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A Manager's Guide to Data Warehousing

Book Description

Aimed at helping business and IT managers clearly communicate with each other, this helpful book addresses concerns straight-on and provides practical methods to building a collaborative data warehouse. You’ll get clear explanations of the goals and objectives of each stage of the data warehouse lifecycle while learning the roles that both business managers and technicians play at each stage. Discussions of the most critical decision points for success at each phase of the data warehouse lifecycle help you understand ways in which both business and IT management can make decisions that best meet unified objectives.

Table of Contents

  1. Copyright
  2. About the Author
  3. Credits
  4. Acknowledgments
  5. Introduction
    1. Overview of the Book
    2. How This Book Is Organized
    3. Who Should Read This Book
  6. I. The Essentials of Data Warehousing
    1. 1. Gaining Data Warehouse Success
      1. 1.1. The Essentials of Data Warehousing
        1. 1.1.1. What Is a Data Warehouse?
          1. 1.1.1.1. Differences Between Operational and DW Systems
          2. 1.1.1.2. The Data Warehousing Environment
          3. 1.1.1.3. What Is a Data Model?
          4. 1.1.1.4. Understanding Industry Perspectives
          5. 1.1.1.5. Design and Development Sequence
        2. 1.1.2. Why Build a Data Warehouse?
        3. 1.1.3. The Value of Data Warehousing
      2. 1.2. The Promises of Data Warehousing
        1. 1.2.1. Keys to Success
          1. 1.2.1.1. Developing and Maintaining Strong Business and Technology Partnerships
          2. 1.2.1.2. Identifying True Business Requirements
          3. 1.2.1.3. Shifting to a Global Perspective
          4. 1.2.1.4. Overcoming Unrealistic Expectations
          5. 1.2.1.5. Providing Clear Communication
          6. 1.2.1.6. Treating Data As a Corporate Asset
          7. 1.2.1.7. Effectively Leveraging Technology
        2. 1.2.2. Roadblocks to Success
          1. 1.2.2.1. Believing the Myth: "If You Build It, They Will Come"
          2. 1.2.2.2. Falling into the Project Deadline Trap
          3. 1.2.2.3. Failing to Uphold Organizational Discipline
          4. 1.2.2.4. Lacking Business Process Change
          5. 1.2.2.5. Narrowing the Focus Too Much
          6. 1.2.2.6. Resting on Your Laurels
          7. 1.2.2.7. Relying on the Technology Fix
          8. 1.2.2.8. Getting the Right People Involved
          9. 1.2.2.9. Finding Lost Institutional Knowledge
      3. 1.3. Summary
    2. 2. The Executive's FAQ for Data Warehousing
      1. 2.1. Question: What is the business benefit of a data warehouse?
        1. 2.1.1. Answer
      2. 2.2. Question: How much will it cost?
        1. 2.2.1. Answer
      3. 2.3. Question: How long will it take?
        1. 2.3.1. Answer
      4. 2.4. Question: How can I ensure success?
        1. 2.4.1. Answer
      5. 2.5. Question: Do other companies really build these in 90 days?
        1. 2.5.1. Answer
      6. 2.6. Question: How will we know we are doing this right?
        1. 2.6.1. Answer
      7. 2.7. Question: Why didn't this work last time? What is different this time?
        1. 2.7.1. Answer
      8. 2.8. Question: Do we have the right technology in place?
        1. 2.8.1. Answer
      9. 2.9. Question: Are we the only company with data warehouse problems?
        1. 2.9.1. Answer
      10. 2.10. Question: Will I get one version of the truth?
        1. 2.10.1. Answer
      11. 2.11. Question: Why can't we just use our current systems?
        1. 2.11.1. Answer
      12. 2.12. Question: Will the data warehouse replace our old systems?
      13. 2.13. Question: Who needs to be involved?
      14. 2.14. Question: Do we know where we are going? How will we know when we get there?
        1. 2.14.1. Answer
      15. 2.15. Question: How do we get started and stay focused?
