e-Data
Turning Data Into Information With Data Warehousing
Published on 28. February 2000
Book
Paperback/Softback
368 pages
978-0-201-65780-7 (ISBN)
Description
Data warehouse adoption is increasing 30-40% per year, and data warehouse technology is evolving rapidly to serve new requirements. e-Data: Turning Data Into Information with Data Warehousing is the complete manager's briefing on what this technology can do today, and how to achieve optimal results. Start by understanding why data warehousing has generated so much excitement throughout the business community; review four types of decision support analyses commonly performed with data warehouses; understand how data warehouses support powerful new database marketing applications; and see data warehousing at work in a wide range of industries. Next, master data warehouse technologies, understand today's best practices for implementation and staffing, and learn the tough questions to ask vendors before you make product commitments. e-Data includes detailed metrics for evaluating the success of a data warehouse; techniques for identifying both "hard" and "soft" benefits; and "from the trenches" advice on what to do next, once your data warehouse is running successfully. For IT managers, business professionals, consultants, and others who need to understand the benefits of the latest data warehousing technology.
More details
Language
English
Place of publication
Boston
United States
Publishing group
Pearson Education (US)
Target group
College/higher education
Dimensions
Height: 185 mm
Width: 234 mm
Thickness: 18 mm
Weight
590 gr
ISBN-13
978-0-201-65780-7 (9780201657807)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition
Book
04/2000
Addison Wesley
€38.37
Article exhausted; check for reprint
Person
Jill Dyche 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.
0201657805AB04062001
0201657805AB04062001
Content
Foreword.
Acknowledgments.
About the Author.
Introduction.
The Book and Its Purpose.
You the Reader.
Content Overview.
Part I: Getting the Value.
Part II: Getting the Technology.
Part III: Getting Ready.
A Case Study Sneak Preview.
Requisite Caveats.
I. GETTING THE VALUE.
1. What Is a Data Warehouse Anyway?
The Data Warehouse Defined.
Data Warehousing, Decision Support, and Business Intelligence.
The Data-Warehousing Bandwagon and Why Everyone Jumped on It.
Data-Warehousing Objectives.
Some Trite Data-Warehousing Aphorisms.
Venus and Mars: How IT and Businesspeople Communicate.
Some Other Buzzwords and What They Mean.
Some Lingering Questions.
2. Decision Support from the Bottom Up.
The Evolution of Decision Support.
Standard Query: The Workhorse of DSS.
Multidimensional Analysis: The Power of Slice 'n' Dice.
Modeling and Segmentation: Analysis for Knowledge Workers.
Knowledge Discovery: The Power of the Unknown.
Some Real-Life Examples.
Standard Queries.
Multidimensional Analysis.
Modeling and Segmentation.
Knowledge Discovery.
Wherefore Data Mining?
Data Warehousing in the Real World.
What It Takes to Get to the Top.
3. Data Warehouses and Database Marketing.
Customer Relationship Management.
Customer Segmentation.
Individual Customer Analysis.
Case Study: Bank of America.
A Word about CRM Technology.
Popular Database-Marketing Initiatives and What They Mean.
Target Marketing.
Cross-Selling.
Sales Analysis and Forecasting.
Market Basket Analysis.
Promotions Analysis.
Customer Retention and Churn Analysis.
Profitability Analysis.
Customer Value Measurement.
Product Packaging.
Call Centers.
Sales Contract Analysis.
Database Marketing Lessons Learned.
Some Lingering Questions.
4. Data Warehousing by Industry.
Retail.
Uses of Data Warehousing in Retail.
Market Basket Analysis.
In-Store Product Placement.
Product Pricing.
Product Movement and the Supply Chain.
The Good News and Bad News in Retailing.
Case Study: Hallmark.
Financial Services.
Uses of Data Warehousing in Financial Services.
The Good News and Bad News in Financial Services.
Case Study: Royal Bank of Canada.
Telecommunications.
U.S. Local Service Carriers.
U.S. Long-Distance Carriers.
International Long-Distance Carriers.
Wireless Carriers.
Uses of Data Warehousing in Telecommunications.
The Good News and Bad News in Telecommunications.
Case Study: GTE.
Transportation.
Yield Management.
Frequent-Passenger Programs.
Travel Packaging and Pricing.
Fuel Management.
Customer Retention.
The Good News and Bad News in Transportation.
