DAMA-DMBOK (2nd Edition): Data Management Body of Knowledge

Preface
chapter 1
Data Management
	1. Introduction
	1.1 Business Drivers
	1.2 Goals
	2. Essential Concepts
		2.1 Data
		2.2 Data and Information
		2.3 Data as an Organizational Asset
		2.4 Data Management Principles
		2.5 Data Management Challenges
		2.5.1 Data Differs from Other Assets
		2.5.2 Data Valuation
		2.5.3 Data Quality
		2.5.4 Planning for Better Data
		2.5.5 Metadata and Data Management
		2.5.6 Data Management is Cross-functional
		2.5.7 Establishing an Enterprise Perspective
		2.5.8 Accounting for Other Perspectives
		2.5.9 The Data Lifecycle
		2.5.10 Different Types of Data
		2.5.11 Data and Risk
		2.5.12 Data Management and Technology
		2.5.13 Effective Data Management Requires Leadership and Commitment
		2.6 Data Management Strategy
	3. Data Management Frameworks
		3.1 Strategic Alignment Model
		3.2 The Amsterdam Information Model
		3.3 The DAMA-DMBOK Framework
		3.4 DMBOK Pyramid (Aiken)
		3.5 DAMA Data Management Framework Evolved
	4. DAMA and the DMBOK
	5. Works Cited / Recommended
chapter 2
Data Handling Ethics
	1. Introduction
	2. Business Drivers
	3. Essential Concepts
		3.1 Ethical Principles for Data
		3.2 Principles Behind Data Privacy Law
		3.3 Online Data in an Ethical Context
		3.4 Risks of Unethical Data Handling Practices
		3.4.1 Timing
		3.4.2 Misleading Visualizations
		3.4.3 Unclear Definitions or Invalid Comparisons
		3.4.4 Bias
		3.4.5 Transforming and Integrating Data
		3.4.6 Obfuscation / Redaction of Data
		3.5 Establishing an Ethical Data Culture
		3.5.1 Review Current State Data Handling Practices
		3.5.2 Identify Principles, Practices, and Risk Factors
		3.5.3 Create an Ethical Data Handling Strategy and Roadmap
		3.5.4 Adopt a Socially Responsible Ethical Risk Model
		3.6 Data Ethics and Governance
	4. Works Cited / Recommended
chapter 3
Data Governance
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Data-centric Organization
		1.3.2 Data Governance Organization
		1.3.3 Data Governance Operating Model Types
		1.3.4 Data Stewardship
		1.3.5 Types of Data Stewards
		1.3.6 Data Policies
		1.3.7 Data Asset Valuation
	2. Activities
		2.1 Define Data Governance for the Organization
		2.2 Perform Readiness Assessment
		2.3 Perform Discovery and Business Alignment
		2.4 Develop Organizational Touch Points
		2.5 Develop Data Governance Strategy
		2.6 Define the DG Operating Framework
		2.7 Develop Goals, Principles, and Policies
		2.8 Underwrite Data Management Projects
		2.9 Engage Change Management
		2.10 Engage in Issue Management
		2.11 Assess Regulatory Compliance Requirements
		2.12 Implement Data Governance
		2.13 Sponsor Data Standards and Procedures
		2.14 Develop a Business Glossary
		2.15 Coordinate with Architecture Groups
		2.16 Sponsor Data Asset Valuation
		2.17 Embed Data Governance
	3. Tools and Techniques
		3.1 Online Presence / Websites
		3.2 Business Glossary
		3.3 Workflow Tools
		3.4 Document Management Tools
