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9780201616354

Data Warehouse Project Management

by ;
  • ISBN13:

    9780201616354

  • ISBN10:

    0201616351

  • Edition: 1st
  • Format: Paperback
  • Copyright: 2000-09-14
  • Publisher: Addison-Wesley Professional
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Summary

You have been a project manager for years and have successfully implemented many systems, but on your data warehouse project nothing seemed to work. All those proven techniques you've acquired over the years did not smooth the path. The methodology you so faithfully followed for years did not seem to help you as much in controlling the activities on the project. Tasks had to be repeated many times, and some new tasks that you had never considered before had to be performed. Roles and responsibilities assigned to your staff seemed inadequate and sometimes inappropriate. Your users had not planned on spending so much time on your project, and you had not realized what was going to be required of them. You knew your source files had some bad data, but you had not anticipated the impact it would have on the extract/transform/load (ETL) process.

Maybe you are just planning your first data warehouse project and you have heard that it will be different and difficult. In either case, whether you already managed a data warehouse project or you are planning your first data warehouse project, this book will help you pave the road for a successful implementation. But before you immerse yourself into the content of this book, we would like to explain how we organized the book and provide a roadmap to guide you.

Purpose of This Book

The hardest aspect to data warehousing is to manage a highly dynamic project. Data warehouse projects are dynamic because the requirements are usually not as well defined as they are for an operational system, and the process of building a data warehouse often leads to adjustments of these requirements or to discovery of new ones. Furthermore, these projects are staffed with talented but often inexperienced personnel. The complexity and learning curve on the new technology components are often underestimated. Management on both the IT and the business side all too often do not understand the complexity of a data warehouse project and put unreasonable demands on the team and the project manager. In other words, these projects are extremely challenging to manage.

The purpose of our book is to address the typical challenges on a data warehouse project and to educate the project manager on how to recognize the roadblocks and pitfalls. We give examples of risks and failures where we've encountered them, and we offer suggestions for avoiding them, or at least for mitigating them. At the end of every chapter is a section titled "A Cautionary Tale" that briefly describes our own experiences. Each chapter concludes with a workshop to practice what you have learned.

Who Should Read This Book

If this is your first or second attempt at a data warehouse project and you are not familiar or accustomed to using a different approach to managing this type of project, this book will help you. If you have already managed a data warehouse project that has been less than successful and you would like to do better on your next project, this book will provide some explanations for the difficulties you"ve encountered as well as suggestions for avoiding or mitigating these difficulties. This book is not meant to be a tutorial for basic project management. Instead, it is meant to be a guide for the experienced project manager who needs to know about the differences between a data warehouse project and a traditional project and who can use a helping hand from someone who has already been there.

How This Book Is Organized

Our approach to this book was to write each chapter in such a way that it could stand on its own because we recognized that some project managers will want to use it only as a reference. In order to accomplish this, it was unavoidable to include some overlapping material in various chapters. However, we present the overlapping material within the context of its chapter and hope that it will not affect the reading pleasure of those who wish to read this book cover to cover. Every chapter begins with a short list of its topics, followed by our experience from the field, highlighting landmines to watch out for, and concludes with a summary and a set of workshops. Some chapters also have appendices, which may be templates or worksheets, or additional guidelines. The workshops as well as the templates and appendices are stored in electronic format on the CD to make it easier for you to reproduce them. We encourage you to make use of these templates. They will help you standardize the process within your organization and simplify your own job. We made every effort to write this book in gender-neutral format. At times, however, when we did have to use a gender, we chose the masculine "he." We most certainly realize that there are many women project managers, but alternating genders or using terms like "he/she" interrupted the flow of the book. Therefore, we hope that our readers will forgive us for taking this shortcut. This book is on a serious subject and is written in a serious tone—most of the time. However, to keep our readers entertained, we chose to interject some wit, occasionally purposefully avoiding political correctness. We hope that our readers will not be offended. Whether you plan to read this book cover to cover or use it only for reference, we suggest you start with Chapter 1, "Introduction," in order to understand our mindset and our terminology. All of the topics presented in this book culminate in Chapter 12, "Project Planning," which brings together all the chapters into one completed picture for the project manager.

