Prentice Hall

Business



The Essential Guide to Data Warehousing
Lou Agosta, Giga Information Group, Chicago, Illinois

ISBN-10: 013085087X
ISBN-13: 9780130850874

Publisher: Prentice Hall
Copyright: 2000
Format: Paper; 526 pp
Published: 08/26/1999

Suggested retail price: $34.99
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For any introductory to intermediate course in data warehousing; ideal for both computer science and business students.

This is the only data warehousing book that speaks directly to newcomers—explaining the benefits, risks, technologies, and processes with remarkable clarity and insight. Consultant Lou Agosta shows students how data warehousing can dramatically reduce business uncertainty by transforming a tidal wave of information into knowledge that can be acted upon. Agosta explains the quantitative business case for data warehousing, including its potential to reduce supply chain management costs, enhance consumer cross-selling, and strengthen brand relationships. Next, he drills down, helping students understand how organizations can align their business and technical goals for data warehousing; and walking through the entire implementation process, from planning and design through deployment and performance optimization. Finally, students will be introduced to new innovations such as metadata, and their impact on the enterprise.

  • Leveraging data warehousing to understand customers and strengthen business relationships.

    • Covers all the applications businesses have discovered for data warehousing, including supply chain applications, cross-selling, total quality management, profitability analysis, and much more.

  • Best practices for designing, implementing, and optimizing data warehouses—and how to avoid failure.

    • Walks step-by-step through data warehouse development planning, project management, and deployment, helping students understand the decision-making process, in-depth, at each stage.

  • Includes coverage of data warehousing and the Web.

    • Using the Web as delivery system, data store, and business intelligence portal.

  • Detailed, real-world coverage of “data cleaning”—making sure data is accurate and reliable enough to depend upon.
  • Includes a comprehensive, insight-filled glossary, plus to-the-point executive summaries in every chapter.

I. FUNDAMENTAL COMMITMENTS.

1. Basic Data Warehousing Distinctions.

An Architecture, Not A Product. The One Fundamental Question. The One Question—The Thousand and One Answers…. The First Distinction: Transaction and Decision Support System. Data Warehouse Sources of Data. Dimensions. The Data Warehouse Fact. The Data Warehouse Model of the Business: Alignment. The Data Cube. Aggregation. Data Warehouse Professional Roles. The Data Warehouse Process Model. Summary.

2. A Short History of Data.

In the Beginning…. Fast Forward to Modern Times. The Very Idea of Decision Support. From Mainframes To PCs. The Promise of the Relational Database. Data Every Which Way. From Client-Server to Thin Client Computing. Why Will Things Be Different This Time? The More Things Change, the More They Stay the Same. Model of Technology Dynamics. Summary.

3. Justifying Data Warehousing.

Competition for Limited Resources. An Integrated Business and Technology Solution. Economic Value, Not Business Benefits. Selling the Data Warehouse. The Reporting Data Warehouse: Running Fewer Errands. The Supply Chain Warehouse. The Cross-Selling Warehouse. The Total Quality Management Data Warehouse. The Profitability Warehouse. Data Warehousing Case Vignettes in the Press. Summary.

4. Data Warehousing Project Management.

Simulating a Rational Design Process. Managing Project Requirements. Managing the Development of Architecture. Managing Project Schedule. Managing Project Quality. Managing Project Risks. Managing Project Documentation. Managing the Project Development Team. Managing Project Management. Summary.

II. DESIGN AND CONSTRUCTION.

5. Business Design: The Unified Representations of The Customer and Product.

The Critical Path: Alignment. A Unified Representation of the Customer. Data Scrubbing. The Cross-Functional Team. Hierarchical Structure. Customer Demographics. A Unified Representation of the Product. Data Marts: Between Prototype and Retrotype. Summary.

6. Total Data Warehouse Quality.

The Information Product. Data Quality as Data Integrity. Intrinsic Qualities. Ambiguity. Timeliness and Consistency in Time. Security. Secondary Qualities. Credibility. Quality Data, Quality Reports. Information Quality, System Quality. Performance. Availability. Scalability. Functionality. Maintainability. Reinterpreting the Past. Summary.

7. Data Warehousing Technical Design.

Use case Scenarios. Abstract Data Types and Concrete Data Dimensions. Data Normalization: Relevance and Limitations. Dimensions and Facts. Primary and Foreign Keys. Design for Performance: Technical Interlude. Summary.

8. Data Warehouse Construction Technologies: SQL.

The Relational Database: A Dominant Design. Twelve Principles. Thinking in Sets: Declarative and Procedural Approaches. Data Definition Language. Indexing: B-Tree. Indexing: Hashing. Indexing: Bitmap. Indexing Rules of Thumb. Data Manipulation Language. Data Control Language. Stored Procedures. User-Defined Functions. Summary.

9. Data Warehouse Construction Technologies: Transaction Management.

The Case For Transaction Management: The ACID Test. The Logical Unit of Work. Two-tier and Three-tier Architectures. Distributed Architecture. Middleware: Remote Procedure Call Model. Middleware: Message-Oriented Middleware. The Long Transaction. Summary.

