e-Data: Turning Data Into Information With Data Warehousing / Edition 1

e-Data: Turning Data Into Information With Data Warehousing / Edition 1

by Jill Dyche, Jill Dyché
     
 

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ISBN-10: 0201657805

ISBN-13: 9780201657807

Pub. Date: 02/09/2000

Publisher: Addison-Wesley

"Jill Dyché does an expert job of describing the varied uses of data warehouses and data marts--not only in marketing, but across lines of business."
-- Martha Rogers, Ph.D.

Over the last ten years the strategic use of detailed data has changed the face of business. This change was made possible through the use of data

Overview

"Jill Dyché does an expert job of describing the varied uses of data warehouses and data marts--not only in marketing, but across lines of business."
-- Martha Rogers, Ph.D.

Over the last ten years the strategic use of detailed data has changed the face of business. This change was made possible through the use of data warehouses, which are now widely accepted for their role in the delivery of decision-support and business-intelligence applications. Today's data warehouses are the critical hubs of such burgeoning strategic initiatives as e-commerce, knowledge management, database marketing, and customer relationship management. Given this, a working knowledge of the fundamentals of data warehousing is essential for today's executives, managers, and other professionals who must maximize the power of data warehousing in both existing business contexts and future strategic initiatives.

Written especially for these business professionals, 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. This 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. This book provides:

  • Definitions of key data warehousing terms
  • Descriptions of emerging database marketing applications that mandate detailed data
  • A primer on 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 warehouses
  • Checklists and success metrics for evaluating data warehouse effectiveness
  • Coming trends in the use of e-data in business

Inspirational real-world case studies and staff profiles appear throughout, showcasing data warehousing's "vanguards"--companies that have succeeded in achieving long-term financial and strategic benefits. Included are Bank of America, Charles Schwab & Co., Qantas Airways, GTE, Royal Bank of Canada, Sears, and Twentieth Century Fox.

e-Data provides invaluable information about data warehousing as a whole, its development and strategic value, the technologies that support it, and its effect on corporate decision making--information that will enable you to turn a gold mine of raw data into valuable information, position your company for market leadership, and enhance customer satisfaction.

0201657805B04062001

Product Details

ISBN-13:
9780201657807
Publisher:
Addison-Wesley
Publication date:
02/09/2000
Series:
Information Technology Series
Edition description:
New Edition
Pages:
368
Product dimensions:
7.40(w) x 9.10(h) x 1.00(d)

Related Subjects

Table of Contents

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.

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