O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Business Intelligence

Business Intelligence

by Stacia Misner, Michael Luckevich, Elizabeth Vitt
Publisher: Microsoft Press
Release Date: December 2008
ISBN: 9780735626607
View table of contents
Start reading

Book Description

"This readable, practical book helps business people quickly understand what business intelligence is, how it works, where it's used, and why and when to use it-all illustrated by real case studies, not just theory." Nigel Pendse Author of The OLAP Report www.olapreport.com So much information, so little time. All too often, business data is hard to get at and use-thus slowing decision-making to a crawl. This insightful book illustrates how organizations can make better, faster decisions about their customers, partners, and operations by turning mountains of data into valuable business information that's always at the fingertips of decision makers. You'll learn what's involved in using business intelligence to bring together information, people, and technology to create successful business strategies-and how to execute those strategies with confidence. Topics covered include: • THE BUSINESS INTELLIGENCE MINDSET: Discover the basics behind business intelligence, such as how it's defined, why and how to use it in your organization, and what characteristics, components, and general architecture most business intelligence solutions share. • THE CASE FOR BUSINESS INTELLIGENCE: Read how world leaders in finance, manufacturing, and retail have successfully implemented business intelligence solutions and see what benefits they have reaped. • THE PRACTICE OF BUSINESS INTELLIGENCE: Find out what's involved in implementing a business intelligence solution in your organization, including how to identify your business intelligence opportunities, what decisions you must make to get a business intelligence project going, and what to do to sustain the momentum so that you can continue to make sense of all the data you gather.

Table of Contents

  1. Business Intelligence
  2. Foreword
  3. Introduction
    1. Our Assumptions About You
    2. Organization of the Book
    3. Acknowledgments
  4. I. Business Intelligence Foundations
    1. Prologue: The Mystery on Lovesick Lake
    2. 1. Understanding Business Intelligence
      1. Describing Business Intelligence
        1. Making Better Decisions Faster
        2. Converting Data into Information
        3. Using a Rational Approach to Management
      2. Defining the BI Cycle
        1. Analysis
        2. Insight
        3. Action
        4. Measurement
      3. Enabling Business Intelligence
        1. Technology
        2. Processing Power
        3. Data Volumes
        4. Network Technologies
        5. Standards
        6. BI Software
        7. People
        8. Culture
      4. Summary
    3. 2. Bridging the Analysis Gap
      1. Multidimensional Analysis
      2. Operational Systems
        1. OLTP Systems
        2. Operational Reporting
      3. Business Intelligence Systems
        1. Why Online Analytical Processing?
        2. OLAP System Structures
          1. Dimensions for Slice and Dice
          2. Hierarchies for Drill Down
          3. What Are You Measuring?
          4. OLAP Storage Modes
      4. Summary
    4. 3. Defining BI Technologies
      1. The High-Level View
        1. Where Data Is Stored
        2. How Data Gets to Business Users
      2. Reporting and Analysis
        1. Making Data Easy to Analyze
        2. Defining User Communities
          1. The Information User
          2. The Information Consumer
          3. The Power Analyst
        3. Using Front-End Tools
        4. Applying Data Mining
      3. The Data Warehouse
        1. Common Characteristics
          1. Subject Oriented
          2. Consistent Data
          3. Cleansed Data
          4. Historical Data
          5. Fast Delivery of Data
      4. Summary
  5. II. Business Intelligence Case Studies
    1. 4. Improving Operational Efficiency—Audi AG
      1. Company Background
      2. Business Requirements
      3. The Solution
      4. Solution Benefits
      5. Future Plans
      6. Summary
    2. 5. Maximizing Profitability—The Frank Russell Company
      1. Company Background
      2. Business Requirements
      3. The Picasso Solution
        1. Solution Benefits
      4. The Einstein Solution: Building on Success
        1. Solution Benefits
      5. Summary
    3. 6. Impacting the Bottom Line—CompUSA Inc.
      1. Company Background
      2. Business Requirements
      3. The Solution
      4. Solution Benefits
        1. Improved Sales Performance
        2. Improved Data Quality
        3. Fraud and Loss Prevention
        4. Improved Productivity
      5. Project Challenges
        1. Data Volumes
        2. Data Integration
        3. Data Quality
      6. Lessons Learned
      7. Future Plans
      8. Summary
    4. 7. Keeping Customers Loyal—Disco S.A.
      1. Company Background
      2. Business Requirements
      3. The Solution
      4. Solution Benefits
      5. Project Challenges
      6. Summary
    5. 8. Managing Seasonal Variability—Cascade Designs
      1. Company Background
      2. Business Requirements
      3. The Solution
      4. Solution Benefits
      5. Lessons Learned
      6. Future Plans
      7. Summary
  6. III. A Business Intelligence Roadmap
    1. 9. Identifying BI Opportunities
      1. Doing Your Homework
        1. Where Will Business Intelligence Be Used?
        2. Who Will Use the Application?
        3. What Information Do You Need?
          1. Define the Measures
          2. Define the Dimensions
          3. Define the Level of Detail
      2. Sharing and Collecting Ideas
        1. Arrange a Brainstorming Session
        2. Define the Brainstorming Team
        3. Ask Business Questions
          1. Identify Information Requirements
        4. Organize the Information Requirements
      3. Evaluating Alternatives
        1. Group Requirements into Opportunity Areas
        2. Grade Opportunities by Importance
          1. Actionability of Information
          2. Materiality of the Impact
          3. Tactical vs. Strategic Focus
          4. Applying the Importance Criteria
        3. Grade Opportunities by Difficulty
          1. Cross-Functionality of Design
          2. Existence and Accessibility of Data
          3. Complexity of Calculations
          4. Applying the Difficulty Criteria
        4. Rank Opportunities
          1. Creating a BI Opportunity Scorecard
          2. Costs, Benefits, and Returns
      4. Summary
    2. 10. Implementing a BI Solution
      1. An Implementation Strategy
        1. Think Big and Start Small
        2. Pay Special Attention to Your First Step
        3. Assemble the Puzzle Pieces Early
        4. Use Well-Defined Projects
          1. Proof-of-Concept Project
          2. Pilot Project
          3. The Full-Scale Project
        5. Leverage Each Success Again and Again
      2. The Fundamental Decisions
        1. The Implementation Team
          1. Executive Sponsor
          2. Business Team
          3. Technical Team
          4. Data Modelers
          5. Project Manager
          6. Internal vs. External Resources
        2. BI Technologies
          1. Overall Evaluation Criteria
          2. Front-End Reporting and Analysis Tools
          3. OLAP and Relational Databases
          4. ETL Tools
          5. BI Accelerators
        3. Dimension Design
          1. Levels
          2. Hierarchies
          3. Attributes
          4. Changes
        4. Measure Design
          1. Calculations
          2. Aggregation Method
          3. Refresh and History
        5. Source Data
          1. Identifying Data Sources
          2. Evaluating Data Sources
          3. Handling Data Issues
      3. Summary
  7. A. Conclusion
  8. B. Microsoft Data Warehousing Framework
    1. SQL Server
      1. SQL Server Relational Database Engine
      2. Data Transformation Services
      3. Analysis Services
      4. English Query
      5. Meta Data Services
    2. Data Analyzer
    3. Microsoft Business Intelligence Accelerator
    4. Data Warehousing Alliance
  9. Glossary
  10. C. Bibliography
  11. Index
  12. About the Authors
  13. Copyright