Case Study: Qantas.
Government.
The Good News and Bad News in Government.
Case Study: State of Michigan.
Health Care.
Uses of Data Warehousing in Health Care.
The Good News and Bad News in Health Care.
Case Study: Aetna U.S. Healthcare, U.S. Quality Algorithms.
Insurance.
Uses of Data Warehousing in Insurance.
The Good News and Bad News in the Insurance Industry.
Case Study: California State Automobile Association.
Entertainment.
Case Study: Twentieth Century Fox.
Some Lingering Questions.
II. GETTING THE TECHNOLOGY.
5. The Underlying Technologies: A Primer.
Data Warehouse Architecture.
The Operational Data Store.
Two-Tier Versus n-Tier.
Middleware.
Databases and What They're Good For.
Multidimensional Databases.
Metadata.
Disseminating the Information: Application Software.
Graphical User Interfaces.
A Word about the Web.
Development Definitions and Differentiators.
OLAP Subcategories.
Data Modeling and Design Tools.
Data Extraction and Loading Tools.
Management and Administration.
Putting It All Together.
Some Lingering Questions.
6. What Managers Should Know about Implementation.
What You Should Know about Data Warehouse Methodologies.
Evaluating a Methodology.
The Data Warehouse Implementation Process.
The Steps in Data Structure and Management.
The Steps in Application Development.
Who Should Be Doing What?
Development Job Roles and Responsibilities.
Consultants Versus Full-Time Staff.
The Lost Fine Art of Skill Delineation.
Good and Evil Square Off:A Tale of Two Project Plans.
Executive Involvement on the Project.
Profile: Hank Steermann of Sears, Roebuck and Co..
Some Lingering Questions.
7. Value or Vapor? Finding the Right Vendors.
The Hardware Vendors.
Five Questions to Ask Your Hardware Vendor.
The Database Vendors.
Five Questions to Ask Your Database Vendor.
TPC Benchmarks.
The Application Vendors.
Five Questions to Ask Your Application Tool Vendor.
Data-Mining Tools: A Breed Apart.
Ten Questions to Ask Your Data-Mining Vendor.
The Consultants.
The Big Guys.
The Little Guys.
A Word about the Analysts.
A Word about the Vendors.
Five Questions Your Consultant Should Ask You.
The RFP Process.
The Components of a Good RFP.
A Sample Table of Contents.
Some Lingering Questions.
III. GETTING READY.
8. Data Warehousing's Business Value Proposition.
Return on Investment.
Hard ROI: The Tangible Benefits.
Soft ROI: The Intangible Benefits.
Budgeting for the Data Warehouse.
Technology Costing.
Resource Costing.
Obtaining Funding - But Not Too Much!
Data Warehouse Operations Planning.
Developing an Operating Plan.
Are You Ready for a Data Warehouse? A Quiz.
Data Warehouse Readiness Score.
Some Lingering Questions.
9. The Perils and Pitfalls.
The New Top 10 Data-Warehousing Pitfalls.
Pitfall #1: The Data Warehouse as Panacea Syndrome.
Pitfall #2: They Talked to End-Users--But the Wrong Ones!
Pitfall #3: Too Much Time Spent on Research, Alienating Constituents.
Pitfall #4: Bogging a Good Project Down by Creating Metadata.
Pitfall #5: Being Sidetracked by "Neat to Know" Analysis.
Pitfall #6: Adopting Decision Support Without Supporting Decisions.
Pitfall #7: Greediness on the Part of Development Organizations.
Pitfall #8: Lack of "Internal PR".
Pitfall #9: Failing to Acknowledge That DSS Applications Are Finite.
Pitfall #10: Overemphasizing Development and Ignoring Deployment.
Thinking of Outsourcing?
Data Warehousing's Dirty Little Secrets.
The Politics of Data Warehousing.
The Top 10 Signs of Data Warehouse Sabotage.
The Vanguards of Data Warehousing.
Case Study: Charles Schwab & Co., Inc..
10. What to Do Now.
If You Need a Data Warehouse.
Establish Up-Front Success Metrics.
Consider Benchmarking.
Research External Staff.
Prepare Your Environment.
Classify Your Stakeholders.
Ramp Up Support Capabilities.
Profile: Philippe Klee, Qantas Airways.
Look Outside Your Box.
Solicit a Request for Information.
If You Already Have a Data Warehouse.