		3.5 Data Governance Scorecards
	4. Implementation Guidelines
		4.1 Organization and Culture
		4.2 Adjustment and Communication
	5. Metrics
	6. Works Cited / Recommended
chapter 4
Data Architecture
	1. Introduction
		1.1 Business Drivers
		1.2 Data Architecture Outcomes and Practices
		1.3 Essential Concepts
		1.3.1 Enterprise Architecture Domains
		1.3.2 Enterprise Architecture Frameworks
		1.3.2.1 Zachman Framework for Enterprise Architecture
		1.3.3 Enterprise Data Architecture
		1.3.3.1 Enterprise Data Model
		1.3.3.2 Data Flow Design
	2. Activities
		2.1 Establish Data Architecture Practice
		2.1.1 Evaluate Existing Data Architecture Specifications
		2.1.2 Develop a Roadmap
		2.1.3 Manage Enterprise Requirements within Projects
		2.2 Integrate with Enterprise Architecture
	3. Tools
		3.1 Data Modeling Tools
		3.2 Asset Management Software
		3.3 Graphical Design Applications
	4. Techniques
		4.1 Lifecycle Projections
		4.2 Diagramming Clarity
	5. Implementation Guidelines
		5.1 Readiness Assessment / Risk Assessment
		5.2 Organization and Cultural Change
	6. Data Architecture Governance
		6.1 Metrics
	7. Works Cited / Recommended
chapter 5
Data Modeling and Design
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Data Modeling and Data Models
		1.3.2 Types of Data that are Modeled
		1.3.3 Data Model Components
		1.3.3.1 Entity
		1.3.3.1.1 Entity Aliases
		1.3.3.1.2 Graphic Representation of Entities
		1.3.3.1.3 Definition of Entities
		1.3.3.2 Relationship
		1.3.3.2.1 Relationship Aliases
		1.3.3.2.2 Graphic Representation of Relationships
		1.3.3.2.3 Relationship Cardinality
		1.3.3.2.4 Arity of Relationships
		1.3.3.2.4.1 Unary (Recursive) Relationship
		1.3.3.2.4.2 Binary Relationship
		1.3.3.2.4.3 Ternary Relationship
		1.3.3.2.5 Foreign Key
		1.3.3.3 Attribute
		1.3.3.3.1 Graphic Representation of Attributes
		1.3.3.3.2 Identifiers
		1.3.3.3.2.1 Construction-type Keys
		1.3.3.3.2.2 Function-type Keys
		1.3.3.3.2.3 Identifying vs. Non-Identifying Relationships
		1.3.3.4 Domain
		1.3.4 Data Modeling Schemes
		1.3.4.1 Relational
		1.3.4.2 Dimensional
		1.3.4.2.1 Fact Tables
		1.3.4.2.2 Dimension Tables
		1.3.4.2.3 Snowflaking
		1.3.4.2.4 Grain
		1.3.4.2.5 Conformed Dimensions
		1.3.4.2.6 Conformed Facts
		1.3.4.3 Object-Oriented (UML)
		1.3.4.4 Fact-Based Modeling (FBM)
		1.3.4.4.1 Object Role Modeling (ORM or ORM2)
		1.3.4.4.2 Fully Communication Oriented Modeling (FCO-IM)
		1.3.4.5 Time-Based
		1.3.4.5.1 Data Vault
		1.3.4.5.2 Anchor Modeling
		1.3.4.6 NoSQL
		1.3.4.6.1 Document
		1.3.4.6.2 Key-value
		1.3.4.6.3 Column-oriented
		1.3.4.6.4 Graph
		1.3.5 Data Model Levels of Detail
		1.3.5.1 Conceptual
		1.3.5.2 Logical
		1.3.5.3 Physical
		1.3.5.3.1 Canonical
		1.3.5.3.2 Views
		1.3.5.3.3 Partitioning
		1.3.5.3.4 Denormalization
		1.3.6 Normalization
		1.3.7 Abstraction
	2. Activities
		2.1 Plan for Data Modeling
		2.2 Build the Data Model
		2.2.1 Forward Engineering
		2.2.1.1 Conceptual Data Modeling
