Table of Contents
Chapter 1.1: An Introduction to Data Architecture
Repetitive/Nonrepetitive Unstructured Data
Chapter 1.2: The Data Infrastructure
Comparing the Two Infrastructures
Chapter 1.3: The “Great Divide”
Nonrepetitive Unstructured Data
Chapter 1.4: Demographics of Corporate Data
Chapter 1.5: Corporate Data Analysis
Chapter 1.6: The Life Cycle of Data: Understanding Data Over Time
Chapter 1.7: A Brief History of Data
Data Base Management System (DBMS)
Chapter 2.1: The End-State Architecture—The “World Map”
Different Kinds of Data in the End State Architecture
Shaping the Data Through Models
Where Different Types of Questions Are Answered Across the End State Architecture
Metadata in the End State Architecture
Chapter 3.1: Transformations in the End-State Architecture
Transforming Data Into a Customized State
Transforming Data Into Bulk Storage
Transforming Data Generated Automatically
Chapter 4.1: A Brief History of Big Data
An Analogy—Taking the High Ground
Enter Teradata and MPP Processing
Chapter 4.2: What Is Big Data?
Chapter 4.3: Parallel Processing
Chapter 4.4: Unstructured Data
Textual Information—Everywhere
Decisions Based on Structured Data
The Business Value Proposition
Repetitive and Nonrepetitive Unstructured Information
Some Approaches to Contextualization
Chapter 4.5: Contextualizing Repetitive Unstructured Data
Parsing Repetitive Unstructured Data
Chapter 4.6: Textual Disambiguation
From Narrative Into an Analytical Data Base
Input Into Textual Disambiguation
Document Fracturing/Named Value Processing
Taxonomies in Multiple Languages
Commercial or Private Taxonomies?
Dynamics of Taxonomies and Textual Disambiguation
Taxonomies and Textual Disambiguation—Separate Technologies
Taxonomies—Maintenance Over Time
Chapter 5.1: The Siloed Application Environment
The Challenge of Siloed Applications
What Does a Siloed Application Look Like?
Overlap Between Siloed Applications
Dismantling Siloed Applications
Chapter 6.1: Introduction to Data Vault 2.0
Data Vault Origins and Background
What Is Data Vault 2.0 Modeling?
How Is Data Vault 2.0 Methodology Defined?
Why Do We Need a Data Vault 2.0 Architecture?
Where Does Data Vault 2.0 Implementation Fit?
What Are the Business Benefits of Data Vault 2.0?
Chapter 6.2: Introduction to Data Vault Modeling
What Is a Data Vault Model Concept?
Components of a Data Vault Model
What Makes Business Keys So Interesting?
What Does This Have to Do With Data Vault and Data Warehousing?
How Does This Translate to Data Vault Modeling?
Why Restructure the Data From the Staging Area?
What Are the Basic Rules of the Data Vault Model?
Why Do We Need Many to Many Link Structures?
Primary Key Options for Data Vault 2.0
Chapter 6.3: Introduction to Data Vault Architecture
What Is a Data Vault 2.0 Architecture?
How Does NoSQL Fit in to the Architecture?
What Are the Objectives of the Data Vault 2.0 Architecture?
What Is the Objective of the Data Vault 2.0 Model?
What Are Hard and Soft Business Rules?
How Does Managed Self Service BI Fit in the Architecture?
Chapter 6.4: Introduction to Data Vault Methodology
Data Vault 2.0 Methodology Overview
How Does CMMI Contribute to the Methodology?
If CMMI Is So Great, Why Should We Care About Agility Then?
Why Include PMP, SDLC If CMMI and Agile Should Be All That's Needed?
So Then, What Does Six Sigma Contribute to the Data Vault 2 Methodology?
Where Does TQM (Total Quality Management) Fit in to All of This?
Chapter 6.5: Introduction to Data Vault Implementation
What's So Important About Patterns?
Why Does Reengineering Happen Because of Big Data?
Why Do We Need to Virtualize Our Data Marts?
What Is Managed Self-Service BI?
Chapter 7.1: The Operational Environment: A Short History
Commercial Uses of the Computer
Ed Yourdon and the Structured Revolution
Response Time and Availability
Chapter 7.2: The Standard Work Unit
Your Vehicle Runs as Fast as the Vehicle in Front of It
Chapter 7.3: Data Modeling for the Structured Environment
Relating the Different Levels of the Data Model
Operational Data Models/Data Warehouse Data Models
Chapter 8.1: A Brief History of Data Architecture
Chapter 8.2: Big Data/Existing System Interface
The Big Data/Existing Systems Interface
The Repetitive Raw Big Data/Existing Systems Interface
The Nonrepetitive Raw Big Data/Existing Systems Interface
Into the Existing Systems Environment
The “Context Enriched” Big Data Environment
Analyzing Structured Data/Unstructured Data Together
Chapter 8.3: The Data Warehouse/Operational Environment Interface
The Operational/Data Warehouse Interface
Chapter 8.4: Data Architecture: A High-Level Perspective
Chapter 9.1: Repetitive Analytics: Some Basics
Chapter 9.2: Analyzing Repetitive Data
Active/Passive Indexing of Data
Chapter 9.3: Repetitive Analysis
Chapter 10.1: Nonrepetitive Data
Functional Sequencing Within Textual ETL
Internal Referential Integrity
Chapter 10.3: Analytics From Nonrepetitive Data
Chapter 11.1: Operational Analytics: Response Time
Chapter 12.1: Operational Analytics
Different Perspectives of Data
The Operational Data Store—ODS
Chapter 13.1: Personal Analytics
Chapter 14.1: Data Models Across the End-State Architecture
Functional Decomposition and Data Flow Diagrams
The Star Join/Dimensional Data Model
The Selective Subdivision of Data
Proactive/Reactive Data Models
Chapter 15.1: The System of Record
The End User Cycle of Awareness
The System of Record in the End State Architecture
The Role of Age in the System of Record
The Flow of Data in the System of Record
Other Data Than the System of Record
Is Data Updated in the System of Record?
Detailed and Summary Data in the System of Record
Auditing Data and the System of Record
Chapter 16.1: Business Value and the End-State Architecture
The Evolution of the End State Architecture
What is Meant by “Business Value”
Tactical Business Value/Strategic Business Value
Volume of Data Versus Business Value
Where Tactical Decisions Are Made
The Processing Components of Textual ETL
Merging Text Based Data and Structured Data
Chapter 18.1: An Introduction to Data Visualizations
Introduction to Data Visualizations—Overview
Visualization—A Science and an Art