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CITATION

Laberge, Robert (Bob). The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights. US: McGraw-Hill Education, 2011.

The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights

Authors: Robert (Bob) Laberge

Published:  2011

ISBN: 9780071745321 0071745327
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  • Contents
  • Acknowledgments
  • Introduction
  • Part I Preparation
  • Chapter 1 Data Warehouse and Business Intelligence Overview
  • Business Intelligence Overview
  • Definition
  • Value of Business Intelligence
  • Breakdown of Business and Intelligence
  • Business Intelligence Success Factors
  • Purpose of BI
  • BI User Presentation
  • BI Tool and Architecture
  • Advancements Due to Globalization
  • Data Warehouse Overview
  • Definition
  • Data Warehouse System
  • Data Warehouse Architecture
  • Data Flow Terminology
  • Data Warehouse Purpose
  • Data Structure Strategy
  • Data Warehouse Business
  • Frequently Asked Questions
  • Current Systems Good Enough?
  • What Is the Value of a Data Warehouse?
  • How Much Will It Cost?
  • How Long Will It Take?
  • What Will Make Us Successful?
  • Chapter 2 Data in the Organization
  • Corporate Asset
  • Data in Context
  • Data Quality
  • Data Vocabulary
  • Data Components
  • Organizing the Data
  • Structuring the Data
  • Data Models
  • Data Architecture
  • Competitive Advantage
  • Data Model Build or Buy
  • Mentoring the Business
  • Chapter 3 Reasons for Building
  • Platform Migration
  • Business Continuity
  • Reverse Engineering
  • Data Quality
  • Parallel Environments
  • Added Value
  • Data Warehouse Centralization
  • Corporate Merger
  • In-house Merging
  • Central Design and Local Usage
  • Data Mart Consolidation
  • New Initiative
  • New Initiative: Dynamic Reporting
  • “Just Build It”
  • Data Floundation
  • Reasons for NOT Building a Data Warehouse
  • Poor Data Quality
  • Lack of Business Interest
  • Lack of Sponsorship
  • Unclear Focus
  • Sufficiency of Current Systems
  • Lack of Resources
  • Unstable Environment
  • Too Costly
  • Poor Management
  • Chapter 4 Data Warehouse and Business Intelligence Strategy
  • Business Intelligence Strategy
  • Business Purpose
  • Business Usage
  • Architecture Overview
  • Data Warehouse Strategy
  • Usage
  • DW Architecture
  • Focus and Success
  • Enterprise or Line of Business?
  • Goal Focused
  • Success: When Are We Done?
  • Where to Start?
  • For BI
  • For DW
  • How to Start?
  • For BI
  • For DW
  • Project Phasing
  • How Long Will It Take, Revisited
  • Points of Interest
  • Typical Failure Reasons
  • Basic Values
  • Chapter 5 Project Resources: Roles and Insights
  • Key Observations
  • Project Teams
  • Senior Expertise
  • Leadership
  • Project Sponsor
  • Data Warehouse Executive
  • Team Structure
  • Executive Sponsorship
  • Data Stewards
  • Basic Resources
  • Periodic Reviews: Progress Audit
  • Center of Competence
  • Chapter 6 Write-It-Up Overview
  • Project Charter
  • Project Scope
  • Statement of Work (SOW)
  • Part II Components
  • Chapter 7 Business Intelligence: Data Marts and Usage
  • Why Model the Data?
  • Types of Data Models
  • Design of Data
  • Fact Tables
  • Types of Facts
  • Types of Fact Tables
  • Source of Measures
  • Fact Table Key
  • Grain of Fact Table
  • Fact Table Density
  • Factless Fact Table
  • Dimensions
  • Dimension or Measure
  • History and Dates
  • Dimension Table Key
  • Grain of Dimension
  • Source and Value of Dimension Attributes
  • Types of Dimensions
  • Hierarchies and Helper Tables
  • Profile Tables
  • Number of Dimensions
  • Sizing
  • Chapter 8 Enterprise Data Models
  • Data Models Overview
  • Inmon and Kimball
  • EDM Purpose
  • EDM Benefit