Overview of Chapters

Chapter 1, "Introduction to Data Warehousing," gives an overview of the data warehouse world. It compares traditional decision support to data warehousing and lists the differences between these two environments. This chapter also addresses the difficulties of managing these projects and explains the views and positions of the authors on this subject.

Chapter 2, "Goals and Objectives," has an in-depth discussion about the deficiencies of traditional decision support systems and addresses the short-term goals as well as the long-terms goals of data warehousing.

Chapter 3, "Indicators of Success," discusses the measures of success, describing the determinants by which a project has succeeded or failed. It also talks about critical success factors, which are the project characteristics that are necessary for the project to be successful, and how to measure results.

Chapter 4, "Risks," presents the types of failures that various data warehouse projects have experienced. It lists the inherent risks with all of their attendant horrors and then suggests techniques to deal with each of them.

Chapter 5, "Satisfying the User," emphasizes the importance of understanding the business and then examines all areas that either affect or are affected by the users, from gathering the requirements from them to communicating with them.

Chapter 6, "Cost Benefit," discusses the need for cost-justifying each data warehouse project. It deals with the typical costs and with the expected benefits and provides a template for you to develop the cost justification for your own project.

Chapter 7, "Selecting Software," presents categories of data warehouse tools, suggests how the tools fit in an organization's architecture, discusses the process of determining product requirements, and deals with weeding out the vendors you want to avoid.

Chapter 8, "Organization and Cultural Issues," examines the roles and responsibilities of team members on a data warehouse project. It explains the structure of data warehouse teams and discusses staffing issues, such as recruitment and retention, training and mentoring.

Chapter 9, "Methodology," explains why the traditional waterfall methodology is not applicable to data warehousing. It also describ

Author Biography

Sid Adelman is founder of Sid Adelman and Associates, an organization specializing in planning and implementing data warehouses. He consults exclusively on data warehouse topics including assessments, determining requirements, project planning, establishing roles and responsibilities, metadata strategy, and organizational and cultural issues. Sid presents regularly at data warehouse conferences and conducts a Data Warehouse Project Management seminar. Sid is a founding member of the Business Intelligence Alliance. He jointly developed a methodology, MapXpert for Data Warehouse, which provides a master plan for implementing data warehouses. Larissa Moss is founder and president of Method Focus Inc., a consulting firm specializing in improving business information quality. She frequently lectures and speaks at conferences on various data management topics, such as data warehousing, data quality, data modeling, and data audit and control. Her articles on data warehousing, project management, data management, and data quality are regularly published in magazines such as DM Review. She also provides consulting services in data warehouse assessments, data warehouse methodologies, project management, data administration, data modeling, data quality assessment, data transformation and cleansing, and metadata capture and utilization.

