Modern enterprise business intelligence and data management : a roadmap for IT directors, managers, and architects /

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Alan Simon addresses the triad of technology, work processes, and organizational/human factors considerations in a manner that blends the visionary and the pragmatic. This book takes a fresh look at true enterprise-scale BI/DW in the "Dawn of the Big Data Era"; details a checklist-based ap...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Simon, Alan, 1958- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Morgan Kaufmann, 2014.
Temas:
Business intelligence.
Data warehousing.
Big data.
Commerce.
Commerce
Entrepôts de données (Informatique)
Données volumineuses.
COMPUTERS > General.
Big data
Business intelligence
Data warehousing
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover; Title Page; Copyright Page; Table of contents; Preface; Terminology; Defining "Enterprise"; Defining "Data"; Defining "Enterprise Data Management"; Defining "Business Intelligence"; About the author; Chapter 1
  • The Rebirth of Enterprise Data Management; 1.1
  • In the beginning: how we got to where we are today; 1.1.1
  • 1960s and 1970s; 1.1.2
  • 1980s; 1.1.2.1
  • Enterprise Data Models; 1.1.2.2
  • Distributed Database Management Systems (DDBMSs); 1.1.3
  • 1990s; 1.1.3.1
  • Data Warehousing; 1.1.3.2
  • Read-Only Distributed Data Access; 1.1.4
  • 2000s; 1.1.5
  • Today.
  • 1.2
  • A manifesto for modern enterprise data management: what are we trying to accomplish?1.2.1
  • Bringing Order to an Organization's Data, Reporting, and Analytics; 1.2.2
  • Supporting Emerging Technologies and New or Enhanced Applications; 1.2.3
  • Turning "Data is our Lifeblood" and "The Data-Driven Organization" into More than Just Slogans; 1.2.4
  • Aligning Our Approach and Architecture with Our Organizational Structure and Culture; 1.3
  • Chapter summary; Chapter 2
  • Assessing Your Organization's Current State of Enterprise Data Management; 2.1
  • Introduction.
  • 2.2
  • A rapid, consensus-driven starting point to current state assessment2.2.1
  • Step 1: Determining the Scope and Scale of the Enterprise; 2.2.2
  • Step 2: Complete a 4-by-4 Assessment Scorecard; 2.2.2.1
  • Complexity Index; 2.2.2.2
  • Quality Index; 2.2.2.3
  • Support Index; 2.2.2.4
  • Tension Index; 2.3
  • Category 1: operational reporting and querying; 2.4
  • Category 2: strategic insights; 2.5
  • Category 3: data architecture; 2.6
  • Category 4: work processes and human/organizational factors; 2.7
  • Building and grading the 4-by-4 scorecard; 2.8
  • Interpreting the meaning of the results.
  • 2.9
  • Chapter summaryReferences; Chapter 3
  • Identifying and Cataloguing Key Business Imperatives; 3.1
  • Introduction; 3.2
  • Cross-brand, cross-geography strategic sourcing; 3.3
  • Lean manufacturing; 3.4
  • "Mega-processes"; 3.5
  • Heightened risk mitigation and management; 3.6
  • Enterprise systems initiatives; 3.6.1
  • New ERP Implementation; 3.6.2
  • Enterprise Systems Migration; 3.6.3
  • Enterprise Systems Rationalization and Consolidation; 3.7
  • Enterprise-level business quality initiatives; 3.8
  • Chapter summary; References.
  • Chapter 4
  • Surveying Relevant Enterprise Data Management Technologies4.1
  • Introduction; 4.2
  • Databases and data storage; 4.3
  • Database administration and maintenance; 4.4
  • Data virtualization; 4.5
  • Master data management; 4.6
  • Metadata management; 4.7
  • Data quality and profiling; 4.8
  • Data governance; 4.9
  • Data interchange and movement; 4.10
  • Data retrieval, preparation, and delivery (business intelligence, reporting, and analytics); 4.11
  • Other core and enabling technologies; 4.12
  • Staying on top of proliferating technologies; References.
  • Chapter 5
  • Building an Enterprise Data Management and Business Intelligence Roadmap.

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