A functional data warehouse is vital to an organization's success, but building and maintaining any data warehouse is fraught with managerial and technical pitfalls. Impossible Data Warehouse Situations introduces possible solutions to ninety-one common crises that confront companies of all types, sizes, and structures. Nine leading data warehousing experts provide the thoughtful and detailed guidance corporate executives, IT managers and staff, and end-users need to prevent and survive seemingly impossible situations.
This book serves as a quick reference for resolving specific data warehouse problems and as a practical introduction to the realities of data warehousing not covered in basic texts. Part I addresses management issues, including weak organizational support, unrealistic schedules, and personnel problems. Part II focuses on technical challenges, such as security, integration, and performance. In both sections, Sid Adelman and his coauthors provide multiple perspectives and solutions illustrated by practical examples. Also included are recommendations for further reading and glossaries covering technical terminology and acronyms as well as colloquial English.
Drawing upon their combined 142 years of experience with data warehouse implementations, Sid Adelman, Joyce Bischoff, Jill Dyche, Douglas Hackney, Sean Ivoghli, Chuck Kelley, David Marco, Larissa Moss, and Clay Rehm offer invaluable advice on:
Readers gain access to data warehousing's top minds and learn how to thrive in seemingly impossible situations.
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Sample Chapter 1
(NOTE: Each chapter begins with an Overview.)
I IMPOSSIBLE MANAGEMENT SITUATIONS.1. Management Issues.
The Data Warehouse Has a Record of Failure.
IT Is Unresponsive.
Management Constantly Changes.
IT Is the Assassin.
The Pilot Must Be Perfect.
User Departments Don't Want to Share Data.
Senior Management Doesn't Know What the Data Warehouse Team Does.2. Changing Requirements and Objectives.
The Operational System Is Changing.
The Source System Constantly Changes.
The Data Warehouse Vision Has Become Blurred.
The Objectives Are Misunderstood.
The Prototype Becomes Production.
Management Doesn't Recognize the Success of the Data Warehouse Project.3. Justification and Budget.
User Productivity Justification Is Not Allowed.
How Can the Company Identify Infrastructure Benefits?
Does a Retailer Need a Data Warehouse?
How Can Costs Be Allocated Fairly?
Historical Data Must Be Justified.
No Money Exists for a Prototype.4. Organization and Staffing.
To Whom Should the Data Warehouse Team Report?
The Organization Uses Matrix Management.
The Project Has No Consistent Business Sponsor.
Should a Line of Business Build Its Own Data Mart?
The Project Has No Dedicated Staff.
The Project Manager Has Baggage.
No One Wants to Work for the Company.
The Organization Is Not Ready for a Data Warehouse.5. User Issues.
The Users Want It Now.
The Business Does Not Support the Project.
Web-Based Implementation Doesn't Impress the Users.
Management Rejects Multidimensional Tools as Being Too Complex.
The Users Have High Data Quality Expectations.
The Users Don't Know What They Want.6. Team Issues.
A Heat-Seeking Employee Threatens the Project.
Management Assigned Dysfunctional Team Members to the Data Warehouse Project.
Management Requires Team Consensus.
Prima Donnas on the Team Create Dissension.
Team Members Aren't Honest about Progress on Assignments.
A Consultant Offers to Come to the Rescue.
The Consultants Are Running the Show.
The Contractors Have Fled.
Knowledge Transfer Is Not Happening.
How Can Data Warehouse Managers Best Use Consultants?
Management Wants to Outsource the Data Warehouse Activities.7. Project Planning and Scheduling.
Management Requires Substantiation of Estimates.
IT Management Sets Unrealistic Deadlines.
The Sponsor Changes the Scope But Doesn't Want to Change the Schedule.
The Users Want the First Data Warehouse Delivery to Include Everything.
The Project Manager Severely Underestimates the Schedule.
II. IMPOSSIBLE TECHNICAL SITUATIONS.8. Data Warehouse Standards.
The Organization Has No Experience with Methodologies.
Database Administration Standards Are Inappropriate for the Data Warehouse.
The Employees Misuse Data Warehouse Terminology.
It's All Data Mining.
A Multinational Company Needs to Build a Business Intelligence Environment.9. Tools and Vendors.
What Are the Best Practices for Writing a Request for Proposals?
The Users Don't Like the Query and Reporting Tool.
OO Is the Answer (But What's the Question?).
IT Has Already Chosen the Tool.
Will the Tools Perform Well?
The Vendor Has Undue Influence.
The Rejected Vendor Doesn't Understand "No".
The Vendor's Acquiring Company Provides Poor Support.10. Ten Security.
The Data Warehouse Has No Security Plan.
Responsibility for Security Must Be Established.
Where Should a New Security Administrator Start?11. Eleven Data Quality.
How Should Sampling Be Applied to Data Quality?
Redundant Data Needs to Be Eliminated.
Management Underestimated the Amount of Dirty Data.
Management Doesn't Recognize the Value of Data Quality.
The Data Warehouse Architect Is Obsessed with Data Quality.
The ETL Process Partially Fails.
Source Data Errors Cause Massive Updates.12. Integration.
Multiple Source Systems Require Major Data Integration.
The Enterprise Model Is Delaying Progress.