        1. 2.15.1. Answer
      16. 2.16. Summary
  7. II. The Business Side of Data Warehousing
    1. 3. Understanding Where You Are and Finding Your Way
      1. 3.1. Assessing Your Current State
        1. 3.1.1. What Is Your Company's Strategic Direction?
        2. 3.1.2. What Are the Company's Top Initiatives?
        3. 3.1.3. How Healthy Is Your Data?
        4. 3.1.4. Does the Business Place Value on Analysis?
        5. 3.1.5. Reflecting on Your Data Warehouse History
      2. 3.2. Understanding Your Existing Reporting Environment
        1. 3.2.1. Finding the Reporting Systems
        2. 3.2.2. Compiling an Inventory
          1. 3.2.2.1. Identifying the Business Purpose
          2. 3.2.2.2. Discovering the Data You Already Have
          3. 3.2.2.3. Understanding the People
          4. 3.2.2.4. Tracking Technology and Tools
          5. 3.2.2.5. Understanding Enterprise Resources
        3. 3.2.3. Netting It All Out
      3. 3.3. Introducing the Case Studies
        1. 3.3.1. The Call Center Data Warehouse Project
        2. 3.3.2. In Real Life
          1. 3.3.2.1. Giant Company
          2. 3.3.2.2. Agile, Inc
      4. 3.4. Summary
    2. 4. Successful IT–Business Partnerships
      1. 4.1. What a Partnership Really Means
      2. 4.2. What the Business Partners Should Expect to Do
        1. 4.2.1. Business Executives and Senior Management
        2. 4.2.2. The Executive Business Sponsor
        3. 4.2.3. Business Managers
        4. 4.2.4. The Business Champion
        5. 4.2.5. Business Analysts
          1. 4.2.5.1. Helping the Business Analyst Deal with Change
        6. 4.2.6. Business User Audience
        7. 4.2.7. Project Manager
      3. 4.3. What You Should Expect from IT
        1. 4.3.1. CIO/IT Executive Sponsor
        2. 4.3.2. Data Warehouse Manager
        3. 4.3.3. Business Systems Analyst
        4. 4.3.4. Source System Analyst
        5. 4.3.5. Data Modeler/Data Architect
        6. 4.3.6. ETL Developer(s)
        7. 4.3.7. Business Intelligence Application Developer
        8. 4.3.8. Other Supporting Roles
      4. 4.4. Tips for Building and Sustaining a Partnership
        1. 4.4.1. Leveraging External Consulting
        2. 4.4.2. Building Strong Project Teams
      5. 4.5. Effective Communication
        1. 4.5.1. Netting Out Key Messages
        2. 4.5.2. Presenting in Business Terms
        3. 4.5.3. Meeting Preparation
          1. 4.5.3.1. Presentation Tips
        4. 4.5.4. When to Communicate
      6. 4.6. Partnerships Beyond a Project
        1. 4.6.1. The Decision-Making Process
          1. 4.6.1.1. Executive Steering Committee
          2. 4.6.1.2. DW Business Support Team
        2. 4.6.2. Enterprise Considerations
      7. 4.7. In Real Life
        1. 4.7.1. A Glimpse into Giant, Co
        2. 4.7.2. Insight from Agile, Inc
      8. 4.8. Summary
    3. 5. Setting Up a Successful Project
      1. 5.1. Defining the Project
        1. 5.1.1. Setting Up the Project Charter
        2. 5.1.2. Documenting Project Scope
        3. 5.1.3. Developing a Statement of Work
          1. 5.1.3.1. How Much Will It Cost?
        4. 5.1.4. Project Approval
      2. 5.2. Starting the Project
        1. 5.2.1. Launching the Project
      3. 5.3. Managing a Successful Project
        1. 5.3.1. Issue Tracking
        2. 5.3.2. Using Project Change Control
        3. 5.3.3. Discussing Change in Business Terms
        4. 5.3.4. Managing Expectations
      4. 5.4. In Real Life
        1. 5.4.1. Structured Projects with Giant
        2. 5.4.2. Freedom for Creativity at Agile, Inc
      5. 5.5. Summary
    4. 6. Providing Business Requirements
      1. 6.1. What Requirements Are Needed?