		2.2.1.2 Logical Data Modeling
		2.2.1.2.1 Analyze Information Requirements
		2.2.1.2.2 Analyze Existing Documentation
		2.2.1.2.3 Add Associative Entities
		2.2.1.2.4 Add Attributes
		2.2.1.2.5 Assign Domains
		2.2.1.2.6 Assign Keys
		2.2.1.3 Physical Data Modeling
		2.2.1.3.1 Resolve Logical Abstractions
		2.2.1.3.2 Add Attribute Details
		2.2.1.3.3 Add Reference Data Objects
		2.2.1.3.4 Assign Surrogate Keys
		2.2.1.3.5 Denormalize for Performance
		2.2.1.3.6 Index for Performance
		2.2.1.3.7 Partition for Performance
		2.2.1.3.8 Create Views
		2.2.2 Reverse Engineering
		2.3 Review the Data Models
		2.4 Maintain the Data Models
	3. Tools
		3.1 Data Modeling Tools
		3.2 Lineage Tools
		3.3 Data Profiling Tools
		3.4 Metadata Repositories
		3.5 Data Model Patterns
		3.6 Industry Data Models
	4. Best Practices
		4.1 Best Practices in Naming Conventions
		4.2 Best Practices in Database Design
	5. Data Model Governance
		5.1 Data Model and Design Quality Management
		5.1.1 Develop Data Modeling and Design Standards
		5.1.2 Review Data Model and Database Design Quality
		5.1.3 Manage Data Model Versioning and Integration
		5.2 Data Modeling Metrics
	6. Works Cited / Recommended
chapter 6
Data Storage and Operations
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Database Terms
		1.3.2 Data Lifecycle Management
		1.3.3 Administrators
		1.3.3.1 Production DBA
		1.3.3.2 Application DBA
		1.3.3.3 Procedural and Development DBAs
		1.3.3.4 NSA
		1.3.4 Database Architecture Types
		1.3.4.1 Centralized Databases
		1.3.4.2 Distributed Databases
		1.3.4.2.1 Federated Databases
		1.3.4.2.2 Blockchain Database
		1.3.4.3 Virtualization / Cloud Platforms
		1.3.5 Database Processing Types
		1.3.5.1 ACID
		1.3.5.2 BASE
		1.3.5.3 CAP
		1.3.6 Data Storage Media
		1.3.6.1 Disk and Storage Area Networks (SAN)
		1.3.6.2 In-Memory
		1.3.6.3 Columnar Compression Solutions
		1.3.6.4 Flash Memory
		1.3.7 Database Environments
		1.3.7.1 Production Environment
		1.3.7.2 Pre-production Environments
		1.3.7.2.1 Development
		1.3.7.2.2 Test
		1.3.7.2.3 Sandboxes or Experimental Environments
		1.3.8 Database Organization
		1.3.8.1 Hierarchical
		1.3.8.2 Relational
		1.3.8.2.1 Multidimensional
		1.3.8.2.2 Temporal
		1.3.8.3 Non-relational
		1.3.8.3.1 Column-oriented
		1.3.8.3.2 Spatial
		1.3.8.3.3 Object / Multi-media
		1.3.8.3.4 Flat File Database
		1.3.8.3.5 Key-Value Pair
		1.3.8.3.6 Triplestore
		1.3.9 Specialized Databases
		1.3.10 Common Database Processes
		1.3.10.1 Archiving
		1.3.10.2 Capacity and Growth Projections
		1.3.10.3 Change Data Capture (CDC)
		1.3.10.4 Purging
		1.3.10.5 Replication
		1.3.10.6 Resiliency and Recovery
		1.3.10.7 Retention
		1.3.10.8 Sharding
	2. Activities
		2.1 Manage Database Technology
		2.1.1 Understand Database Technology Characteristics
		2.1.2 Evaluate Database Technology
		2.1.3 Manage and Monitor Database Technology
		2.2 Manage Databases
		2.2.1 Understand Requirements
		2.2.1.1 Define Storage Requirements
