Building a Data Warehouse for Decision S(Page: 2)

Building a Data Warehouse for Decision Support, 1/e

Vidette Poe, Sierra Madre, CA

Table Of Contents:
(NOTE: Each chapter concludes with a summary.)
1. Let's Start with the Basics.
What is a Decision Support System? Understanding Operational versus Analytical Processing. What is A Data Warehouse? Real Life Data Warehouse Examples. Community Mutual Insurance Company. A Corporate Overview Meeting the Need for Information-Predecessor Systems. In the Beginning . . . Gathering Data Requirements. Infrastructures. User Reaction to Initial Development. The User Community. Building on the Warehouse. What the Future Holds. 20/20 Hindsight. Tips from the Trenches 1.1: Learning from Community. Mutual Internal Cost Allocations: Chargebacks. A Consumer Packaged Goods Company. A Corporate Overview. How it Got Started. Implementation Project. Architecture and Infrastructure. End User Reaction. Data Warehouse Expansion 20/20 Hindsight. Endnote.
2. Understanding Terms and Technology.
Analytical Processing. Operational Processing. Decision Support Systems. Data Warehouse. Environment for Data Access. Architecture. Technical Infrastructures. Source and Target Data. Levels of Users. Classes of Tools. Decision Support System (DSS) Applications. Data Transformation. Data Transformation Tools. Middleware Tools. Metadata. Star Schema. Hierarchies. Granularity. Database Gateway. Megabytes, Gigabytes, and Terabytes. Decision Support Development Cycle.
3. Understanding Architecture and Infrastructures.
The Task at Hand. Understanding Data Warehouse Architecture. The Characteristics of Data Warehouse Architecture. Data Is Extracted from Source Systems, Database, or Files. The Data from the Source Systems is Integrated and Transformed before Being Loaded into the Data Warehouse. A Separate Read-only Database is Created for Decision Support Data. Users Access the Data Warehouse via a Front End Tool or Application. Expanding the Generic Data Warehouse Architecture. Understanding the Relationship of Infrastructures and Architecture. Architecture and Infrastructures as a Separate Project. Tips From the Trenches 3.1: Architecture and Infrastructures. And the Answer is . . .
4. The Decision Support Life Cycle.
Life Cycles for System Development. Issues Affecting the Decision Support Life Cycle. The Decision Support Life Cycle In an Architected Environment. The Phases of the Decision Support Life Cycle (DSLC). Phase 1: Planning. Phase 2: Gathering Data Requirements and Modeling. Gathering Data Requirements. Data Modeling. Phase 3: Physical Database Design and Development. Phase 4: Data Mapping and Transformation. Phase 5: Populating the Data Warehouse. Tips from the Trenches 4.1: Availabiliy of Data. Phase 6: Automating Data Management Procedures. Phase 7: Application Development-Creating the Starter Set of Reports. Phase 8: Data Validation and Testing. Phase 9: Training. Phase 10: Rollout.
5. Getting Started with Data Warehouse Development.
The Proof Is in the Pilot. Clarify the Purpose and Goal of the Pilot Project. Treat the Pilot like a Development Project. Building on the Pilot. Choosing a Business Area for Data Warehouse Development. Tips from the Trenches 5.1: Choosing a Business Area. Ensuring a Successful Data Warehouse. Tips from the Trenches 5.2: Building a Successful Data Warehouse "The Big Eight". Be Clear on Your Goal. Understand the Chosen Data Warehouse Architecture. Make Sure the Technical Infrastructures Are in Place or Being Put in Place. Clarify the Project Team's Responsibility and Final Deliverable. Make Sure the Members of the Project Team Understand the Difference between Operational and Decision Support Data. Get the Correct Training. Get the Right Resources. Choose Front End Data Access Software Based on User Needs and Abilities.
6. Gathering Data Requirements.
A Proper Mindset. User Interviews. The Purpose of Interviews. Setting up Successful Interviews. Who to Interview. Tips from the Trenches 6.1: Setting Up Successful Interviews. Key End Users and Analysts from the Target Business Functions. Managers from the Target Business Functions. Analysts & Users from Related Business Functions. Managers from Related Business Functions. Executives. What to Ask End Users. Job Responsibilities. Current Analysis. Ad Hoc Analysis. Business Analyses. Data Specific Information. A Wish List. What to Ask Executives. Documenting What You Heard. What You Have to Know for DSS. Developing the Data Model. Dimensional Business Model. Logical Data Model. Tips from the Trenches 6.2: The Basics of Data Modeling.
7. Designing the Database for a Data Warehouse.
Transaction-processing Databases. Decision Support Databases. Star Schema Database Design. The Benefits of Using a Star Schema. Understanding Star Schema Design-Facts and Dimensions. Varieties of Star Schemas. How to Read the Diagrams. Tips from the Trenches 7.1: Understanding Facts and Dimensions. Simple Star Schemas. Multiple Fact Tables. Variations of a Star Schema. Multi-Star Schemas. A Salad Dressing Example. Understanding the Available Data, Browsing the Dimension Tables. Using Table Attributes. Creating Attribute Hierarchies. Aggregation. Denormalizaiton. Data Warehouse Database Design Examples. Reservation Database. Investment Database. Health Insurance Database. Putting It All Together.
8. Successful Data Access.
General Understanding of Data Access. What Are You Really Trying To Do? Types of Access. Levels of Users. What Is a DSS Application? Data Access Characteristics. Visualization of the Data Warehouse. User Formulates Request. Metrics and Calculated Metrics. Constraining a Request. How the Request is Processed. Presentation of Results. Reports. Graphs. Maps. Communicate Findings. Advanced Features. Advanced Analytics. Batch Query Processing. DSS Application Development. Classes of Tools. Data Access/Query Tools. Report Writers. Multidimensional Database Management Systems (MDBMS). Advanced DSS Tools. Executive Information Systems (EIS). Tiered Architectures. Data Design Requirements. Metadata. Operational Metadata versus DSS Metadata. Warehouse Changes. How Is DSS Metadata Used? Application Deployment. Selecting Tools for Your Organization. One Tool Fits All? The Request for Proposal (RFP). Key Considerations. What Matters to You? Selecting a Vendor Not Just a Tool.
9. Training, Support, and Rollout.
Success Criteria. Training. Support. Internal marketing of the Data Warehouse. Data Warehouse Marketing Ideas. Target Specific Groups. Get Clear and Visible Management Support. Provide Visible Opportunities. Be Proactive. Create a Publication. Planning a Rollout: Deployment. Phased Rollout Approach. Logistics of a Rollout.
Appendices.
Index.