        1. 6.1.1. Peeling Back the Layers of Requirements Gathering
          1. 6.1.1.1. Who Provides Input?
          2. 6.1.1.2. Who Gathers the Requirements?
      2. 6.2. Providing Business Requirements
        1. 6.2.1. Strategic Requirements
        2. 6.2.2. Broad Business Requirements
        3. 6.2.3. Business Analyses
        4. 6.2.4. Business Data Requirements
          1. 6.2.4.1. Systems and Technical Requirements
      3. 6.3. Communicating What You Really Need
        1. 6.3.1. What Else Would Help the Project Team?
          1. 6.3.1.1. Data Integration Challenges
          2. 6.3.1.2. Assess Organizational Motivation
          3. 6.3.1.3. Complete Picture of the Data
        2. 6.3.2. What If No One Is Asking?
      4. 6.4. Practical Techniques for Gathering Requirements
        1. 6.4.1. Interview Session Characteristics
          1. 6.4.1.1. Individual Interviews
          2. 6.4.1.2. Group Interviews
          3. 6.4.1.3. Project Team Participation
          4. 6.4.1.4. Interview Tips
        2. 6.4.2. Who Needs to Be Included?
          1. 6.4.2.1. Setting a Good Example
        3. 6.4.3. Preparing for Interview Sessions
        4. 6.4.4. Conducting the Interview Sessions
          1. 6.4.4.1. Capturing Content: Notes vs. Tapes
          2. 6.4.4.2. Running the Interview
          3. 6.4.4.3. Concluding the Interview
      5. 6.5. Putting the Pieces Together
        1. 6.5.1. Individual Interview Documentation
          1. 6.5.1.1. Responsibilities
          2. 6.5.1.2. Business Themes
          3. 6.5.1.3. Business Data
        2. 6.5.2. Consolidated Requirements Documentation
          1. 6.5.2.1. Executive Summary
          2. 6.5.2.2. Consolidated Business Themes
          3. 6.5.2.3. Candidate Business Analyses
          4. 6.5.2.4. Consolidated Business Data Requirements
          5. 6.5.2.5. Identification of Non-Data Warehouse Requirements
        3. 6.5.3. Common Requirements Gathering Challenges
          1. 6.5.3.1. Sifting Through Reports
          2. 6.5.3.2. Listing Data Elements
          3. 6.5.3.3. Developing Functional Specifications
          4. 6.5.3.4. Moving Beyond Immediate
          5. 6.5.3.5. Lack of Requirements
          6. 6.5.3.6. The Cynic
      6. 6.6. Setting Attainable Goals
        1. 6.6.1. Exploring Alternatives
        2. 6.6.2. Setting Priorities
      7. 6.7. In Real Life
        1. 6.7.1. A Glimpse into Giant Company
        2. 6.7.2. Insight from Agile, Inc
      8. 6.8. Summary
  8. III. Dealing with the Data
    1. 7. Modeling the Data for your Business
      1. 7.1. The Purpose of Dimensional Models
        1. 7.1.1. Ease of Use
        2. 7.1.2. Query Performance
      2. 7.2. Understanding Your Data
      3. 7.3. What Is a Dimensional Model?