		2.2.1.2 Identify Usage Patterns
		2.2.1.3 Define Access Requirements
		2.2.2 Plan for Business Continuity
		2.2.2.1 Make Backups
		2.2.2.2 Recover Data
		2.2.3 Develop Database Instances
		2.2.3.1 Manage the Physical Storage Environment
		2.2.3.2 Manage Database Access Controls
		2.2.3.3 Create Storage Containers
		2.2.3.4 Implement Physical Data Models
		2.2.3.5 Load Data
		2.2.3.6 Manage Data Replication
		2.2.4 Manage Database Performance
		2.2.4.1 Set Database Performance Service Levels
		2.2.4.2 Manage Database Availability
		2.2.4.3 Manage Database Execution
		2.2.4.4 Maintain Database Performance Service Levels
		2.2.4.4.1 Transaction Performance vs. Batch Performance
		2.2.4.4.2 Issue Remediation
		2.2.4.5 Maintain Alternate Environments
		2.2.5 Manage Test Data Sets
		2.2.6 Manage Data Migration
	3. Tools
		3.1 Data Modeling Tools
		3.2 Database Monitoring Tools
		3.3 Database Management Tools
		3.4 Developer Support Tools
	4. Techniques
		4.1 Test in Lower Environments
		4.2 Physical Naming Standards
		4.3 Script Usage for All Changes
	5. Implementation Guidelines
		5.1 Readiness Assessment / Risk Assessment
		5.2 Organization and Cultural Change
	6. Data Storage and Operations Governance
		6.1 Metrics
		6.2 Information Asset Tracking
		6.3 Data Audits and Data Validation
	7. Works Cited / Recommended
chapter 7
Data Security
	1. Introduction
		1.1 Business Drivers
		1.1.1 Risk Reduction
		1.1.2 Business Growth
		1.1.3 Security as an Asset
		1.2 Goals and Principles
		1.2.1 Goals
		1.2.2 Principles
		1.3 Essential Concepts
		1.3.1 Vulnerability
		1.3.2 Threat
		1.3.3 Risk
		1.3.4 Risk Classifications
		1.3.5 Data Security Organization
		1.3.6 Security Processes
		1.3.6.1 The Four A’s
		1.3.6.2 Monitoring
		1.3.7 Data Integrity
		1.3.8 Encryption
		1.3.8.1 Hash
		1.3.8.2 Private-key
		1.3.8.3 Public-key
		1.3.9 Obfuscation or Masking
		1.3.9.1 Persistent Data Masking
		1.3.9.2 Dynamic Data Masking
		1.3.9.3 Masking Methods
		1.3.10 Network Security Terms
		1.3.10.1 Backdoor
		1.3.10.2 Bot or Zombie
		1.3.10.3 Cookie
		1.3.10.4 Firewall
		1.3.10.5 Perimeter
		1.3.10.6 DMZ
		1.3.10.7 Super User Account
		1.3.10.8 Key Logger
		1.3.10.9 Penetration Testing
		1.3.10.10 Virtual Private Network (VPN)
		1.3.11 Types of Data Security
		1.3.11.1 Facility Security
		1.3.11.2 Device Security
		1.3.11.3 Credential Security
		1.3.11.3.1 Identity Management Systems
		1.3.11.3.2 User ID Standards for Email Systems
		1.3.11.3.3 Password Standards
		1.3.11.3.4 Multiple Factor Identification
		1.3.11.4 Electronic Communication Security
		1.3.12 Types of Data Security Restrictions
		1.3.12.1 Confidential Data
		1.3.12.2 Regulated Data
		1.3.12.2.1 Sample Regulatory Families
		1.3.12.2.2 Industry or Contract-based Regulation
		1.3.13 System Security Risks
		1.3.13.1 Abuse of Excessive Privilege
		1.3.13.2 Abuse of Legitimate Privilege
		1.3.13.3 Unauthorized Privilege Elevation
		1.3.13.4 Service Account or Shared Account Abuse
