000 | 05429cam a2200205 i 4500 | ||
---|---|---|---|
005 | 20230814134930.0 | ||
008 | 141006s2015 ne b 001 0 eng | ||
010 | _a 2014031205 | ||
020 |
_a9780124114616 _c4692.73 |
||
040 | _aS.X.U.K | ||
041 | _aEnglish | ||
082 | 0 | 0 | _aR 658.472 SHE(BUS) |
100 | 1 | _aSherman, Rick. | |
245 | 1 | 0 |
_aBusiness intelligence guidebook : _bfrom data integration to analytics / _cRick Sherman. |
260 |
_aWyman Street _aWalthaman _bMorgan Kaufmann _bElsevier _cc2015 |
||
300 |
_axxiii, 525 pages ; _c24 cm _bP.B. |
||
500 | _aTable of contents Foreword How to Use This Book Acknowledgments Part I. Concepts and Context Chapter 1. The Business Demand for Data, Information, and Analytics Just One Word: Data Welcome to the Data Deluge Taming the Analytics Deluge Too Much Data, Too Little Information Data Capture versus Information Analysis The Five Cs of Data Common Terminology from our Perspective Part II. Business and Technical Needs Chapter 2. Justifying BI: Building the Business and Technical Case Why Justification is Needed Building the Business Case Building the Technical Case Assessing Readiness Creating a BI Road Map Developing Scope, Preliminary Plan, and Budget Obtaining Approval Common Justification Pitfalls Chapter 3. Defining Requirements—Business, Data and Quality The Purpose of Defining Requirements Goals Deliverables Roles Defining Requirements Workflow Interviewing Documenting Requirements Part III. Architectural Framework Chapter 4. Architecture Framework The Need for Architectural Blueprints Architectural Framework Information Architecture Data Architecture Technical Architecture Product Architecture Metadata Security and Privacy Avoiding Accidents with Architectural Planning Do Not Obsess over the Architecture Chapter 5. Information Architecture The Purpose of an Information Architecture Data Integration Framework DIF Information Architecture Operational BI versus Analytical BI Master Data Management Chapter 6. Data Architecture The Purpose of a Data Architecture History Data Architectural Choices Data Integration Workflow Data Workflow—Rise of EDW Again Operational Data Store Chapter 7. Technology & Product Architectures Where are the Product and Vendor Names? Evolution Not Revolution Technology Architecture Product and Technology Evaluations Part IV. Data Design Chapter 8. Foundational Data Modeling The Purpose of Data Modeling Definitions—The Difference Between a Data Model and Data Modeling Three Levels of Data Models Data Modeling Workflow Where Data Models Are Used Entity-Relationship (ER) Modeling Overview Normalization Limits and Purpose of Normalization Chapter 9. Dimensional Modeling Introduction to Dimensional Modeling High-Level View of a Dimensional Model Facts Dimensions Schemas Entity Relationship versus Dimensional Modeling Purpose of Dimensional Modeling Fact Tables Achieving Consistency Advanced Dimensions and Facts Dimensional Modeling Recap Chapter 10. Business Intelligence Dimensional Modeling Introduction Hierarchies Outrigger Tables Slowly Changing Dimensions Causal Dimension Multivalued Dimensions Junk Dimensions Value Band Reporting Heterogeneous Products Alternate Dimensions Too Few or Too Many Dimensions Part V. Data Integration Design Chapter 11. Data Integration Design and Development Getting Started with Data Integration Data Integration Architecture Data Integration Requirements Data Integration Design Data Integration Standards Loading Historical Data Data Integration Prototyping Data Integration Testing Chapter 12. Data Integration Processes Introduction: Manual Coding versus Tool-Based Data Integration Data Integration Services Part VI. Business Intelligence Design Chapter 13. Business Intelligence Applications BI Content Specifications Revise BI Applications List BI Personas BI Design Layout—Best Practices Data Design for Self-Service BI Matching Types of Analysis to Visualizations Chapter 14. BI Design and Development BI Design BI Development BI Application Testing Chapter 15. Advanced Analytics Advanced Analytics Overview and Background Predictive Analytics and Data Mining Analytical Sandboxes and Hubs Big Data Analytics Data Visualization Chapter 16. Data Shadow Systems The Data Shadow Problem Are There Data Shadow Systems in Your Organization? What Kind of Data Shadow Systems Do You Have? Data Shadow System Triage The Evolution of Data Shadow Systems in an Organization Damages Caused by Data Shadow Systems The Benefits of Data Shadow Systems Moving beyond Data Shadow Systems Misguided Attempts to Replace Data Shadow Systems Renovating Data Shadow Systems Part VII. Organization Chapter 17. People, Process and Politics The Technology Trap The Business and IT Relationship Roles and Responsibilities Building the BI Team Training Data Governance Chapter 18. Project Management The Role of Project Management Establishing a BI Program BI Assessment Work Breakdown Structure BI Architectural Plan BI Projects Are Different Project Methodologies BI Project Phases BI Project Schedule Chapter 19. Centers of Excellence The Purpose of Centers of Excellence BI COE Data Integration Center of Excellence Enabling a Data-Driven Enterprise Index | ||
650 | 0 | _aBusiness intelligence. | |
942 | _cUCS | ||
999 |
_c10099 _d10099 |