Impossible Data Warehouse Situations

Sid Adelman 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. Joyce Bischoff, president of Bischoff Consulting, Inc., is an internationally recognized consultant, writer, and lecturer specializing in all aspects of data warehousing, database design, and design methodologies. She has been involved in planning, designing, implementing, and performing design reviews of data warehouses in more than 50 companies in the credit card, chemical, pharmaceutical, insurance, financial, oil refining, publishing, and hospital industries. She is the lead author of the book Data Warehouse: Practical Advice from the Experts 1997, which brings together opinions from 20 contributing authors, and a member of the expert panel for the monthly column "Ask the Experts" at http://www.dmreview.com. She is the author of numerous articles and frequently presents at data warehousing conferences all over the world. She may be reached at JoyceBischoff@cs.com. Jill Dyché is a partner with Baseline Consulting Group, a specialty consulting firm focusing on the delivery of business intelligence solutions across industries. Since 1985 she has been working with Fortune 1000 companies worldwide to help align strategic technology initiatives with corporate business objectives. Jill is a frequent speaker at technology and marketing conferences, and her articles have been featured in a variety of publications: Information Week, Oracle magazine, Teradata Review, Telephony Magazine, The Washington Times, and The Chicago Tribune. Douglas Hackney is President of Enterprise Group, Ltd. and is a monthly columnist for Data Management Review. He has contributed to Computer World and speaks regularly at industry events, including DCI's Data Warehousing Conference, Bill Inmon's Data Warehousing Conference, and the Data Warehouse Institute. Sean Ivoghli is the founder and president of Digital Symmetry, Inc., formerly the Data Warehouse Consulting Group, a consulting firm that specializes in providing end-to-end data warehousing, business intelligence, and data/application integration solutions. He has over 12 years of experience in full life-cycle data warehouse and information systems development, and he provides expert consulting services in data warehouse design, development, project management, and information management strategies. Mr. Ivoghli is the coauthor of Compass, a comprehensive data warehousing methodology that offers multiple tracks for developing scalable, flexible, and high-performance data warehousing and data mart solutions in a cost-effective manner. He can be reached at sivoghli@digitalsymmetryinc.com and at sivoghli@dwcg.com. Chuck Kelley is an internationally known expert in database technology. He has over 25 years of experience in designing and implementing operational/production systems and data warehouses. Mr. Kelley has worked in some facet of the implementation process of over 45 data warehouses. Mr. Kelley teaches seminars on SQL, Database Internals, Implementing the Data Warehouse, Designing and Implementing the Star Schema from Your Operational System, and other database and data warehousing topics. He has been a speaker at Database World, Client/Server World, UniForum, COMDEX, Rdb C

(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.