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Building a data warehouse for decision support

Author: Vidette Poe; Patricia Klauer; Stephen Brobst
Publisher: Upper Saddle River, NJ : Prentice Hall PTR, ©1998.
Edition/Format:   Print book : English : 2nd edView all editions and formats

This new edition features chapters on data integration, data management, and data cleansing and transformation. Case studies are be added, and there is an updated product-specific appendix. A CD-ROM  Read more...


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Additional Physical Format: Online version:
Poe, Vidette.
Building a data warehouse for decision support.
Upper Saddle River, NJ : Prentice Hall PTR, ©1998
Online version:
Poe, Vidette.
Building a data warehouse for decision support.
Upper Saddle River, NJ : Prentice Hall PTR, ©1998
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Vidette Poe; Patricia Klauer; Stephen Brobst
ISBN: 0137696396 9780137696390
OCLC Number: 38171972
Notes: Includes index.
Description: xli, 285 pages : illustrations ; 25 cm
Contents: Preface. Preface to the First Edition. Acknowledgments. About the Authors. Other Contributors to This Book. 1. Let's Start with the Basics. What is a Decision Support System? Understanding Operational versus Analytical Processing. Tips From the Trenches 1.1: Chargebacks and Internal Cost allocations. What is A Data Warehouse? Summary. Case Study 1: Ongoing Data Warehouse Development at Anthem Blue Cross & Blue Shield. 2. Understanding Terms and Technology. Analytical Processing. Operational Processing. Decision Support Systems. Data Warehouse. Data Mart. Environment for Data Access. Architecture. Technical Infrastructures. Source and Target Data. Levels of Users. Classes of Tools. Decision Support System (DSS) Applications. Data integration. Synonyms. Homonyms. Analogs. Data Transformation. Data Conversion Tools. Middleware Tools. Metadata. Star Schema. Hierarchies. Granularity. Database Gateway. Megabytes, Gigabytes, and Terabytes. Decision Support Development Cycle. Summary.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, Databases, or Files. The Data from the Source Systems Is Integrated before Being Loaded into the Data Warehouse. The Data Warehouse Is a Separate Read-Only Database Designed Specifically for Decision Support. Users Access the Data Warehouse via a Front End Tool or Application. Expanding the Generic Data Warehouse Architecture. Understanding the Relationship of Architecture and Infrastructures. Architecture and Infrastructures as a Separate Project. Tips From the Trenches 3.1: Architecture and Infrastructures. And the Answer is . . . Summary.4. Critical Success Factors. A Focus on Success. How Is a Data Warehouse Different? A Data Warehouse Incorporates Operational and Historical Data. Periodic Updates Rather than Real-Time. Service Levels for High Availability. Interactive Exploration of Information by Business End Users. Database Structures. Why would I Want a Data Warehouse? Total View of the Organization. The Past Is the Best Predictor of the Future. Single Version of Organizational Truth. Support for DSS without Impacting Operational Systems. Typical Application Deployments. Fraud Detection. Target Marketing. Profitability Analysis. Customer Retention. Inventory Management. Credit Risk Analysis. Long-Term Value Assessment. Pricing. Key Critical Success Factors. Focus on the Business, Not the Technology. Rapid Turnaround on Deliverables. End Users on the Implementation Team. Summary. Case Study 2: EPA Intranet Helps Policy Makers Protect the Environment.5. 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 Sourcing, Integration, and Mapping. Phase 5: Populating the Data Warehouse. Tips from the Trenches 5.1: Availabiliy of Data. Phase 6: Automating Data Load Procedures. Phase 7: Creating the Starter Set of Reports. Phase 8: Data Validation and Testing. Phase 9: Training. Phase 10: Rollout. Summary.6. 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 6.1: Choosing a Business Area. Ensuring a Successful Data Warehouse. Tips from the Trenches 6.2: Building a Successful Data Warehouse "The Big Eight". Be Clear on the Business Objective of Your Data Warehouse. 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 and the Users 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. Summary. Case Study 3: Moving through the Obstacles to Implementation. 