Data Warehouse Project Management

Description

• Copyright 2000
• Dimensions: 7-3/8" x 9-1/4"
• Pages: 448
• Edition: 1st

• Book
• ISBN-10: 0-201-61635-1
• ISBN-13: 978-0-201-61635-4

The average data warehouse takes three years to build and costs $3-5 million -- yet many data warehouse project managers are thrown into the position with no clear idea of their roles, authority, or even objectives. It's no wonder that 85% of all data warehouse projects fall short of their objectives, and 40% fail completely. In Data Warehouse Project Management, two leading data warehouse project management consultants present start-to-finish best practices for getting the job done right. Sid Adelman and Larissa Terpeluk Moss cover the entire lifecycle, from proposing a data warehouse project through staffing a team, developing project scope, justifying, negotiating, and marketing the data warehouse project internally, and then implementing the data warehouse. They present real-world case studies identifying the key pitfalls that arise repeatedly in data warehouse projects -- and offer proven solutions for addressing these challenges. The book and CD-ROM contain an extensive library of templates and checklists, plus self-tests to determine whether an organization is really ready for data warehousing.

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Extras

Related Articles

Case Study of Building a Data Warehouse with Analysis Services (Part One)

Case Study of Building a Data Warehouse with Analysis Services (Part Two)

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Sample Content

Table of Contents

List of Figures.


Foreword.


Preface.


Acknowledgments.


1. Introduction to Data Warehousing.

Traditional Development.

Data Warehousing.

The Role of Project Management.

Difficulty of Managing Data Warehouse Projects.

Summary.

Workshop.

2. Goals and Objectives.

Traditional Decision Support Deficiencies.

Data Management Solutions.

Data Warehouse Short-Term Objectives.

Data Warehouse Long-Term Objectives.

Summary.

Workshop.

3. Indicators of Success.

Measures of Success.

Critical Success Factors.

Measuring Results.

Summary.

Workshop.

4. Risks.

Types of Failures.

Types of Risks.

Poison People.

Summary.

Workshop.

5. Satisfying the User.

Understanding the Business.

Types of Users.

Communicating with the Users.

Requirements.

Internal Selling.

Summary.

Workshop.

6. Cost Benefit.

The Need for Cost Justification.

Costs.

Benefits.

Summary.

Workshop.

7. Selecting Software.

Data Warehouse Tools.

Where the Tools Fit in the Technical Architecture.

Product Requirements.

Vendor Evaluation.

Research.

Making the Decision.

Summary.

Workshop.

8. Organization and Cultural Issues.

Current Situation.

Cultural Imperatives.

Organization to Support the Data Warehouse.

Data Warehouse Roles.

Advisory Boards.

Recruiting and Retention.

The Data Warehouse Team.

Training.

Summary.

Workshop.

9. Methodology.

Data Warehouse Iterations.

Prototyping as a Development Approach.

Parallel Development Tracks.

Major Development Steps.

Summary.

Workshop.

10. Data Models.

Logical Data Model.

Physical Data Model.

Summary.

Workshop.

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.

Summary.

Workshop.

12. Project Planning.

Need for Project Planning.

The Project Plan.

Estimating.

Controlling the Project.

First Project Selection.

Communication.

Summary.

Workshop.

Appendix.

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.

User Scorecard.

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.

Organizational Structures.

Salary Survey.

Service Level Agreement Standards.

Questions for External Data Vendors.

Project Plan Task Template.

Sample Project Plan.

Disaster Examples.

Index. 0201616351T04062001

Preface

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 the extract/transform/load (ETL) process, the data delivery process, and the metadata collection and navigation efforts.

Chapter 10, "Data Models," examines the analytical purpose and usefulness of a logical data model, the primary technique for data integration, as well as the database design purpose of a physical data model. It compares the traditional database design schema of a two-dimensional entity-relationshipnbased architecture to the popular multidimensional star schemanbased architecture.

Chapter 11, "Data Quality," defines what data quality means in a data warehouse and explains the cost of existing nonquality data. It also defines the various dirty data categories found on source files and suggests some triaging steps for data cleansing.

Chapter 12, "Project Planning," coalesces the previous chapters into one cohesive picture for managing a data warehouse project and guides the project manager through the development of a project plan. It presents some estimating guidelines and tips on how to control the project on an ongoing basis.

0201616351P04062001

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