By Jill Dyche, Baseline Partner and Co-Founder.
Published by Addison Wesley, 2000.

Written especially for executives and managers,e-Data: Turning Data into Information with Data Warehousing covers data warehousing and its surrounding technologies in a straightforward and engaging way, illustrating how companies are leveraging their data warehouses to serve a wide range of business needs.

The book clearly lays out what business people should know about data warehouse implementation and the best techniques for evaluating and justifying new data warehouses and data marts.

Best Practices and Techniques

• Definitions of key data warehousing terms
• Descriptions of emerging database marketing applications that mandate detailed data
• A primer of data warehouse technologies, as well as a clear taxonomy of different analysis types
• Staffing and hiring tips for data warehouse development teams
• A review of the diverse uses of business intelligence across various industries
• Key questions to ask your vendors and consultants
• A fresh perspective on the politics involved with data warehousing
• Checklists and success metrics for evaluating data warehouse effectiveness
• Insights about the use of e-data in business

Real World Case Studies

• Bank of America
• Charles Schawb & Co.
• Quantas Airways
• GTE
• Royal Bank of Canada
• Sears
• Twentieth Century Fox

Table of Contents

Foreword
Acknowledgements
Introduction
Part I: Getting the Value

Chapter 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
Some Trite Data-Warehousing Aphorisms
Venus and Mars: How IT Businesspeople Communicate
Some other Buzzwords and What They Mean
Some Lingering Questions

Chapter 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

Chapter 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 Measurements
Product Packaging
Call Centers
Sales Contract Analysis
Database Marketing Lessons Learned
Some Lingering Questions

Chapter 4

DATA WAREHOUSING BY INDUSTRY
Retail
Case Study: Hallmark
Financial Services
Case Study: Royal Bank of Canada
Telecommunications
Case Study: GTE
Transportation
Case Study: Qantas
Government
Case Study: State of Michigan
Health Care
Case Study: Aetna U.S. Healthcare, U.S. Quality Algorithms
Insurance
Case Study: California State Automobile Association
Entertainment
Case Study: Twentieth Century Fox
Some Lingering Questions
Part II: Getting the Technology

Chapter 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

Chapter 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

Chapter 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
Part III: Getting Ready

Chapter 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

Chapter 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
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.

Chapter 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: Phillipe 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 Process
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 of Marketing Analysis Systems
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
Index