Planning, building, and implementation

(NOTE: Each chapter begins with an Introduction and concludes with Questions and Projects.) Preface. 1. Brief History of Information Technology. History of IT. Silos of Business Information. What a Data Warehouse Is. Answering Business Questions. The Enterprise Data Model. Methodology Outline: Scope, Pilot, Production. Spiral Process. Rapid Application Development. Data Warehouse Architecture. Information Worker Access. 2. Business and IT Alignment for the Data Warehouse. Development Tutorial. Business Process Reengineering. Business and IT Alignment. Positional Assessment. Capabilities Position. Situational Assessment. Value Chain Assessment. The Flexible IT Department. Open System. ROI and Justification. IT Service Management. 3. How to Plan and Build a Data Warehouse. Business Needs Analysis. IT Readiness Evaluation. Project Selection. Warehouse Conceptual Architecture. Warehouse Logical Architecture. Warehouse Physical Architecture. Data Architecture. Implementation. 4. Project Selection and Scope. Business Requirements Discovery: Executive Interviews. Business Requirements Definition: JAD Sessions. Scoping and Estimation. Planning. Define the Project. Plan the Project. Manage the Project. Team Members and Skill Sets. 5. Data Modeling. Entities. Enterprise Data Modeling. Just-Enough Enterprise Data Model. Star Schema Analysis: Creating the Dimensional Model. Model Development Methods. Granularity. Time. Events. Developing the Dimensional Model. Snowflakes. Physical Modeling. Ten Commandments of Dimensional Data Modeling. Two Faces of the Pyramid: Transactional ER and Analytical Star. 6. The Metadata Repository. Introduction: What is Metadata? The Metadata Usage Model. Implementation-Time Metadata. Active Run-Time Metadata. Passive Run-Time Metadata. The Metadata Dimensions Model. Activities Metadata. Locations Metadata. Entities Metadata. People Metadata. Motivation Metadata. Time Metadata. Metadata Capture and Maintenance. Initial Metadata Creation. Large Warehouse or Multiple-Subject- Area. The Information Users' Guide. 7. Achieving Quality Information in the Data Warehouse. The Value of Quality Information. Difficulty of Obtaining Quality Data. Methods to Evaluate the Value of Quality Data. For What Quality Should We Strive? Methods to Evaluate the Data. Tools to Evaluate the Data. The Data Evaluation or Audit. 8. The Conceptual and Logical Data Warehouse. Why Principle-Centered? Metaprinciples: Principles About the Principles. The Principles. General Principles. Data Principles. Query Principles. Working Store Principles. Metadata Principles. Scalability Principles. Warehouse Management Principles. Architectural Principles and the Zachman Framework. Architectural Principles Summary. Conceptual Models. Unplanned Decision Support. Virtual Data Warehouse. Semantic Integration of Subject Areas. Query Managed Subject Areas. Monolithic Warehouse. Standard Data Archive. Architecture Selection. Unplanned Decision Support. Virtual Data Warehouse. Semantic Integration of Subject Areas. Query Managed Subject Areas. Monolithic Warehouse. Standard Data Archive. Logical Models. 9. The Physical Data Warehouse. Physical Storage. Database Considerations. The Database Server Hardware. Operating Systems. Performance. Resilience. Integration. Security. Manageability. The Query Server and the Application Server. Networks and Connectivity. Middleware. Usage Tracker. Intelligent Warehouse. Transaction Processing Monitors. Middleware Selection. Knowledge Engineering Workstations. Deploying the Architecture. 10. Data Transformation. Planning. Data Extraction and Movement Methods. Data Transformation. Data Loading. 11. Data Access. Tool Selection. Information Consumer Types. All That FLAP. Vendor Selection Criterion. Information Distribution. Web Access. Spreadsheets. Visualization Tools. Query Tools. Technical Functionality. Query Functionality. Presentation Functionality. Interface Functionality. EIS and DSS Tool Types. Query Management. Data Mining Introduction. 12. Data Mining. Data Preparation. Neural Networks. The Genetic Algorithm. Clustering and Classification. Decision Trees. Statistics. Regression Modeling. Discriminant Analysis. Software Products. Software Example. Glossary. Bibliography. References. Index.