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Business Intelligence Guidebook

Book Description

Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors’ tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers.

After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you’ll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success.

  • Provides practical guidelines for building successful BI, DW and data integration solutions.
  • Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language.
  • Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses
  • Describes best practices and pragmatic approaches so readers can put them into action.
  • Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Foreword
  6. How to Use This Book
  7. Acknowledgments
  8. Part I. Concepts and Context
    1. Chapter 1. The Business Demand for Data, Information, and Analytics
      1. Just One Word: Data
      2. Welcome to the Data Deluge
      3. Taming the Analytics Deluge
      4. Too Much Data, Too Little Information
      5. Data Capture versus Information Analysis
      6. The Five Cs of Data
      7. Common Terminology from our Perspective
  9. Part II. Business and Technical Needs
    1. Chapter 2. Justifying BI: Building the Business and Technical Case
      1. Why Justification is Needed
      2. Building the Business Case
      3. Building the Technical Case
      4. Assessing Readiness
      5. Creating a BI Road Map
      6. Developing Scope, Preliminary Plan, and Budget
      7. Obtaining Approval
      8. Common Justification Pitfalls
    2. Chapter 3. Defining Requirements—Business, Data and Quality
      1. The Purpose of Defining Requirements
      2. Goals
      3. Deliverables
      4. Roles
      5. Defining Requirements Workflow
      6. Interviewing
      7. Documenting Requirements
  10. Part III. Architectural Framework
    1. Chapter 4. Architecture Framework
      1. The Need for Architectural Blueprints
      2. Architectural Framework
      3. Information Architecture
      4. Data Architecture
      5. Technical Architecture
      6. Product Architecture
      7. Metadata
      8. Security and Privacy
      9. Avoiding Accidents with Architectural Planning
      10. Do Not Obsess over the Architecture
    2. Chapter 5. Information Architecture
      1. The Purpose of an Information Architecture
      2. Data Integration Framework
      3. DIF Information Architecture
      4. Operational BI versus Analytical BI
      5. Master Data Management
    3. Chapter 6. Data Architecture
      1. The Purpose of a Data Architecture
      2. History
      3. Data Architectural Choices
      4. Data Integration Workflow
      5. Data Workflow—Rise of EDW Again
      6. Operational Data Store
    4. Chapter 7. Technology & Product Architectures
      1. Where are the Product and Vendor Names?
      2. Evolution Not Revolution
      3. Technology Architecture
      4. Product and Technology Evaluations
  11. Part IV. Data Design
    1. Chapter 8. Foundational Data Modeling
      1. The Purpose of Data Modeling
      2. Definitions—The Difference Between a Data Model and Data Modeling
      3. Three Levels of Data Models
      4. Data Modeling Workflow
      5. Where Data Models Are Used
      6. Entity-Relationship (ER) Modeling Overview
      7. Normalization
      8. Limits and Purpose of Normalization
    2. Chapter 9. Dimensional Modeling
      1. Introduction to Dimensional Modeling
      2. High-Level View of a Dimensional Model
      3. Facts
      4. Dimensions
      5. Schemas
      6. Entity Relationship versus Dimensional Modeling
      7. Purpose of Dimensional Modeling
      8. Fact Tables
      9. Achieving Consistency
      10. Advanced Dimensions and Facts
      11. Dimensional Modeling Recap
    3. Chapter 10. Business Intelligence Dimensional Modeling
      1. Introduction
      2. Hierarchies
      3. Outrigger Tables
      4. Slowly Changing Dimensions
      5. Causal Dimension
      6. Multivalued Dimensions
      7. Junk Dimensions
      8. Value Band Reporting
      9. Heterogeneous Products
      10. Alternate Dimensions
      11. Too Few or Too Many Dimensions
  12. Part V. Data Integration Design
    1. Chapter 11. Data Integration Design and Development
      1. Getting Started with Data Integration
      2. Data Integration Architecture
      3. Data Integration Requirements
      4. Data Integration Design
      5. Data Integration Standards
      6. Loading Historical Data
      7. Data Integration Prototyping
      8. Data Integration Testing
    2. Chapter 12. Data Integration Processes
      1. Introduction: Manual Coding versus Tool-Based Data Integration
      2. Data Integration Services
  13. Part VI. Business Intelligence Design
    1. Chapter 13. Business Intelligence Applications
      1. BI Content Specifications
      2. Revise BI Applications List
      3. BI Personas
      4. BI Design Layout—Best Practices
      5. Data Design for Self-Service BI
      6. Matching Types of Analysis to Visualizations
    2. Chapter 14. BI Design and Development
      1. BI Design
      2. BI Development
      3. BI Application Testing
    3. Chapter 15. Advanced Analytics
      1. Advanced Analytics Overview and Background
      2. Predictive Analytics and Data Mining
      3. Analytical Sandboxes and Hubs
      4. Big Data Analytics
      5. Data Visualization
    4. Chapter 16. Data Shadow Systems
      1. The Data Shadow Problem
      2. Are There Data Shadow Systems in Your Organization?
      3. What Kind of Data Shadow Systems Do You Have?
      4. Data Shadow System Triage
      5. The Evolution of Data Shadow Systems in an Organization
      6. Damages Caused by Data Shadow Systems
      7. The Benefits of Data Shadow Systems
      8. Moving beyond Data Shadow Systems
      9. Misguided Attempts to Replace Data Shadow Systems
      10. Renovating Data Shadow Systems
  14. Part VII. Organization
    1. Chapter 17. People, Process and Politics
      1. The Technology Trap
      2. The Business and IT Relationship
      3. Roles and Responsibilities
      4. Building the BI Team
      5. Training
      6. Data Governance
    2. Chapter 18. Project Management
      1. The Role of Project Management
      2. Establishing a BI Program
      3. BI Assessment
      4. Work Breakdown Structure
      5. BI Architectural Plan
      6. BI Projects Are Different
      7. Project Methodologies
      8. BI Project Phases
      9. BI Project Schedule
    3. Chapter 19. Centers of Excellence
      1. The Purpose of Centers of Excellence
      2. BI COE
      3. Data Integration Center of Excellence
      4. Enabling a Data-Driven Enterprise
  15. Index