Table of Contents

List of Figures
xxi
Foreword xxiii
Preface xxvii
Acknowledgments xxxiii
Introduction to Data Warehousing
1(26)
Traditional Development
2(3)
Swim Lane Development Approach
2(1)
Stovepipe Systems
3(2)
Data Warehousing
5(6)
Cross-organizational Development Approach
6(2)
Integrated Data Warehouse Databases
8(3)
The Role of Project Management
11(2)
Traditional Project Management Techniques
11(1)
Data Warehouse Project Management Techniques
12(1)
Difficulty of Managing Data Warehouse Projects
13(3)
Scoping
14(1)
Estimating
14(1)
Staffing
14(1)
Shortage/Lack of Skills
15(1)
Dirty Data
15(1)
Control
15(1)
Summary
16(2)
Workshop
18(9)
Readiness Test
18(9)
Goals and Objectives
27(30)
Traditional Decision Support Deficiencies
28(6)
Departmental Views of Data
29(1)
Data Is Not Understood
29(1)
Users Disagree on Data Definitions
30(1)
Redundancy
31(1)
Reports Are Inconsistent
31(1)
Users Do Not Trust the Reports
31(1)
Data Is ``Dirty''
32(1)
Data Is Not Shared or Is Shared Reluctantly
32(1)
Data Is Not Integrated
33(1)
Historical Data Is Not Available
33(1)
Data Management Solutions
34(3)
Information Engineering
34(1)
Data Administration
35(1)
Data Warehouse Administration
36(1)
Data Warehouse Short-term Objectives
37(10)
Improve Quality of Data
38(3)
Minimize Inconsistent Reports
41(1)
Capture and Provide Access to Metadata
42(2)
Provide Capability for Data Sharing
44(1)
Integrate Data from Multiple Sources
45(1)
Merge Historical Data with Current Data
46(1)
Data Warehouse Long-term Objectives
47(3)
Reconcile Different Views of the Same Data
47(1)
Provide a Consolidated Picture of Enterprise Data
48(1)
Create a Virtual ``One-Stop-Shopping'' Data Environment
48(2)
Summary
50(2)
Workshop
52(5)
Traditional DSS Deficiencies
52(1)
Data Warehouse Short-term Objectives
52(2)
Data Warehouse Long-term Objectives
54(1)
Matching the Company Strategic Goals
54(3)
Indicators of Success
57(20)
Measures of Success
58(4)
Return on Investment
59(1)
The Data Warehouse Is Used
59(1)
The Data Warehouse Is Useful
60(1)
Project Is Delivered on Time
60(1)
Project Is Delivered within Budget
60(1)
Improved User Satisfaction
61(1)
Additional Requests for Data Warehouse Functions and Data
61(1)
Business Performance-based Benchmarks
61(1)
Goals and Objectives Are Met
61(1)
Business Problems Are Solved
62(1)
Business Opportunity Is Realized
62(1)
The Data Warehouse Has Become an Agent of Change
62(1)
Critical Success Factors
62(4)
Expectations Communicated to the Users
63(1)
Ensured User Involvement
64(1)
The Project Has a Good Sponsor
64(1)
The Team Has the Right Skill Set
64(1)
The Schedule Is Realistic
65(1)
The Project Has Proper Control Procedures (Change Control)
65(1)
The Right Tools Have Been Chosen
65(1)
Common Data Definitions
65(1)
Well-defined Transformation Rules
66(1)
Properly Trained Users
66(1)
Measuring Results
66(3)
Functional Quality
67(1)
Data Quality
67(1)
Computer Performance
67(1)
Network Performance
68(1)
User Satisfaction
68(1)
Number of Queries
68(1)
What Data Is Accessed
68(1)
Project Agreement Satisfaction
68(1)
Benefits Achieved
69(1)
Balanced Scorecard
