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Business Intelligence, 2nd Edition

Book Description

Business Intelligence: The Savvy Managers Guide, Second Edition, discusses the objectives and practices for designing and deploying a business intelligence (BI) program. It looks at the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge.
Organized into 21 chapters, this book begins with an overview of the kind of knowledge that can be exposed and exploited through the use of BI. It then proceeds with a discussion of information use in the context of how value is created within an organization, how BI can improve the ways of doing business, and organizational preparedness for exploiting the results of a BI program. It also looks at some of the critical factors to be taken into account in the planning and execution of a successful BI program. In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata. Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics. Finally, emerging technologies such as text analytics and sentiment analysis are considered.
This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts.

  • Guides managers through developing, administering, or simply understanding business intelligence technology
  • Keeps pace with the changes in best practices, tools, methods and processes used to transform an organization’s data into actionable knowledge
  • Contains a handy, quick-reference to technologies and terminology.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Preface
    1. Introduction
    2. What This Book Is
    3. Why You Should Be Reading This Book
    4. Organization of the Book
    5. Our Approach to Knowledge Transfer
    6. Contact Me
    7. Acknowledgements
  6. Foreword
  7. Chapter 1. Business Intelligence and Information Exploitation
    1. Improving the Decision-Making Process
    2. Why a Business Intelligence Program?
    3. Taking Advantage of the Information Asset
    4. Business Intelligence and Program Success
    5. Business Intelligence Defined
    6. Actionable Intelligence
    7. The Analytics Spectrum
    8. Taming the Information Explosion
    9. Considerations
    10. Continuing Your Business Intelligence Education
    11. Endnotes
  8. Chapter 2. The Value of Business Intelligence
    1. Value Drivers and Information Use
    2. Performance Metrics and Key Performance Indicators
    3. Using Actionable Knowledge
    4. Horizontal Use Cases for Business Intelligence
    5. Vertical Use Cases for Business Intelligence
    6. Business Intelligence Adds Value
  9. Chapter 3. Planning for Success
    1. Introduction
    2. Organizational Preparedness for Business Intelligence and Analytics
    3. Initial Steps in Starting a Business Intelligence Program
    4. Bridging the Gaps Between Information Technology and the Business Users
    5. Knowing the Different Types of Business Intelligence Users
    6. Business Intelligence Success Factors: A Deeper Dive
    7. More on Building Your Team
    8. Strategic Versus Tactical Planning
    9. Summary
    10. Endnotes
  10. Chapter 4. Developing Your Business Intelligence Roadmap
    1. A Business Intelligence Strategy: Vision to Blueprint
    2. Review: The Business Intelligence and Analytics Spectrum
    3. The Business Intelligence Roadmap: Example Phasing
    4. Planning the Business Intelligence Plan
  11. Chapter 5. The Business Intelligence Environment
    1. Aspects of a Business Intelligence and Analytics Platform and Strategy
    2. The Organizational Business Intelligence Framework
    3. Services and System Evolution
    4. Management Issues
    5. Additional Considerations
  12. Chapter 6. Business Processes and Information Flow
    1. Analytical Information Needs and Information Flows
    2. Information Processing and Information Flow
    3. The Information Flow Model
    4. Practical Use
    5. Modeling Frameworks
    6. Management Issues
    7. Deeper Dives
  13. Chapter 7. Data Requirements Analysis
    1. Introduction
    2. Business Uses of Information
    3. Metrics: Facts, Qualifiers, and Models
    4. What is Data Requirements Analysis?
    5. Assessing Suitability
    6. Summary
  14. Chapter 8. Data Warehouses and the Technical Business Intelligence Architecture
    1. Introduction
    2. Data Modeling and Analytics
    3. The Data Warehouse
    4. Analytical Platforms
    5. Operational Data Stores
    6. Management
    7. Do You Really Need a Data Warehouse?
    8. Summary
  15. Chapter 9. Metadata
    1. What is Metadata?
    2. The Origin and Utility of Metadata
    3. Types of Metadata
    4. Semantic Metadata Processes for Business Analytics
    5. Further Considerations
    6. Using Metadata Tools
  16. Chapter 10. Data Profiling
    1. Establishing Usability of Candidate Data Sources
