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Business intelligence guidebook : from data integration to analytics /
This book arms you with the knowledge you need to design rock-solid business intelligence and data integration processes. 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-make...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Morgan Kaufman,
[2015]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 003 | OCoLC | ||
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| 100 | 1 | |a Sherman, Rick, |e author. | |
| 245 | 1 | 0 | |a Business intelligence guidebook : |b from data integration to analytics / |c Rick Sherman. |
| 264 | 1 | |a Waltham, MA : |b Morgan Kaufman, |c [2015] | |
| 264 | 4 | |c Ã2015 | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed December 3, 2014). | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a This book arms you with the knowledge you need to design rock-solid business intelligence and data integration processes. 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. It 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. -- |c Edited summary from book. | ||
| 505 | 0 | |a Intro -- Cover image -- Title page -- Table of Contents -- Copyright -- 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. | |
| 505 | 8 | |a 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 -- Revise BI Applications List -- BI Personas -- BI Design Layout-Best Practices -- Data Design for Self-Service BI -- Matching Types of Analysis to Visualizations. | |
| 505 | 8 | |a BI Content Specifications -- 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. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Business intelligence |v Guidebooks. | |
| 650 | 0 | |a Business intelligence |x Data processing. | |
| 650 | 0 | |a Business intelligence |x Evaluation. | |
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| 655 | 7 | |a Guidebooks |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Sherman, Rick. |t Business intelligence guidebook. |d Waltham, MA : Morgan Kaufmann is an imprint of Elsevier, [2015] |z 9780124114616 |w (DLC) 2014031205 |
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