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| Preface | |
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| Acknowledgments | |
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| Introduction to Data Warehousing: Between Uncertainty and Knowledge | |
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| Learning to Live with Uncertainty | |
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| Bombarded by Paradigm Shifts | |
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| Reducing Uncertainty Through Knowledge | |
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| The Data Warehouse Provides Knowledge as a Special Kind of Representation | |
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| Kinds of Knowledge | |
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| Instrumental Knowledge | |
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| Pragmatic Knowledge | |
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| Fundamental Business Imperatives | |
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| The Three Imperatives | |
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| The Data Warehouse Represents the Business | |
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| Complex Artifact, Simple Principles | |
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| Aligning the Business and the Warehouse | |
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| Data Warehouse Map of the Business | |
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| Cant Shrink-Warp Knowledge of the Business | |
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| A Single Version of the Truth | |
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| An Inventory of Knowledge: Putting the "Decision" Back into "Decision Support" | |
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| Fundamental Commitments | |
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| Basic Data Warehousing Distinctions | |
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| An Architecture, Not a Product | |
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| The One Fundamental Question | |
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| The One Question--The Thousand and one Answers... | |
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| The First Distinction: Transaction and Decision Support System | |
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| Data Warehouse Sources of Data | |
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| Dimensions | |
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| The Data Warehouse Fact | |
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| The Data Warehouse Model of the Business: Alignment | |
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| The Data Cube | |
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| Aggregation | |
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| Data Warehouse Professional Roles | |
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| The Data Warehouse Process Model | |
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| Summary | |
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| A Short History of Data | |
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| In the Beginning... | |
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| Fast Forward to Modern Times | |
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| The Very Idea of Decision Support | |
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| From Mainframes to Pcs | |
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| The Promise of the Relational Database | |
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| Data Every Which Way | |
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| From Client-Server to thin Client Computing | |
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| Why Will Things Be Different this Time? | |
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| The More Things Change, the More They Stay the Same | |
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| Model of Technology Dynamics | |
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| Summary | |
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| Justifying Data Warehousing | |
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| Competition For Limited Resources | |
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| An Integrated Business And Technology Solution | |
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| Economic Value, Not Business Benefits | |
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| Selling the Data Warehouse | |
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| The Reporting Data Warehouse: Running Fewer Errands | |
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| The Supply Chain Warehouse | |
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| The Cross-Selling Warehouse | |
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| The Total Quality Management Data Warehouse | |
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| The Profitability Warehouse | |
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| Data Warehousing Case Vignettes in the Press | |
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| Summary | |
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| Data Warehousing Project Management | |
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| Simulating a Rational Design Process | |
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| Managing Project Requirements | |
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| Managing the Development of Architecture | |
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| Managing Project Schedule | |
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| Managing Project Quality | |
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| Managing Project Risks | |
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| Managing Project Documentation | |
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| Managing the Project Development Team | |
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| Managing Project Management | |
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| Summary | |
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| Design and Construction | |
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| Business Design: The Unified Representations of The Customer and Product | |
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| The Critical Path: Alignment | |
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| A Unified Representation of the Customer | |
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| Data Scrubbing | |
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| The Cross-Functional Team | |
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| Hierarchical Structure | |
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| Customer Demographics | |
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| A Unified Representation of the Product | |
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| Data Marts: Between Prototype and Retrotype | |
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| Summary | |
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| Total Data Warehouse Quality | |
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| The Information Product | |
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| Data Quality as Data Integrity | |
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| Intrinsic Qualities | |
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| Ambiguity | |
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| Timeliness and Consistency in Time | |
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| Security | |
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| Secondary Qualities | |
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| Credibility | |
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| Quality Data, Quality Reports | |
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| Information Quality, System Quality | |
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| Performance | |
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| Availability | |
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| Scalability | |
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| Functionality | |
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| Maintainability | |
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| Reinterpreting the Past | |
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| Summary | |
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| Data Warehousing Technical Design | |
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| Use Case Scenarios | |
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| Abstract Data Types and Concrete Data Dimensions | |
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| Data Normalization: Relevance and Limitations | |
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| Dimensions and Facts | |
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| Primary and Foreign Keys | |
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| Design for Performance: Technical Interlude | |
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| Summary | |
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| Data Warehouse Construction Technologies: SQL | |
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| The Relational Database: A Dominant Design | |
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| Twelve Principles | |
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| Thinking in Sets: Declarative and Procedural Approaches | |
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| Data Definition Language | |
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| Indexing: B-Tree | |