    2. Data Profiling Activities
    3. Data Model Inference
    4. Attribute Analysis
    5. Relationship Analysis
    6. Management Issues
    7. Summary
  17. Chapter 11. Business Rules
    1. The Value Proposition of Business Rules
    2. The Business Rules Approach
    3. The Definition of a Business Rule
    4. Business Rule Systems
    5. Sources of Business Rules
    6. Management Issues
    7. To Learn More
    8. Endnotes
  18. Chapter 12. Data Quality
    1. Good Decisions Rely on Quality Information
    2. The Virtuous Cycle of Data Quality
    3. Types of Data Flaws
    4. Business Impacts of Data Flaws
    5. Dimensions of Data Quality
    6. Data Quality Assessment
    7. Data Quality Rules
    8. Continuous Data Quality Monitoring and Improvement
    9. Considerations Regarding Data Quality for Business Analytics
    10. Data Cleansing
    11. Summary
  19. Chapter 13. Data Integration
    1. Improving Data Accessibility
    2. Extraction/Transformation/Loading
    3. Data Latency and Data Synchrony
    4. Data Replication and Change Data Capture
    5. Data Federation and Virtualization
    6. Data Integration and Cloud Computing
    7. Information Protection
    8. More on Merge/Purge and Record Consolidation
    9. Thoughts on Data Stewardship and Governance for Integration
  20. Chapter 14. High-Performance Business Intelligence
    1. The Need for Speed
    2. The Value of Parallelism
    3. Parallel Processing Systems
    4. Symmetric Multiprocessing
    5. Parallelism and Business Intelligence
    6. Performance Platforms and Analytical Appliances
    7. Data Layouts and Performance
    8. MapReduce and Hadoop
    9. Assessing Architectural Suitability for Application Performance
    10. Endnote
  21. Chapter 15. Deriving Insight from Collections of Data
    1. Introduction
    2. Customer Profiles and Customer Behavior
    3. Customer Lifetime Value
    4. Demographics, Psychographics, Geographics
    5. Geographic Data
    6. Behavior Analysis
    7. Consideration When Drawing Inferences
  22. Chapter 16. Creating Business Value through Location-Based Intelligence
    1. The Business Value of Location
    2. Demystifying Geography: Address Versus Location
    3. Geocoding and Geographic Enhancement
    4. Fundamentals of Location-Based Intelligence for Operational Uses
    5. Geographic Data Services
    6. Challenges and Considerations
    7. Where to Next?
  23. Chapter 17. Knowledge Discovery and Data Mining for Predictive Analytics
    1. Business Drivers
    2. Data Mining, Data Warehousing, Big Data
    3. The Virtuous Cycle
    4. Directed Versus Undirected Knowledge Discovery
    5. Six Basic Data Mining Activities
    6. Data Mining Techniques
    7. Technology Expectations
    8. Summary
  24. Chapter 18. Repurposing Publicly Available Data
    1. Using Publicly Available Data: Some Challenges
    2. Public Data
    3. Data Resources
    4. The Myth of Privacy
    5. Information Protection and Privacy Concerns
    6. Finding and Using Open Data Sets
  25. Chapter 19. Knowledge Delivery
    1. Review: The Business Intelligence User Types
    2. Standard Reports
    3. Interactive Analysis and Ad Hoc Querying
    4. Parameterized Reports and Self-Service Reporting
    5. Dimensional Analysis
    6. Alerts/Notifications
    7. Visualization: Charts, Graphs, Widgets
    8. Scorecards and Dashboards
    9. Geographic Visualization
    10. Integrated Analytics
    11. Considerations: Optimizing the Presentation for the Right Message
  26. Chapter 20. Emerging Business Intelligence Trends
    1. Search as a Business Intelligence Technique
    2. Text Analysis
    3. Entity Recognition and Entity Extraction
    4. Sentiment Analysis
    5. Mobile Business Intelligence
    6. Event Stream Processing
    7. Embedded Predictive Analytic Models
    8. Big Data Analytics
    9. Considerations
    10. Endnote
  27. Chapter 21. Quick Reference Guide
    1. Analytics Appliance
    2. Business Analytics
    3. Business Intelligence
    4. Business Rules
    5. Dashboards and Scorecards
    6. Data Cleansing
    7. Data Enhancement
    8. Data Governance
    9. Data Integration
    10. Data Mart
    11. Data Mining
    12. Data Modeling
    13. Data Profiling
    14. Data Quality
    15. Data Warehouse
    16. Dimensional Modeling
    17. ELT (Extract, Load, Transform)
    18. ETL (Extract, Transform, Load)
    19. Event Stream Processing
    20. Hadoop and MapReduce
    21. Location Intelligence and Geographic Analytics
    22. Metadata and Metadata Management
    23. Mobile Business Intelligence
    24. Online Analytical Processing (OLAP)
    25. Parallel and Distributed Computing
    26. Query and Reporting
    27. Endnotes
  28. Bibliography
  29. Index