Data Architecture Foundations Training

Chapter 1 – Data Architecture Stakeholders, Views and Viewpoints

• Data Architecture Begins with People
• The Big Picture
• Who Are the Stakeholders?
• Stakeholder Identification
• Stakeholders' Unique Concerns
• Stakeholder Concerns & Viewpoints
• Data Architecture Stakeholder Map
• What Are Views and Viewpoints?
• Defining Viewpoints
• Define a Viewpoint: Data Flow Example
• Standard Notation – ArchiMate
• ArchiMate Modeling
• Information Structure View
• Information and Deployment View
• Data Flow View
• Database Deployment View
• Data Architecture Viewpoints
• Summary

Chapter 2 – Data and Requirements Management

• Perspectives on Data Requirements
• Requirements Management
• It Starts with Business
• Knowing Functional Uses
• Getting the Whole Picture
• Data Architecture Requirements Process
• Initiation
• Feasibility
• Application Analysis
• Data Standards
• Recovery Time and Point Objectives
• Let’s be SMART!
• Specific
• Measurable
• Achievable
• Realistic
• Timely
• Data Quality Challenges
• Summary

Chapter 3 – Data Architecture Patterns

• Do You Love Patterns?
• Architecture Patterns
• Where to Begin?
• Motivations for working with Patterns
• Pattern Identification Challenge
• Characteristics of Patterns
• Data Architecture Pattern Types
• Recognize the 'Where'
• Data Patterns Classification
• Let's Try an Example
• MDM Patterns
• MDM Pattern Example
• Data Architecture Patterns in Use
• Data Architecture Patterns Categories
• MDM Pattern Example
• Data Integration Patterns
• Data Integration Pattern Example
• Relationship Between Patterns -Example
• Knowledge Is Critical
• It's Not Just About Technology
• Architecture Saves the Day!
• Summary

Chapter 4 – Data Governance

• Objectives
• Governance
• Data Governance (DMBOK)
• Data Governance
• Deciding if Data Needs Management
• Meta-data Management
• Meta-data
• Examples of Meta-data
• Importance of Meta-data Management
• Meta-data Management Best Practices
• Meta-data Management Tooling
• Glossary of Business Terms & Data Elements
• Data Lineage
• Impact Assessment Via Data Lineage
• Reference & Master Data Management
• Systems of Record and Reference
• Master Data Management Styles
• Repository Style
• Registry Style
• Hybrid Style
• MDM Project Activities
• Data Quality Management
• Data Quality
• Common Causes of Poor Data Quality
• Data Quality: Possible Solutions
• Data Quality Tools
• Data Quality for “Big Data”
• Summary

Chapter 5 – Data Modeling

• Objectives
• Data Architecture Management
• Data Development
• Conceptual Models
• Logical and Physical Models
• Formal Models for Organizing Data
• Classes & Objects
• Object-Relational Mapping
• Unified Modeling Language
• Hierarchical Models
• Hierarchical Model Example: ERD
• Data in Motion
• XML
• Hierarchical Model Example: XML
• JSON
• Hierarchical Model Example: JSON
• Canonical Models
• Benefits of Canonical Models
• Standard Meta-Models
• Summary