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| Indexing: Hashing | |
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| Indexing: Bitmap | |
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| Indexing Rules of Thumb | |
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| Data Manipulation Language | |
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| Data Control Language | |
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| Stored Procedures | |
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| User-Defined Functions | |
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| Summary | |
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| Data Warehouse Construction Technologies: Transaction Management | |
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| The Case For Transaction Management: The Acid Test | |
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| The Logical Unit of Work | |
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| Two-Tier and Three-Tier Architectures | |
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| Distributed Architecture | |
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| Middleware: Remote Procedure Call Model | |
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| Middleware: Message-Oriented Middleware | |
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| The Long Transaction | |
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| Summary | |
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| Operations and Transformations | |
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| Data Warehouse Operation Technologies: Data Management | |
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| Database Administration | |
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| Backing Up the Data (in the Ever-Narrowing Backup Window) | |
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| Recovering the Database: Crash Recovery | |
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| Recovering the Database: Version (Point-in-Time) Recovery | |
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| Recovering the Database: Roll-Forward Recovery | |
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| Managing Lots of Data: Acres of Disk | |
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| Managing Lots of Data: System-Controlled Storage | |
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| Managing Lots of Data: Automated Tape Robots | |
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| Raid Configurations | |
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| Summary | |
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| Data Warehousing Performance | |
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| Performance Parameters | |
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| Denormalization for Performance | |
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| Aggregation for Performance | |
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| Buffering for Performance | |
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| Partitioning for Performance | |
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| Parallel Processing: Shared Memory | |
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| Parallel Processing: Shared Disk | |
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| Parallel Processing: Shared Nothing | |
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| Data Placement: Colocated Join | |
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| Summary | |
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| Data Warehousing Operations: The Information Supply Chain | |
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| A Process, Not an Application | |
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| The Great Chain of Data | |
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| Partitioning: Divide and Conquer | |
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| Determining Temporal Granularity | |
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| Aggregate Up to the Data Warehouse | |
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| Aggregates in the Data Warehouse | |
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| The Debate About the Data Warehouse Data Model | |
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| The Presentation Layer | |
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| Integrated Decision Support Processes | |
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| Summary | |
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| Metadata and Metaphor | |
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| Metaphors Alter Our Perceptions | |
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| A New Technology, a New Metaphor | |
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| Metadata are Metaphorical | |
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| Semantics | |
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| Forms of Data Normalization and Denormalization | |
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| Metadata Architecture | |
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| Metadata Repository | |
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| Models and Metamodels | |
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| Metadata Interchange Specification (Mdis) | |
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| Metadata: A Computing Grand Challenge | |
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| Summary | |
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| Aggregation | |
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| On-Line Aggregation, Real-Time Aggravation | |
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| The Manager's Rule of Thumb | |
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| A Management Challenge | |
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| Aggregate Navigation | |
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| Information Density | |
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| Canonical Aggregates | |
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| Summary | |
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| Applications and Speculations | |
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| OLAP Technologies | |
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| OLAP Architecture | |
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| Cubes, Hypercubes, and Multicubes | |
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| OLAP Features | |
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| The Strengths of OLAP | |
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| Limitations | |
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| Summary | |
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| Data Warehousing and the Web | |
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| The Business Case | |
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| The Web as a Delivery System | |
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| Key Internet Technologies | |
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| Web Harvesting: the Web as the Ultimate Data Store | |
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| The Business Intelligence Portal | |
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| Summary | |
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| Data Mining | |
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| Data Mining and Data Warehousing | |
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| Data Mining Enabling Technologies | |
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| Data Mining Methods | |
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| Data Mining: Management Perspective | |
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| Summary | |
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| Breakdowns: What Can Go Wrong | |
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| The Short List | |
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| The Leaning Cube of Data | |
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| The Data Warehouse Garage Sale | |
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| Will the Future be Like the Past? | |
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| Model Becomes Obsolete | |
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| Missing Variables | |
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| Obsessive Washing | |
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| Combinatorial Explosion | |
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| Technology and Business Misalignment | |
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| Becoming a Commodity | |
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| Summary | |
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| Future Prospects | |
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| Enterprise Server Skills to be in High Demand | |
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| The Cross-Fictional, Oops, -Functional Team | |
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| Governance | |
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| The Operational Data Warehouse | |
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| Request for Update | |
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| The Web Opportunity: Agent Technology | |
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| The Future of Data Warehousing | |
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| Summary | |
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| Glossary | |
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| References | |
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| Index | |