69(1)
Summary
69(2)
Workshop
71(6)
Measures of Success
71(1)
Critical Success Factors
71(2)
Measuring Success
73(4)
Risks
77(24)
Types of Failures
78(4)
The Project Is over Budget
78(1)
Slipped Schedule
78(1)
Functions and Capabilities Not Implemented
79(1)
Unhappy Users
79(1)
Unacceptable Performance
79(1)
Poor Availability
80(1)
Inability to Expand
80(1)
Poor-quality Data/Reports
80(1)
Too Complicated for Users
81(1)
Project Not Cost Justified
81(1)
Management Does Not Recognize the Benefits
81(1)
Types of Risks
82(11)
No Mission or Objectives
82(1)
Data Warehouse Mission and Objectives Do Not Map to Those of the Enterprise
83(1)
Quality of the Source Data Not Known
83(1)
Skills Are Not in Place
84(1)
Inadequate Budget
85(1)
Lack of Supporting Software
86(1)
Source Data Not Understood
86(1)
Weak Sponsor
87(1)
Users Not Computer Literate
87(1)
Political Problems, Turf War
88(1)
Unrealistic User Expectations
88(1)
Architectural and Design Risks
89(1)
Scope Creep and Changing Requirements
89(1)
Vendors Out of Control
90(1)
Multiple Platforms
91(1)
Key People May Leave the Project
91(1)
Loss of the Sponsor
92(1)
Too Much New Technology
92(1)
Having to Fix an Operational System
92(1)
Geographically Distributed Environment
93(1)
Team Geography, Language, and Culture
93(1)
Poison People
93(3)
Summary
96(2)
Workshop
98(3)
Identify Your Risks
98(1)
Mitigate the Risks
98(3)
Satisfying the User
101(16)
Understanding the Business
101(2)
Terminology
102(1)
Learn What Drives the Business
102(1)
Educate Yourself
102(1)
Educate the Team
103(1)
Types of Users
103(1)
Technophobe
103(1)
Tech Wienie
103(1)
Hands Off
104(1)
Control Freak
104(1)
Communicating with the Users
104(3)
Listen
104(1)
Status Reports
105(1)
Meetings
105(1)
Validation and Concurrence
106(1)
Informal Communication
107(1)
Lining Up Support
107(1)
User as a Co-Project Manager
107(1)
Requirements
107(4)
User Interviews
107(1)
Joint Application Development (JAD)
108(1)
Documenting the Requirements
109(1)
Validating the Requirements
109(1)
Project Agreement
109(2)
Internal Selling
111(2)
The Necessity to Sell
111(1)
Evaluating Results and Benefits of the Data Warehouse
111(1)
Modes of Presentation and Communication
112(1)
Summary
113(1)
Workshop
114(3)
Newsletter
114(1)
Presentations/Demonstrations
114(2)
Briefings and Networking
116(1)
Cost Benefit
117(24)
The Need for Cost Justification
118(1)
Costs
118(7)
Controlling Costs
118(1)
Additional Support
119(1)
Consultants and Contractors
119(1)
Products
120(1)
Existing Tools
121(1)
Capacity Planning
122(1)
Hardware Costs
122(1)
Raw Data Multiplier
123(1)
Existing Hardware
123(1)
Controlling Hardware Costs
123(1)
Internal People Costs
124(1)
User Training
124(1)
IT Training
124(1)
Ongoing Costs
125(1)
Total Cost of Ownership
125(1)
Benefits
125(9)
Tangible Benefits
126(3)
Intangible Benefits
129(5)
Postproject Review
134(1)
Summary
134(2)
Workshop
136(5)
Costs
136(2)
Tangible Benefits
138(1)
Intangible Benefits
138(3)
Selecting Software
141(22)
Data Warehouse Tools
142(5)
Main Product Categories
142(4)
Which Categories to Choose
146(1)
Where the Tools Fit in the Technical Architecture