        1. 7.3.1. Dimensions
        2. 7.3.2. Facts
        3. 7.3.3. Using Both Parts of the Model
        4. 7.3.4. Implementing a Dimensional Model
        5. 7.3.5. Diagramming Your Dimensional Model
      4. 7.4. The Business Dimensional Model
        1. 7.4.1. Business Dimensions
        2. 7.4.2. Fact Groups
      5. 7.5. A Call Center Case Study
        1. 7.5.1. Call Center Dimensions
          1. 7.5.1.1. Date Dimension
          2. 7.5.1.2. Time Dimension
          3. 7.5.1.3. Customer Dimension
          4. 7.5.1.4. Employee Dimension
          5. 7.5.1.5. Call Dimension
          6. 7.5.1.6. Call Outcome Dimension
          7. 7.5.1.7. Employee Task Dimension
        2. 7.5.2. Call Center Fact Groups
          1. 7.5.2.1. Calls Fact Group
          2. 7.5.2.2. Call Center Time Tracking Fact Group
          3. 7.5.2.3. Call Forecast Fact Group
        3. 7.5.3. Working with the Model
        4. 7.5.4. Business Dimensional Model Index
      6. 7.6. Enterprise Considerations
        1. 7.6.1. Conformed Dimensions
        2. 7.6.2. Conformed Facts
        3. 7.6.3. Practical Guidelines
          1. 7.6.3.1. Guidelines for a Single Dimension
          2. 7.6.3.2. Guidelines for a Single Fact Group
          3. 7.6.3.3. Characteristics of the Model across the Enterprise
      7. 7.7. Business Participation in the Modeling Process
        1. 7.7.1. Creating the First Draft
        2. 7.7.2. Preparing for Modeling Sessions
          1. 7.7.2.1. Brainstorming the Framework
          2. 7.7.2.2. Drafting the Initial Dimensions
          3. 7.7.2.3. Drafting the Initial Fact Groups
          4. 7.7.2.4. Documenting the Model
          5. 7.7.2.5. Logging Questions and Issues
          6. 7.7.2.6. Building the Business Measures Worksheet
          7. 7.7.2.7. Preliminary Source to Target Data Map
        3. 7.7.3. Completing or Fleshing Out the Model
          1. 7.7.3.1. Working Through the Issues
          2. 7.7.3.2. Completing the Documentation
          3. 7.7.3.3. Working Through All the Data Elements
          4. 7.7.3.4. Refining the Model
        4. 7.7.4. Business Reviews of the Model
          1. 7.7.4.1. Small Business Reviews
          2. 7.7.4.2. When Are You Done?
          3. 7.7.4.3. Gaining Final Commitment
        5. 7.7.5. Expanding Business Data Over Time
          1. 7.7.5.1. Enhancing Dimensions
          2. 7.7.5.2. Adding More Fact Groups
      8. 7.8. Reflecting on Business Realities: Advanced Concepts
        1. 7.8.1. Supporting Multiple Perspectives: Multiple Hierarchies
        2. 7.8.2. Tracking Changes in the Dimension: Slowly Changing Dimensions
        3. 7.8.3. Depicting the Existence of a Relationship: Factless Fact Tables
        4. 7.8.4. Linking Parts of a Transaction: Degenerate Dimensions
        5. 7.8.5. Pulling Together Components: Junk Dimensions
        6. 7.8.6. Multiple Instances of a Dimension: Role Playing
        7. 7.8.7. Other Notation
          1. 7.8.7.1. Dimension Connectors
          2. 7.8.7.2. Clusters of Future Attributes
          3. 7.8.7.3. Notation Summary
      9. 7.9. Taking the Model Forward
        1. 7.9.1. Translating the Business Dimensional Model
          1. 7.9.1.1. Dimension Table Design
          2. 7.9.1.2. Translating Fact Groups
          3. 7.9.1.3. Physical Database Design
      10. 7.10. In Real Life
        1. 7.10.1. A Glimpse into Giant Co
        2. 7.10.2. Insight from Agile, Inc.
      11. 7.11. Summary
    2. 8. Managing Data As a Corporate Asset
      1. 8.1. What Is Information Management?
        1. 8.1.1. Information Management Example—Customer Data
        2. 8.1.2. IM Beyond the Data Warehouse
      2. 8.2. Master Data Management
        1. 8.2.1. Master Data Feeds the Data Warehouse
        2. 8.2.2. Finding the Right Resources
      3. 8.3. Data Governance
        1. 8.3.1. Data Ownership
          1. 8.3.1.1. Who Really Owns the Data?
