Synopses & Reviews
"Anyone who finds Data Warehouse Project Management has found for themselves a veritable gold mine, a wealth of wisdom and experience from some real pros . . . it is the most thorough and thoughtful work on data warehouse projects I have ever read."
--From the Foreword by John A. Zachman
Data warehouse development projects present a unique set of management challenges that can confound even the most experienced project manager. Data Warehouse Project Management addresses these challenges and provides a comprehensive roadmap to managing every aspect of data warehouse design, development, and implementation. Drawing on their extensive experience in the field, Sid Adelman and Larissa Moss point to critical success factors, reveal the many pitfalls to watch out for, and offer proven solutions that will enable you to put a successful data warehouse project into place.
You will find in-depth coverage of such topics as:
- Critical success factors and reasons for failures
- Measuring results
- Cost-benefit analysis
- Selecting the right software and vendors
- Roles and responsibilities of team members
- Methodology, including rapid application development and parallel development tracks
- Developing a logical and physical data model for smooth data integration
- The important issue of data quality and how to cleanse dirty data from source files
At the end of each chapter, "A Cautionary Tale" warns you of potential problems, and a workshop enables you to practice what you've just learned. The book concludes with a comprehensive example that illustrates project planning and management in action, from determining milestones, schedules, and tasks to maintaining control when the project goes off course. An accompanying CD-ROM contains the workshops in electronic format as well as helpful templates and additional reading material.
According to Earl Hadden & Associates, 85% of data warehouse (DW) projects fail to meet objectives, while 40% fail completely. Effective project management can reduce the possibility of failure. The average DW takes about 3 years to build, and costs $3-5 million. A project of this magnitude requires a project manager, analogous to the software project manager. Since many of the activities and challenges of data warehousing are new, even experienced project managers need guidance and a means of tapping into best and avoiding worst practices. And while there are many books available on different aspects of data warehousing, none target the project manager the way this book does. This book will contain templates, examples, and checklists that will be extremely useful to the project manager in proposing, staffing, designing, developing, and maintaining a data warehouse.
Provides a roadmap for the data warehouse project manager, offering tips and guidance from experts for effective management of a large and expensive project. Included in the book are templates, examples, checklists and real-world case studies to make management easier.
Includes bibliographical references (p. 387-388) and index.
About the Author
is founder of Sid Adelman & Associates, an organization specializing in planning and implementing data warehouses. He presents regularly at data warehouse conferences and conducts a Data Warehouse Project Management seminar. Sid is also a founding member of the BIAlliance. He jointly developed a methodology that provides a master plan for implementing data warehouses. He wrote Data Warehouse Project Management
(Addison-Wesley, 2000) with Larissa Moss.
Larissa Moss is founder and president of Method Focus, Inc., a consulting firm specializing in business intelligence and data warehousing. She is a frequent lecturer and speaker at conferences in the United States, Europe, and Asia on data warehousing, project management, development methodologies, and organizational and cultural issues. Her articles on these topics are regularly published in magazines such as DM Review and Journal of Data Warehousing. She is coauthor of Data Warehouse Project Management (Addison-Wesley, 2000) and Impossible Data Warehouse Situations (Addison-Wesley, 2003). She is a senior consultant at the Cutter Consortium and one of the authors of their Business Intelligence Executive Reports.
Table of Contents
List of Figures.
1. Introduction to Data Warehousing.
The Role of Project Management.
Difficulty of Managing Data Warehouse Projects.
2. Goals and Objectives.
Traditional Decision Support Deficiencies.
Data Management Solutions.
Data Warehouse Short-Term Objectives.
Data Warehouse Long-Term Objectives.
3. Indicators of Success.
Measures of Success.
Critical Success Factors.
Types of Failures.
Types of Risks.
5. Satisfying the User.
Understanding the Business.
Types of Users.
Communicating with the Users.
6. Cost Benefit.
The Need for Cost Justification.
7. Selecting Software.
Data Warehouse Tools.
Where the Tools Fit in the Technical Architecture.
Making the Decision.
8. Organization and Cultural Issues.
Organization to Support the Data Warehouse.
Data Warehouse Roles.
Recruiting and Retention.
The Data Warehouse Team.
Data Warehouse Iterations.
Prototyping as a Development Approach.
Parallel Development Tracks.
Major Development Steps.
10. Data Models.
Logical Data Model.
Physical Data Model.
11. Data Quality.
Data Management and Data Delivery.
The Cost of Data Chaos.
Defining Data Quality for the Data Warehouse.
Data Cleansing Categories.
Triaging Data Cleansing Activities.
12. Project Planning.
Need for Project Planning.
The Project Plan.
Controlling the Project.
First Project Selection.
Data Warehouse Applications by Industry.
User Responsibility Problem.
User Validation Template.
Words to Use/Words Not to Use.
Sample Letter to Interviewees.
Interview Results Template.
User Satisfaction Survey.
Benefits Analysis for Health Care.
Benefits Analysis for Finance.
Desired Types of References.
Questions for the References.
Vendor Rules of Engagement.
Plan to Select Products.
Data Warehouse Product Categories.
Service Level Agreement Standards.
Questions for External Data Vendors.
Project Plan Task Template.
Sample Project Plan.