147(1)
Product Requirements
147(1)
Vendor Evaluation
148(2)
Financial Stability
148(1)
Vendor Support
149(1)
Vendor Reputation
150(1)
Getting the Most from the Vendor
150(1)
Research
150(5)
What to Read
150(1)
Consultants
151(1)
Client References
152(1)
Conferences and Seminars
152(1)
Vendor Presentations and Demonstrations
153(1)
The Short List
154(1)
Install and Compare---the Bake-off
154(1)
Making the Decision
155(1)
Summary
156(2)
Workshop
158(5)
Identify Categories of Tools Needed
158(2)
Criteria for Evaluation
160(1)
Weighting Factors and Scoring
161(2)
Organization and Cultural Issues
163(30)
Current Situation
164(1)
Cultural Imperatives
165(2)
Organization to Support the Data Warehouse
167(1)
Data Warehouse Roles
167(14)
Executive Sponsor
168(1)
User Liaison and User
168(3)
Business Analyst
171(1)
User Support
171(2)
Data Administrator
173(1)
Application Developer
174(1)
Security Officer and Auditor
175(1)
Database Administrator
176(1)
Technical Services
177(1)
Data Warehouse Architect
177(1)
Data Warehouse Project Manager
178(1)
Data Quality Analyst
179(1)
Query Tool Administrator
179(1)
Web Administrator
180(1)
Consultants and Contractors
181(1)
Advisory Boards
181(1)
Technical Advisory Board
181(1)
Business Advisory Board
181(1)
Recruiting and Retention
182(1)
The Data Warehouse Team
183(2)
Training
185(3)
Just-in-Time Training
185(1)
The Users' Data
186(1)
Mentors
186(1)
Monitoring Effectiveness of the Class
186(1)
Curriculum
187(1)
Vendor Training
188(1)
Summary
188(2)
Workshop
190(3)
Your Organization
190(3)
Methodology
193(36)
Data Warehouse Iterations
194(9)
Reasons for Big Bang Failures
194(1)
Considerations for First Data Warehouse Project
195(5)
Guidelines for First Data Warehouse Project
200(3)
Prototyping as a Development Approach
203(2)
What Is RAD?
204(1)
Prototyping Success Spiral vs. Death Spiral
204(1)
Parallel Development Tracks
205(2)
Back-end ETL Track
205(1)
Front-end Data Delivery Track
206(1)
Repository Navigation Track
206(1)
Technical Infrastructure Track
207(1)
Major Development Steps
207(17)
Project Agreement Step
208(7)
Establish Technology Platform Step
215(2)
Database and Extract/Transform/Load Development Step
217(2)
Query and Reporting Development Step
219(3)
Implementation Step
222(1)
Postimplementation Review Step
222(2)
Summary
224(2)
Workshop
226(3)
Nontechnical Infrastructure
226(1)
Project Agreement Document
227(1)
Activities and Deliverables
227(2)
Data Models
229(26)
Logical Data Model
230(9)
Purpose of a Logical Data Model
231(1)
Data Integration
232(1)
Understanding the Business
233(1)
Data Analysis
233(1)
The Need for Data Analysis
234(1)
Controlling Modeling Scope Creep
235(1)
Concepts of a Logical Data Model
236(2)
How to Use a Logical Data Model in Database Design
238(1)
Physical Data Model
239(10)
Purpose of a Physical Data Model
240(2)
Concepts of a Physical Data Model
242(2)
Denormalization
244(1)
Two-dimensional Design Schema
245(1)
Multidimensional Design Schema
246(3)
Summary
249(2)
Workshop
251(4)
High-level Logical Data Model
251(1)
Two-dimensional Physical Data Model
251(1)