          2. 8.3.1.2. Your Responsibilities If You Are "the Owner"
          3. 8.3.1.3. What are IT's Responsibilities?
          4. 8.3.1.4. Challenges with Data Ownership
        2. 8.3.2. Data Quality
          1. 8.3.2.1. Profiling the Data
          2. 8.3.2.2. How Clean Does the Data Really Need to Be?
          3. 8.3.2.3. Measuring Quality
          4. 8.3.2.4. Quality of Historical Data
          5. 8.3.2.5. Cleansing at the Source
          6. 8.3.2.6. Cleaning Up for Reporting
          7. 8.3.2.7. Managing the Integrity of Data Integration
          8. 8.3.2.8. Quality Improves When It Matters
          9. 8.3.2.9. Example: Data Quality and Grocery Checkout Scanners
          10. 8.3.2.10. Example: Data Quality and the Evaluation of Public Education
          11. 8.3.2.11. Realizing the Value of Data Quality
      4. 8.4. Implementing a Data Dictionary
        1. 8.4.1. The Data Dictionary Application
        2. 8.4.2. Populating the Data Dictionary
        3. 8.4.3. Accessing the Data Dictionary
        4. 8.4.4. Maintaining the Data Dictionary
      5. 8.5. Getting Started with Information Management
        1. 8.5.1. Understanding Your Current Data Environment
          1. 8.5.1.1. What Data Do You Have?
          2. 8.5.1.2. What Already Exists?
        2. 8.5.2. Where Do You Want to Be?
        3. 8.5.3. Develop a Realistic Strategy
          1. 8.5.3.1. Sharing the Information Management Strategy
        4. 8.5.4. Setting Up a Sustainable Process
          1. 8.5.4.1. Enterprise Commitment
          2. 8.5.4.2. The Data Governance Committee
          3. 8.5.4.3. Revising the Strategy
      6. 8.6. In Real Life
        1. 8.6.1. A Glimpse into Giant, Co
        2. 8.6.2. Insight from Agile, Inc
      7. 8.7. Summary
  9. IV. Building the Project
    1. 9. Architecture, Infrastructure, and Tools
      1. 9.1. What Is Architecture?
      2. 9.2. Why Do We Need Architecture?
        1. 9.2.1. Making Architecture Work
      3. 9.3. Data Architecture
        1. 9.3.1. Revisiting DW Goals
        2. 9.3.2. Components of DW Data Architecture
        3. 9.3.3. A Closer Look at Common Data Warehouse Architectures
          1. 9.3.3.1. Bottom-Up Data Architecture
            1. 9.3.3.1.1. Capture/Create the Data: Source Systems
            2. 9.3.3.1.2. Extract the Data
            3. 9.3.3.1.3. Prepare the Data: The ETL Data Stores
            4. 9.3.3.1.4. Publish the Data: The Presentation Server
            5. 9.3.3.1.5. Use the Data: Business Intelligence Applications
          2. 9.3.3.2. Top-Down Data Architecture
            1. 9.3.3.2.1. Capture/Create the Data
            2. 9.3.3.2.2. Extract the Data: The Staging Data Store
            3. 9.3.3.2.3. Prepare the Data: The Data Warehouse
          3. 9.3.3.3. Publish the Data: Data Marts
            1. 9.3.3.3.1. Use the Data: Business Intelligence Applications
          4. 9.3.3.4. Adopting an Architecture
            1. 9.3.3.4.1. Common Mistakes Adopting the Top-Down Approach
            2. 9.3.3.4.2. Common Mistakes Adopting the Bottom-Up Approach
            3. 9.3.3.4.3. Success Factors for Both Approaches
      4. 9.4. Technical Architecture
        1. 9.4.1. Technical Architecture Basics
        2. 9.4.2. Components of Technical Architecture
        3. 9.4.3. Infrastructure
        4. 9.4.4. Technical Architecture in Action
          1. 9.4.4.1. What You Need to Know about Technical Architecture