Should a Company Decentralize?
The Business Sponsor Wants Real-Time Customer Updates.
The Company Doesn't Want Stovepipe Systems.
Reports from the Data Warehouse and Operational Systems Don't Match.
Should the Data Warehouse Team Fix an Inadequate Operational System?13. Data Warehouse Architecture.
The Data Warehouse Architecture Is Inadequate.
Stovepipes Are Impeding Integration.
Should Backdated Transactions Change Values in the Data Warehouse?
A Click-Stream Data Warehouse Will Be Huge.
Time-Variant Analysis Requires Special Designs.
Management Wants to Develop a Data Warehouse Simultaneously with a New Operational System.
The Data Warehouse Gets Assigned the Role of a Reporting System.
Meta Data Needs to Be Integrated Across Multiple Products.
How Can UPC Code Changes Be Reconciled?14. Performance.
The Software Does Not Perform Properly.
The Data Warehouse Grows Faster Than the Source Data.
Loading the Fact Table Takes Too Long.Appendix A: Data Warehouse Glossary.
In the seminars, presentations and classes we teach on data warehouse, we are often subjected to what appear to be “Impossible Situations.” Likewise, on the DMReview “Ask the Experts” forum (reference www.dmreview.com) we are confronted with questions that, at first glance, appear to have no answers or solutions. They do have solutions and that’s what this book is all about.
The 91 Impossible Situations in this book were taken from our classes, from the DMReview “Ask the Expert” forum, from data warehouse consultants and colleagues in the field who have experienced these situations. These are all real situations but they have been disguised and sanitized to protect the authors as well as to protect the organizations experiencing the situations from the attendant shame and humiliation. As a side note, as the situations were disseminated, reviewers were quick to say, “I know what company this describes,” and they were almost always wrong.Purpose of This Book
There is no reason that each organization, as it begins and continues to develop data warehouse projects, must wrestle with many of the very difficult situations that have confounded other organizations. The same impossible situations continue to raise their ugly heads, often with surprisingly little relation to the industry, the size of the organization, or the makeup of the organizational structure. This book is, first of all, to let you know you are not alone and your problems are not unique. The book should give hope to the perplexed who see no obvious solution to their problems.
Some of the situations should resonate with those of you planning to enhance your data warehouse, to add new data, additional users, and new applications. It may be that the impossible situation has not yet emerged, but you definitely see it just around the bend. This means that you should be able to avoid the situation rather than having it develop and then needing to fix it.Who Should Read This Book
Every stakeholder, data warehouse architect, data warehouse project manager, and user liaison responsible for any portion of the data warehouse will be faced with the challenges identified in these pages. These people are looking for solutions to situations that, at first, appear to have no answer and appear to be impossible.
This is not an introduction to data warehouse. Readers should have some level of familiarity with data warehouse either through practical experience, conferences, or previous reading of data warehouse texts. This book is also not geared to any primary topic such as meta data or data quality but covers a broad range of areas. The reference section has both introductory as well as more advanced suggested reading material.
User liaisons and managers may only be interested in and want to read Section I, Impossible Managerial Situations.How This Book is Organized
The first part of the book deals with managerial situations and the second part with technical situations. The order within these sections is very roughly the order in which projects are developed and situations are encountered but the sections are not dependent on each other. The book can be read from front to back but more likely, the readers will be drawn to the sections that cause them the most pain. For example, if the reader is struggling with data quality issues, Section 11, Data Quality, would be the place to start. The order of the answers is alphabetical by the experts’ last name.
A technical glossary at the back of the book should clarify some terms and will also keep the reader from going down the wrong path. Terminology misunderstanding in this fast-changing field has caused significant misinterpretation that has resulted in wasted time and money, dissension and hurt feelings. The glossary contains acronyms and data warehouse and IT terminology. There will be a few cases with more than one definition. Please refer to the definitions to avoid any misunderstandings on your part. Those whose native language is not English will find the Colloquial Glossary useful since the experts were fond of using colloquial expressions.
The reader will notice strong biases in the experts’ responses. The experts came to these dearly held opinions honestly through extensive experience in real world situations. The reader will also quickly realize that some of the answers are in sharp disagreement and appear to be contradictory while a few are embarrassingly similar. The editor made no attempt to reconcile the differences. Recognizing that there is usually more than one answer, very much depending on the organization and the situation, the different approaches have been maintained. Any reconciliation or choice of approach is left to the reader who, we assume, is astute enough to find the relevant answer and pick the solution that will work in his or her organization.The Experts
The experts represented in this book are all real experts in data warehouse. They have been working in the data warehouse arena for a cumulative 130 (+12) years. If anyone can address these impossible situations, they can.
The experts have suggested best practices based on their experiences with both successful and unsuccessful implementations. The experts correctly identified many of the situations as reflecting the symptoms of a dysfunctional organization, knowing that without understanding the real causes, no effective solution could be honestly recommended. The experts resorted to making assumptions about the situations when not enough information was provided.
The experts’ bios are in the Appendix.Follow-on
There were a number of impossible situations that came in after the original batch of 91 were sent to the experts and we all feel sure more will appear. If you have a situation you would like to contribute, please send it to email@example.com. If there is to be a second edition, your situation may be included.
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