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: *Management issues* Changing objectives and requirements* Budget* Organization and staffing* User and team issues* Product planning and schedules* Data warehousing standards* Tools and vendors* Security* Data quality* Integration* Data warehousing architecture* PerformanceReaders gain access to data warehousing's top minds and learn how to thrive in seemingly impossible situations. 0201760339B09112002

Table of Contents:
(NOTE: Each chapter begins with an Overview.) Credits. 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. Appendix B: Colloquialism Glossary. Bibliography. Experts' Bios. Index. 0201760339T09112002.