      5. 9.5. Navigating the Technology Jungle
        1. 9.5.1. Weighing Technology Options
          1. 9.5.1.1. Best of Breed
          2. 9.5.1.2. End-to-End Solutions
          3. 9.5.1.3. Deciding Not to Buy a Tool
        2. 9.5.2. Finding the Right Products
          1. 9.5.2.1. Requests for Information or Proposals
          2. 9.5.2.2. Business Participation in the Selection Process
          3. 9.5.2.3. Understanding Product Genealogy
        3. 9.5.3. Understanding Value and Evaluating Your Options
          1. 9.5.3.1. Cutting through the Marketing Hype
        4. 9.5.4. The Value of References
      6. 9.6. Making Architecture Work for You
        1. 9.6.1. Just-In-Time Architecture
      7. 9.7. In Real Life
        1. 9.7.1. Architecture at Giant
        2. 9.7.2. Agile Ignores the Need for Architecture
      8. 9.8. Summary
    2. 10. Implementation: Building the Database
      1. 10.1. Extract, Transform, and Load (ETL) Fundamentals
        1. 10.1.1. What Work Is Being Done?
        2. 10.1.2. ETL System Functionality
          1. 10.1.2.1. Extraction
          2. 10.1.2.2. Transformation
          3. 10.1.2.3. Load
      2. 10.2. The Business Role in ETL
        1. 10.2.1. Why Does the Business Need to Help?
        2. 10.2.2. Defining Business Rules
        3. 10.2.3. Defining Expected Results–The Test Plan
        4. 10.2.4. Development Support
        5. 10.2.5. Testing the ETL System–Is the Data Right?
        6. 10.2.6. Why Does It Take So Long and Cost So Much?
      3. 10.3. Balancing Requirements and Data Reality
        1. 10.3.1. Discovering the Flaws in Your Current Systems
        2. 10.3.2. Applying New Business Rules
        3. 10.3.3. Working Toward Long-Term Solutions
        4. 10.3.4. Manually Including Business Data
      4. 10.4. Tracking Progress–Are We There Yet?
      5. 10.5. What Else Can You Do to Help?
        1. 10.5.1. Encouragement and Support
        2. 10.5.2. Ensuring Continued Business Participation
        3. 10.5.3. Proactive Communication
      6. 10.6. In Real Life
        1. 10.6.1. Building the Data Warehouse at Giant, Co.
        2. 10.6.2. Agile, Inc., Builds a Data Warehouse Quickly
      7. 10.7. Summary
    3. 11. Data Delivery: What you Finally See
      1. 11.1. What Is Business Intelligence?
        1. 11.1.1. Business Intelligence without a DW
        2. 11.1.2. BI in Action
          1. 11.1.2.1. Tabular Reports
          2. 11.1.2.2. Parameter-Driven Reports
          3. 11.1.2.3. Interactive Reports–Drilling Down and Across
          4. 11.1.2.4. Exception Reports
          5. 11.1.2.5. Other BI Capabilities
          6. 11.1.2.6. Complex Analysis
      2. 11.2. BI Building Blocks
        1. 11.2.1. Data Content–Understanding What You Have
        2. 11.2.2. Navigation–Finding What You Need
        3. 11.2.3. Presentation–How Do You Want to See Results?
        4. 11.2.4. Delivery–How Do You Receive the Results?
      3. 11.3. Supporting Different Levels of Use
      4. 11.4. Construction of the BI Solution
        1. 11.4.1. Planning for Business Change
        2. 11.4.2. Design–What Needs to Be Delivered?
        3. 11.4.3. Development
        4. 11.4.4. Testing BI Applications and Validating Data
        5. 11.4.5. Additional Responsibilities
          1. 11.4.5.1. Security–Who Can Look at the Data?
          2. 11.4.5.2. System Controls–Who Can Change What?