Multidimensional Physical Data Model
252(3)
Data Quality
255(32)
Data Management and Data Delivery
256(2)
Data Management
256(2)
Data Delivery
258(1)
The Cost of Data Chaos
258(3)
The Need for Pagers
259(1)
Reconciliation of Files and Reports
259(1)
Bad Business Decisions and Lost Opportunities
260(1)
Bad Public Relations
260(1)
Defining Data Quality for the Data Warehouse
261(2)
Data Is Correct
261(1)
Data Is Accurate
261(1)
Data Follows the Business Rules
262(1)
Data Is Consistent
262(1)
Data Is Complete
262(1)
Data Is Integrated
263(1)
Data Cleansing Categories
263(11)
Dummy Default Values
263(2)
Missing Values
265(1)
Multipurpose Fields
266(1)
Cryptic Data
267(1)
Contradicting Data Values
268(1)
Violation of Business Rules
269(1)
Inappropriate Use of Address Lines
270(1)
Reused Primary Keys
270(2)
No Unique Identifiers
272(1)
Data Integration Problems
272(2)
Triaging Data Cleansing Activities
274(3)
To Cleanse or Not to Cleanse
274(1)
Steps for Data Cleansing
275(2)
Summary
277(2)
Workshop
279(8)
Inventory Your Source Data
279(1)
Determine Source Data Quality
280(1)
Choose the Best Source Files
281(1)
Cost for Nonquality Data
281(1)
Data Cleansing Categories
282(1)
Data Cleansing Costs
283(1)
Triage the Data Cleansing Activities
283(4)
Project Planning
287(28)
Need for Project Planning
288(1)
The Project Plan
289(9)
Work Breakdown Structure
290(1)
Tasks
290(1)
Milestones
291(1)
Deliverables
292(1)
Schedules
292(2)
Resources
294(2)
Developing the Project Plan
296(1)
Maintaining the Project Plan
297(1)
Estimating
298(4)
Experience
298(1)
Dependencies
299(1)
Industry Standards
300(1)
In the Best of All Possible Worlds
300(1)
Hours and Elapsed Time
301(1)
Rework
301(1)
Controlling the Project
302(3)
When Things Go Wrong
302(1)
Change Control
302(1)
Managing Risk
303(2)
Project Management Tools and Methodology
305(1)
First Project Selection
305(3)
Sponsor
305(1)
Value
306(1)
High Profile
306(1)
Measurable
306(1)
Reasonable Size
307(1)
Noncontroversial
307(1)
Reasonable Time Expectation
307(1)
Not Too Complex
307(1)
The Project Is Already in the Ditch
308(1)
Communication
308(2)
Team Meetings
308(1)
Management Presentations
309(1)
Newsletters
309(1)
Honesty
309(1)
Summary
310(2)
Workshop
312(3)
Rate Your Project's Probability for Success
312(2)
Create a Communication Plan
314(1)
Appendix 315
4.A Data Warehouse Applications by Industry
315
5.A User Responsibility Problem
338
5.B User Validation Template
338
5.C Words to Use/Words Not to Use
339
5.D Sample Letter to Interviewees
340
5.E Interview Results Template
341
5.F User Satisfaction Survey
342
5.G User Scorecard
345
6.A Benefits Analysis for Health Care
346
6.B Benefits Analysis for Finance
354
7.A Desired Types of References
359
7.B Questions for the References
360
7.C Vendor Rules of Engagement
362
7.D Plan to Select Products
363
7.E Data Warehouse Product Categories
364
8.A Organizational Structures
366
8.B Salary Survey
368
9.A Service Level Agreement Standards
370
11.A Questions for External Data Vendors
371
12.A Project Plan Task Template
372
12.B Sample Project Plan
375
12.C Disaster Examples
385