Establish a Formal Postmortem Process.
Inventory Existing Applications.
Spring for an Audit.
Improve Customer-Facing Business Processes.
Establish a Closed-Loop Process.
Go Web, Young Man!
Case Study: Allsport.
Consider Branching Out Vertically.
Consider Branching Out Horizontally.
If You Have a Data Mart or Marketing Analysis System.
Share Your Toys.
Migrate to Enterprisewide.
An Insider's Crystal Ball.
Clickstream Storage.
Enterprise Resource Planning.
Extending the Data Warehouse to External Vendors.
Customized Web Portals.
Real-Time E-Marketing.
Privacy.
The Whole Truth.
Appendix: Haven't Had Enough? Suggested Reading.
Business Books.
Technology Books.
Websites.
Index. 0201657805T04062001
Acknowledgments.
About the Author.
Introduction.
The Book and Its Purpose.
You the Reader.
Content Overview.
Part I: Getting the Value.
Part II: Getting the Technology.
Part III: Getting Ready.
A Case Study Sneak Preview.
Requisite Caveats.
I. GETTING THE VALUE.
1. What Is a Data Warehouse Anyway?
The Data Warehouse Defined.
Data Warehousing, Decision Support, and Business Intelligence.
The Data-Warehousing Bandwagon and Why Everyone Jumped on It.
Data-Warehousing Objectives.
Some Trite Data-Warehousing Aphorisms.
Venus and Mars: How IT and Businesspeople Communicate.
Some Other Buzzwords and What They Mean.
Some Lingering Questions.
2. Decision Support from the Bottom Up.
The Evolution of Decision Support.
Standard Query: The Workhorse of DSS.
Multidimensional Analysis: The Power of Slice 'n' Dice.
Modeling and Segmentation: Analysis for Knowledge Workers.
Knowledge Discovery: The Power of the Unknown.
Some Real-Life Examples.
Standard Queries.
Multidimensional Analysis.
Modeling and Segmentation.
Knowledge Discovery.
Wherefore Data Mining?
Data Warehousing in the Real World.
What It Takes to Get to the Top.
3. Data Warehouses and Database Marketing.
Customer Relationship Management.
Customer Segmentation.
Individual Customer Analysis.
Case Study: Bank of America.
A Word about CRM Technology.
Popular Database-Marketing Initiatives and What They Mean.
Target Marketing.
Cross-Selling.
Sales Analysis and Forecasting.
Market Basket Analysis.
Promotions Analysis.
Customer Retention and Churn Analysis.
Profitability Analysis.
Customer Value Measurement.
Product Packaging.
Call Centers.
Sales Contract Analysis.
Database Marketing Lessons Learned.
Some Lingering Questions.
4. Data Warehousing by Industry.
Retail.
Uses of Data Warehousing in Retail.
Market Basket Analysis.
In-Store Product Placement.
Product Pricing.
Product Movement and the Supply Chain.
The Good News and Bad News in Retailing.
Case Study: Hallmark.
Financial Services.
Uses of Data Warehousing in Financial Services.
The Good News and Bad News in Financial Services.
Case Study: Royal Bank of Canada.
Telecommunications.
U.S. Local Service Carriers.
U.S. Long-Distance Carriers.
International Long-Distance Carriers.
Wireless Carriers.
Uses of Data Warehousing in Telecommunications.
The Good News and Bad News in Telecommunications.
Case Study: GTE.
Transportation.
Yield Management.
Frequent-Passenger Programs.
Travel Packaging and Pricing.
Fuel Management.
Customer Retention.
The Good News and Bad News in Transportation.
Case Study: Qantas.
Government.
The Good News and Bad News in Government.
Case Study: State of Michigan.
Health Care.
Uses of Data Warehousing in Health Care.
The Good News and Bad News in Health Care.
Case Study: Aetna U.S. Healthcare, U.S. Quality Algorithms.
Insurance.
Uses of Data Warehousing in Insurance.
The Good News and Bad News in the Insurance Industry.
Case Study: California State Automobile Association.
Entertainment.
Case Study: Twentieth Century Fox.
Some Lingering Questions.
II. GETTING THE TECHNOLOGY.
5. The Underlying Technologies: A Primer.
Data Warehouse Architecture.
The Operational Data Store.
Two-Tier Versus n-Tier.
Middleware.
Databases and What They're Good For.
Multidimensional Databases.