7. Gathering Data Requirements. A Proper Mindset. User Interviews. The Purpose of Interviews. Setting up Successful Interviews. Who to Interview. Key End Users and Analysts from the Target Business Functions. Managers from the Target Business Functions. Analysts and Users from Related Business Functions. Managers from Related Business Functions. Executives. What to Ask End Users. Job Responsibilities. Current Analysis. What You Receive. What You Create. Ad Hoc Analysis. Business Analyses. Data Specific Information. Wish List Information. Other Data Sourcing Information. Business Hierarchies. What Have You Missed? What to Ask Executives. Documenting What You Heard. What You Have to Know for DSS. Facilitation Via Alignment. Developing the Data Model. Dimensional Business Model. Tips from the Trenches 7.1: The Basics of Data Modeling. Logical Data Model. Summary.8. Data Integration. Introduction. The metamorphosis of data information. Defining Data versus Transformation. Data Integration. Data Architecture. Tips from the Trenches 8.1: Data Architecture to Support Change. MetaData. The Data Integration Process. Data Sourcing. Data Consolidation. Understanding the Process of Data Consolidation. Analyze Source Data Documentation (Metadata). Tips from the Trenches 8.2: Source Documentation Quality. "Flatten Out" the Data into Logical Records. Tips from the Trenches 8.3: Flattening Files. Perform Domain Analysis. Tips from the Trenches 8.4: Get Representative Set of Data to Analyze. Determine the Primary Keys. Tips from the Trenches 8.5: Data Integration Resources. Determine Foreign Keys. Tips from the Trenches 8.6: Identifying Synonyms Across Source Files. Additional data analysis Needed for Data Consolidation. Subject Area Analysis. Synonyms, Homonyms, and Analogs. Analyzing Data to Integrate It Into An Existing Data Warehouse. Tips from the Trenches 8.7: Data Analysis and Integration as Ongoing Processes. Understanding Business Rules and Nuances of Meaning. Data-Driven Analysis. Tips from the Trenches 8.8: Documenting Business Rules. Data Conversion. Map Source File Attributes to the Data Warehouse Physical Data Structure. Map Source Attribute Allowable Values to Target Value. Specify Default Values. Write Conversion Specifications. Data Population. Tips from the Trenches 8.9: Data Integration Tools. Write Conversion Programs. Perform Testing. Determine Exception Processing. Collect Statistics. Perform Quality Assurance. Perform Stress Tests. Summary.9. Designing the Database for a Data Warehouse. 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. Simple Star Schemas. Tips from the Trenches 9.1: Understanding Facts and Dimensions. Multiple Fact Tables. Outboard Tables. Variations of a Star Schema. Multistar Schemas. A Salad Dressing Example. Understanding the Available Data, Browsing the Dimension Tables. Using Table Attributes. Creating Attribute Hierarchies. Aggregation. Denormalization. Limitations of The Star Schema. Data Warehouse Database Design Examples. Reservation Database. Investment Database. Health Insurance Database. Putting It All Together. Summary.10. 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. Viewing the Results. 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 (EISs). Tiered Architectures. 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. Summary. Case Study 4: Selecting a Front End to the Data Warehouse.11. Training, Support, and Rollout. 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. Summary.12. Metadata. Introduction. Using MetaData for Change Management. Metadata and Data Administration. Metadata Directory. Tips from the Trenches 12.1: A Cautionary Note. Metadata Administration in Practice. Change Management. Understanding Versions and Releases. Metrics for Change Management. Tips From the Trenches 12.2: The Skills of a Change Manager. A Realistic Appraisal of Metadata Management Within Corporations. Understanding Types of Metadata. Metadata for Data Integration. Metadata for Data Transformation. How to Approach a Short-Term Solution For Your Project. Other Uses of the Data Transformation Metadata. Using Data Transformation Metadata for Application Deployment. Summary.Appendix: Consulting Companies Assisting in the Development of this Book. Index.
Responsibility: Vidette Poe, Patricia Klauer, and Stephen Brobst.


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