Supplemental Materials

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The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

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Excerpts

You have been a project manager for years and have successfully implemented many systems, but on your data warehouse project nothing seemed to work. All those proven techniques you''ve acquired over the years did not smooth the path. The methodology you so faithfully followed for years did not seem to help you as much in controlling the activities on the project. Tasks had to be repeated many times, and some new tasks that you had never considered before had to be performed. Roles and responsibilities assigned to your staff seemed inadequate and sometimes inappropriate. Your users had not planned on spending so much time on your project, and you had not realized what was going to be required of them. You knew your source files had some bad data, but you had not anticipated the impact it would have on the extract/transform/load (ETL) process. Maybe you are just planning your first data warehouse project and you have heard that it will be different and difficult. In either case, whether you already managed a data warehouse project or you are planning your first data warehouse project, this book will help you pave the road for a successful implementation. But before you immerse yourself into the content of this book, we would like to explain how we organized the book and provide a roadmap to guide you. Purpose of This Book The hardest aspect to data warehousing is to manage a highly dynamic project. Data warehouse projects are dynamic because the requirements are usually not as well defined as they are for an operational system, and the process of building a data warehouse often leads to adjustments of these requirements or to discovery of new ones. Furthermore, these projects are staffed with talented but often inexperienced personnel. The complexity and learning curve on the new technology components are often underestimated. Management on both the IT and the business side all too often do not understand the complexity of a data warehouse project and put unreasonable demands on the team and the project manager. In other words, these projects are extremely challenging to manage. The purpose of our book is to address the typical challenges on a data warehouse project and to educate the project manager on how to recognize the roadblocks and pitfalls. We give examples of risks and failures where we''ve encountered them, and we offer suggestions for avoiding them, or at least for mitigating them. At the end of every chapter is a section titled "A Cautionary Tale" that briefly describes our own experiences. Each chapter concludes with a workshop to practice what you have learned. Who Should Read This Book If this is your first or second attempt at a data warehouse project and you are not familiar or accustomed to using a different approach to managing this type of project, this book will help you. If you have already managed a data warehouse project that has been less than successful and you would like to do better on your next project, this book will provide some explanations for the difficulties you"ve encountered as well as suggestions for avoiding or mitigating these difficulties. This book is not meant to be a tutorial for basic project management. Instead, it is meant to be a guide for the experienced project manager who needs to know about the differences between a data warehouse project and a traditional project and who can use a helping hand from someone who has already been there. How This Book Is Organized Our approach to this book was to write each chapter in such a way that it could stand on its own because we recognized that some project managers will want to use it only as a reference. In order to accomplish this, it was unavoidable to include some overlapping material in various chapters. However, we present the overlapping material within the context of its chapter and hope that it will not affect the reading pleasure of those who wish to read this book cover to cover. Every chapter begins with a short list of its topics, followed by our experience from the field, highlighting landmines to watch out for, and concludes with a summary and a set of workshops. Some chapters also have appendices, which may be templates or worksheets, or additional guidelines. The workshops as well as the templates and appendices are stored in electronic format on the CD to make it easier for you to reproduce them. We encourage you to make use of these templates. They will help you standardize the process within your organization and simplify your own job. We made every effort to write this book in gender-neutral format. At times, however, when we did have to use a gender, we chose the masculine "he." We most certainly realize that there are many women project managers, but alternating genders or using terms like "he/she" interrupted the flow of the book. Therefore, we hope that our readers will forgive us for taking this shortcut. This book is on a serious subject and is written in a serious tone--most of the time. However, to keep our readers entertained, we chose to interject some wit, occasionally purposefully avoiding political correctness. We hope that our readers will not be offended. Whether you plan to read this book cover to cover or use it only for reference, we suggest you start with Chapter 1, "Introduction," in order to understand our mindset and our terminology. All of the topics presented in this book culminate in Chapter 12, "Project Planning," which brings together all the chapters into one completed picture for the project manager. Overview of Chapters Chapter 1, "Introduction to Data Warehousing," gives an overview of the data warehouse world. It compares traditional decision support to data warehousing and lists the differences between these two environments. This chapter also addresses the difficulties of managing these projects and explains the views and positions of the authors on this subject. Chapter 2, "Goals and Objectives," has an in-depth discussion about the deficiencies of traditional decision support systems and addresses the short-term goals as well as the long-terms goals of data warehousing. Chapter 3, "Indicators of Success," discusses the measures of success, describing the determinants by which a project has succeeded or failed. It also talks about critical success factors, which are the project characteristics that are necessary for the project to be successful, and how to measure results. Chapter 4, "Risks," presents the types of failures that various data warehouse projects have experienced. It lists the inherent risks with all of their attendant horrors and then suggests techniques to deal with each of them. Chapter 5, "Satisfying the User," emphasizes the importance of understanding the business and then examines all areas that either affect or are affected by the users, from gathering the requirements from them to communicating with them. Chapter 6, "Cost Benefit," discusses the need for cost-justifying each data warehouse project. It deals with the typical costs and with the expected benefits and provides a template for you to develop the cost justification for your own project. Chapter 7, "Selecting Software," presents categories of data warehouse tools, suggests how the tools fit in an organization''s architecture, discusses the process of determining product requirements, and deals with weeding out the vendors you want to avoid. Chapter 8, "Organization and Cultural Issues," examines the roles and responsibilities of team members on a data warehouse project. It explains the structure of data warehouse teams and discusses staffing issues, such as recruitment and retention, training and mentoring. Chapter 9, "Methodology," explains why the traditional waterfall methodology is not applicable to data warehousing. It also describes the various parallel development tracks, such as

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