      5. 11.5. Planning a Successful Launch
        1. 11.5.1. Marketing the Solution
        2. 11.5.2. Learning to Use the Data without a Technical Degree
          1. 11.5.2.1. Learning about the Data
          2. 11.5.2.2. Learning about the BI Tool/Application
        3. 11.5.3. Ensuring That the Right Help Is Available
      6. 11.6. In Real Life
        1. 11.6.1. BI at Giant Company
        2. 11.6.2. Agile, Inc. Dives into BI
      7. 11.7. Summary
  10. V. Next Steps–Expanding on Success
    1. 12. Managing the Production Data Warehouse
      1. 12.1. Finishing the Project
        1. 12.1.1. Recapping the BI Application Launch
        2. 12.1.2. Post-Implementation Review
          1. 12.1.2.1. Looking Back—Did you Accomplish Your Objectives?
      2. 12.2. Adopting the Solution
        1. 12.2.1. Tracking Data Warehouse Use
        2. 12.2.2. Getting the Rest of the Business Community on Board
        3. 12.2.3. Business Process Change
          1. 12.2.3.1. Changing How Data Is Used
          2. 12.2.3.2. Streamlining Business Processes
          3. 12.2.3.3. Encouraging Change
      3. 12.3. The Production Data Warehouse
        1. 12.3.1. Staffing Production Activities
        2. 12.3.2. Maintaining the Environment
          1. 12.3.2.1. Keeping Up with Technology
            1. 12.3.2.1.1. The Importance of Testing
            2. 12.3.2.1.2. Falling Behind
          2. 12.3.2.2. Monitoring Performance and Capacity Planning
        3. 12.3.3. Maintaining the Data Warehouse
          1. 12.3.3.1. Maintaining the ETL System
          2. 12.3.3.2. Maintaining the BI Application
        4. 12.3.4. Tracking Questions and Problems
        5. 12.3.5. Fixing Bugs
      4. 12.4. When the Data Warehouse Falls Short
        1. 12.4.1. Common Causes for a Stalled Warehouse
        2. 12.4.2. Jump-Starting a Stalled Data Warehouse
          1. 12.4.2.1. Conducting an Assessment
          2. 12.4.2.2. Determining What Can Be Salvaged
          3. 12.4.2.3. Developing a Plan to Move On
          4. 12.4.2.4. Aligning DW Objectives with Business Goals
          5. 12.4.2.5. Getting It Right This Time
        3. 12.4.3. Launching the Improved Data Warehouse and BI Solution
      5. 12.5. In Real Life
        1. 12.5.1. Lack of Support for the Production DW at Giant Co.
        2. 12.5.2. Unleashing BI at Agile, Inc
      6. 12.6. Summary
    2. 13. Achieving Long-Term Success
      1. 13.1. Planning for Expansion and Growth
        1. 13.1.1. Exploring Expansion Opportunities
        2. 13.1.2. Prioritization of Feedback
      2. 13.2. Managing Enterprise DW Resources
        1. 13.2.1. Creating an Enterprise Data Warehouse Team
          1. 13.2.1.1. The Centralized Enterprise Data Warehouse Team
          2. 13.2.1.2. The Virtual Enterprise Data Warehouse Team
        2. 13.2.2. Enterprise DW Team Responsibilities
        3. 13.2.3. Funding the Enterprise DW Team
      3. 13.3. Pushing into the Future
        1. 13.3.1. Embedded Business Intelligence
        2. 13.3.2. Operational Business Intelligence
        3. 13.3.3. Real-Time Data Warehousing
        4. 13.3.4. Unstructured Data
        5. 13.3.5. Monitoring Industry Innovation
      4. 13.4. Moving Toward Business Value
        1. 13.4.1. Measuring Success One Step at a Time
        2. 13.4.2. Adjusting Expectations to Reality
        3. 13.4.3. Keeping the Momentum Going
        4. 13.4.4. Celebrating Progress
        5. 13.4.5. Success Can Be Attained
      5. 13.5. Conclusion
  11. Glossary