Metadata.
Disseminating the Information: Application Software.
Graphical User Interfaces.
A Word about the Web.
Development Definitions and Differentiators.
OLAP Subcategories.
Data Modeling and Design Tools.
Data Extraction and Loading Tools.
Management and Administration.
Putting It All Together.
Some Lingering Questions.
6. What Managers Should Know about Implementation.
What You Should Know about Data Warehouse Methodologies.
Evaluating a Methodology.
The Data Warehouse Implementation Process.
The Steps in Data Structure and Management.
The Steps in Application Development.
Who Should Be Doing What?
Development Job Roles and Responsibilities.
Consultants Versus Full-Time Staff.
The Lost Fine Art of Skill Delineation.
Good and Evil Square Off:A Tale of Two Project Plans.
Executive Involvement on the Project.
Profile: Hank Steermann of Sears, Roebuck and Co..
Some Lingering Questions.
7. Value or Vapor? Finding the Right Vendors.
The Hardware Vendors.
Five Questions to Ask Your Hardware Vendor.
The Database Vendors.
Five Questions to Ask Your Database Vendor.
TPC Benchmarks.
The Application Vendors.
Five Questions to Ask Your Application Tool Vendor.
Data-Mining Tools: A Breed Apart.
Ten Questions to Ask Your Data-Mining Vendor.
The Consultants.
The Big Guys.
The Little Guys.
A Word about the Analysts.
A Word about the Vendors.
Five Questions Your Consultant Should Ask You.
The RFP Process.
The Components of a Good RFP.
A Sample Table of Contents.
Some Lingering Questions.
III. GETTING READY.
8. Data Warehousing's Business Value Proposition.
Return on Investment.
Hard ROI: The Tangible Benefits.
Soft ROI: The Intangible Benefits.
Budgeting for the Data Warehouse.
Technology Costing.
Resource Costing.
Obtaining Funding - But Not Too Much!
Data Warehouse Operations Planning.
Developing an Operating Plan.
Are You Ready for a Data Warehouse? A Quiz.
Data Warehouse Readiness Score.
Some Lingering Questions.
9. The Perils and Pitfalls.
The New Top 10 Data-Warehousing Pitfalls.
Pitfall #1: The Data Warehouse as Panacea Syndrome.
Pitfall #2: They Talked to End-Users--But the Wrong Ones!
Pitfall #3: Too Much Time Spent on Research, Alienating Constituents.
Pitfall #4: Bogging a Good Project Down by Creating Metadata.
Pitfall #5: Being Sidetracked by "Neat to Know" Analysis.
Pitfall #6: Adopting Decision Support Without Supporting Decisions.
Pitfall #7: Greediness on the Part of Development Organizations.
Pitfall #8: Lack of "Internal PR".
Pitfall #9: Failing to Acknowledge That DSS Applications Are Finite.
Pitfall #10: Overemphasizing Development and Ignoring Deployment.
Thinking of Outsourcing?
Data Warehousing's Dirty Little Secrets.
The Politics of Data Warehousing.
The Top 10 Signs of Data Warehouse Sabotage.
The Vanguards of Data Warehousing.
Case Study: Charles Schwab & Co., Inc..
10. What to Do Now.
If You Need a Data Warehouse.
Establish Up-Front Success Metrics.
Consider Benchmarking.
Research External Staff.
Prepare Your Environment.
Classify Your Stakeholders.
Ramp Up Support Capabilities.
Profile: Philippe Klee, Qantas Airways.
Look Outside Your Box.
Solicit a Request for Information.
If You Already Have a Data Warehouse.
Establish a Formal Postmortem Process.
Inventory Existing Applications.
Spring for an Audit.
Improve Customer-Facing Business Processes.
Establish a Closed-Loop Process.
Go Web, Young Man!
Case Study: Allsport.
Consider Branching Out Vertically.
Consider Branching Out Horizontally.
If You Have a Data Mart or Marketing Analysis System.
Share Your Toys.
Migrate to Enterprisewide.
An Insider's Crystal Ball.
Clickstream Storage.
Enterprise Resource Planning.
Extending the Data Warehouse to External Vendors.
Customized Web Portals.
Real-Time E-Marketing.
Privacy.
The Whole Truth.
Appendix: Haven't Had Enough? Suggested Reading.
Business Books.
Technology Books.
Websites.
Index. 0201657805T04062001