III. OPERATIONS AND TRANSFORMATIONS.

10. Data Warehouse Operation Technologies: Data Management.

Database Administration. Backing Up the Data (in the Ever-Narrowing Backup Window). Recovering the Database: Crash Recovery. Recovering the Database: Version (Point-in-Time) Recovery. Recovering the Database: Roll-Forward Recovery. Managing Lots of Data: Acres of Disk. Managing Lots of Data: System-Controlled Storage. Managing Lots of Data: Automated Tape Robots. RAID Configurations. Summary.

11. Data Warehousing Performance.

Performance Parameters. Denormalization for Performance. Aggregation For Performance. Buffering For Performance. Partitioning For Performance. Parallel Processing: Shared Memory. Parallel Processing: Shared Disk. Parallel Processing: Shared Nothing. Data Placement: Colocated Join. Summary.

12. Data Warehousing Operations: The Information Supply Chain.

A Process, Not an Application. The Great Chain of Data. Partitioning: Divide and Conquer. Determining Temporal Granularity. Aggregate Up To the Data Warehouse. Aggregates in the Data Warehouse. The Debate about the Data Warehouse Data Model. The Presentation Layer. Integrated Decision Support Processes. Summary.

13. Metadata and Metaphor.

Metaphors Alter Our Perceptions. A New Technology, a New Metaphor. Metadata are Metaphorical. Semantics. Forms of Data Normalization and Denormalization. Metadata Architecture. Metadata Repository. Models and Metamodels. Metadata Interchange Specification (MDIS). Metadata: A Computing Grand Challenge. Summary.

14. Aggregation.

On-line Aggregation, Real-Time Aggravation. The Manager's Rule of Thumb. A Management Challenge. Aggregate Navigation. Information Density. Canonical Aggregates. Summary.

IV. APPLICATIONS AND SPECULATIONS.

15. OLAP Technologies.

OLAP Architecture. Cubes, Hypercubes, and Multicubes. OLAP Features. The Strengths of OLAP. Limitations. Summary.

16. Data Warehousing and the Web.

The Business Case. The Web as a Delivery System. Key Internet Technologies. Web Harvesting: The Web as the Ultimate Data Store. The Business Intelligence Portal. Summary.

17. Data Mining.

Data Mining and Data Warehousing. Data Mining Enabling Technologies. Data Mining Methods. Data Mining: Management Perspective. Summary.

18. Breakdowns: What Can Go Wrong.

The Short List. The Leaning Cube of Data. The Data Warehouse Garage Sale. Will the Future be Like the Past? Model Becomes Obsolete. Missing Variables. Obsessive Washing. Combinatorial Explosion. Technology and Business Misalignment. Becoming a Commodity. Summary.

19. Future Prospects.

Enterprise Server Skills to be in High Demand. The Cross-Fictional, Oops, -Functional Team. Governance. The Operational Data Warehouse. Request for Update. The Web Opportunity: Agent Technology. The Future of Data Warehousing. Summary.

Glossary.

References.

Index.

Lou Agosta has provided database and data warehousing solutions for clients in the insurance, retail, publishing, fast food, and software industries since 1980. He has published extensively on issues in business and data warehousing technology, presented on data warehousing at IT Forum, and served as an instructor in Logic and System Design at Depaul, Loyola, and Roosevelt Universities.


8508G-5

"Lou Agosta's book is a guide to a better understanding of how collections of separately collected bits of information can be organized to serve the needs of an enterprise. Agosta's book offers technical and managerial insights into how to leverage the data warehousing concept for best advantage."- Paul A. Strassman, Chairman and CEO, Software Testing Assurance Corporation

Data warehousing for everyone! The essential guide for all business people.

This is the only data warehousing book that speaks directly to business leaders and data warehousing newcomers -- explaining the benefits, risks, technologies, and processes with remarkable clarity and insight. Leading consultant and industry analyst Lou Agosta shows how data warehousing can dramatically reduce business uncertainty by transforming a tidal wave of information into knowledge you can act on.

Agosta presents the quantitative business case for (and against) data warehousing, and helps you evaluate every key data warehousing application in the context of your own enterprise. Learn how to use data warehousing to slash supply chain management costs, make cross-selling more effective, strengthen customer and brand relationships, promote product quality, and more. Discover how to align your business and technical goals for data warehousing; then review every stage of the data warehousing project lifecycle, from planning and design through deployment and optimization. Understand what can go wrong -- and how to keep it from happening to you! Coverage includes:

  • Creating a unified representation of your customers and products
  • Data quality: the key to a successful data warehousing
  • The key basics of data warehouse technical design
  • Best practices for data warehouse operations
  • Web-based data warehousing, metadata, and other key innovations

Read by business and technical leaders from Paul A. Strassmann to Arno Penzias, The Essential Guide to Data Warehousing is for every business executive and IT professional seeking to understand the benefits, risks, and technologies of data warehousing -- without the jargon and hype!

View a Sample Chapter PDF: /samplechapter/013085087X.pdf

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