

second half of Chapter 8, What Is the Denormalization Survival Guide. For all, I rec-

ommend reading Chapter 11 at some point. Although it is short, it contains valuable

advice for the data modeler.

I would also recommend marking those sections that you find most interesting in this

text and referring back to them as you are doing your data modeling activities. Many

times when we read a text, we nod our heads with understanding as we complete each

chapter but forget to apply the information as we are actually doing the work. I rec-

ommend using this book as an active reference as you are modeling. For example, you

might ask yourself, “How do I structure my Data Element Family Tree again?” and

then quickly find the answer in Chapter 7.

What’s on the Web Site?

If you visit my Web site, www.wiley.com/compbooks/hoberman, you will find ad-

ditional tools and templates and updates for existing tools from this text. If you do

not want to recreate some of the spreadsheets in this book, you can go to this Web

site and download an empty spreadsheet as your starting point. I also will continue

to update this Web site with new tools and advice for the data modeler. This Web site

also contains pointers to some of my favorite data modeling sites on the World

Wide Web.

You can contact me through the companion Web site or at me@stevehoberman.com.

From Here

I have written this book with the intention of making it very practical and having it

contain information that easily can be applied to any type of application. I would like

you to use your imagination in dreaming up new tools as you are reading about the

tools in these pages. Think up new tools or customizations to the tools included herein

that can directly impact your current assignments.

As the field of data modeling increases in importance, there will be more demands

placed on the data modeler. To look at it in a more positive light, there will be more

INTRODUCTION

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