  • Data Model: Where to Start
  • Full Top-Down Data Model
  • Subject Area Model
  • Concept Model
  • Entity Relationship Model
  • Bus Architecture
  • Purchased Data Model
  • Model Insights
  • Data Components
  • Normalizing a Data Model
  • Supertype/Subtype Models
  • Capturing History in a Normalized Data Model
  • Surrogate Keys
  • Logical vs. Physical Data Model
  • Referential Integrity or Not
  • Other Data Models
  • Input Data Model
  • Staging Data Model
  • Final Thoughts
  • Chapter 9 Data Warehouse Architecture: Components
  • Architecture Overview
  • Architect Roles
  • Solution Architect
  • Data Warehouse Architect
  • Technical Architect
  • Data Architect
  • ETL Architect
  • BI Architect
  • Overall
  • Architecture Tiers
  • Single-Tier Architecture
  • Classic Two-Tier Architecture
  • Advanced Three-Tier Architecture
  • Data Warehouse Architectures
  • Solo Data Mart Architecture
  • Bus Architecture
  • Central Repository Architecture
  • Federated Architecture
  • Components (Layers)
  • Data Sources
  • Data Population
  • Data Organization
  • Data Distribution
  • Information Out
  • Implementation Approaches
  • Data Design and Data Flow
  • Logical vs. Physical Models
  • Top-Down Approach
  • Bottom-Up Approach
  • Hybrid Approach
  • Accelerators
  • Data Acquisition Layer
  • Centralized Data Layer
  • Data Distribution Layer
  • Performance Layer
  • User Presentation Layer
  • Methodology
  • Out-of-the-Box Solution
  • Chapter 10 ETL and Data Quality
  • Architecture
  • Data Population
  • Data Distribution
  • ETL Mapping
  • Initial and Incremental Loads
  • ETL vs. ELT vs. ETTL
  • Parallel Operations
  • ETL Roles
  • Data Flow Diagrams
  • Operational Data Store (ODS)
  • Source Systems
  • No Source
  • Multiple Sources
  • Alternate Sources (SIFs)
  • Unstructured Data
  • Data Profiling
  • Data Capture
  • Multiple Large Files
  • Switch Files
  • Failsafe Strategy
  • Transformation and Staging
  • Preparation
  • Surrogate Keys
  • Referential Integrity
  • Aggregating, Profiling, and Summarizing
  • Code Tables
  • Loading
  • History vs. No History
  • Insert/Update/Upsert/Delete
  • Population Information
  • Load Scheduling
  • Staging for EDW vs. Staging for Bus Architecture
  • Data Distribution
  • 3NF to Star
  • Data Quality
  • ETL Tools
  • Chapter 11 Project Planning and Methodology
  • Fundamentals
  • Risk: Phased Development
  • Risk: Data Quality
  • Risk: Resources
  • Risk: Cost
  • Change Management
  • Best Practices
  • Mistakes
  • Project Plan Methodology
  • Business Requirements
  • Strategy and Plan
  • Solution Outline
  • Design
  • Build
  • Deploy
  • Use
  • Part III Let’s Build
  • Chapter 12 Working Scenarios
  • The Chef: Let’s Get Cooking!
  • Top-Down (Enterprise Repository)
  • Vocabulary
  • Centralized Data Model
  • Data Architecture
  • Sources
  • Data Model
  • Database
  • Acquisition
  • Solution Overview
  • Bottom-Up (OLAP Reporting)
  • End Result
  • Vocabulary
  • Data Architecture
  • Conformed Dimension Administration
  • Sources
  • Solution Overview
  • Hybrid (Normalized Design and OLAP)
  • First Efforts
  • Data Models
  • Data Architecture
  • Solution Overview
  • Merging
  • Plan of Action
  • No Input: Structured Input Files
  • Integrating Phase 2
  • Change Management
  • The Bigger Picture: Enterprise Information Architecture (EIA)
  • Chapter 13 Data Governance
  • What Is Data Governance?
  • Definition
  • Reasons for Data Governance
  • Organizational Structure
  • Drivers and Initiatives
  • Data Governance: Major Points
  • Security and Sensitivity
  • Data Quality
  • Ownership
  • Change Control
  • Data Governance Readiness
  • Chapter 14 Post-Project Review
  • Synopsis
  • Project Review
  • Next Phase
  • Index