		1.3.13.4.1 Service Accounts
		1.3.13.4.2 Shared Accounts
		1.3.13.5 Platform Intrusion Attacks
		1.3.13.6 SQL Injection Vulnerability
		1.3.13.7 Default Passwords
		1.3.13.8 Backup Data Abuse
		1.3.14 Hacking / Hacker
		1.3.15 Social Threats to Security / Phishing
		1.3.16 Malware
		1.3.16.1 Adware
		1.3.16.2 Spyware
		1.3.16.3 Trojan Horse
		1.3.16.4 Virus
		1.3.16.5 Worm
		1.3.16.6 Malware Sources
		1.3.16.6.1 Instant Messaging (IM)
		1.3.16.6.2 Social Networking Sites
		1.3.16.6.3 Spam
	2. Activities
		2.1 Identify Data Security Requirements
		2.1.1 Business Requirements
		2.1.2 Regulatory Requirements
		2.2 Define Data Security Policy
		2.2.1 Security Policy Contents
		2.3 Define Data Security Standards
		2.3.1 Define Data Confidentiality Levels
		2.3.2 Define Data Regulatory Categories
		2.3.3 Define Security Roles
		2.3.3.1 Role Assignment Grid
		2.3.3.2 Role Assignment Hierarchy
		2.3.4 Assess Current Security Risks
		2.3.5 Implement Controls and Procedures
		2.3.5.1 Assign Confidentiality Levels
		2.3.5.2 Assign Regulatory Categories
		2.3.5.3 Manage and Maintain Data Security
		2.3.5.3.1 Control Data Availability / Data-centric Security
		2.3.5.3.2 Monitor User Authentication and Access Behavior
		2.3.5.4 Manage Security Policy Compliance
		2.3.5.4.1 Manage Regulatory Compliance
		2.3.5.4.2 Audit Data Security and Compliance Activities
	3. Tools
		3.1 Anti-Virus Software / Security Software
		3.2 HTTPS
		3.3 Identity Management Technology
		3.4 Intrusion Detection and Prevention Software
		3.5 Firewalls (Prevention)
		3.6 Metadata Tracking
		3.7 Data Masking/Encryption
	4. Techniques
		4.1 CRUD Matrix Usage
		4.2 Immediate Security Patch Deployment
		4.3 Data Security Attributes in Metadata
		4.4 Metrics
		4.4.1 Security Implementation Metrics
		4.4.2 Security Awareness Metrics
		4.4.3 Data Protection Metrics
		4.4.4 Security Incident Metrics
		4.4.5 Confidential Data Proliferation
		4.5 Security Needs in Project Requirements
		4.6 Efficient Search of Encrypted Data
		4.7 Document Sanitization
	5. Implementation Guidelines
		5.1 Readiness Assessment / Risk Assessment
		5.2 Organization and Cultural Change
		5.3 Visibility into User Data Entitlement
		5.4 Data Security in an Outsourced World
		5.5 Data Security in Cloud Environments
	6. Data Security Governance
		6.1 Data Security and Enterprise Architecture
	7. Works Cited / Recommended
chapter 8
Data Integration and Interoperability
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Extract, Transform, and Load
		1.3.1.1 Extract
		1.3.1.2 Transform
		1.3.1.3 Load
		1.3.1.4 ELT
		1.3.1.5 Mapping
		1.3.2 Latency
		1.3.2.1 Batch
		1.3.2.2 Change Data Capture
		1.3.2.3 Near-real-time and Event-driven
		1.3.2.4 Asynchronous
		1.3.2.5 Real-time, Synchronous
		1.3.2.6 Low Latency or Streaming
		1.3.3 Replication
		1.3.4 Archiving
		1.3.5 Enterprise Message Format / Canonical Model
		1.3.6 Interaction Models
		1.3.6.1 Point-to-point
		1.3.6.2 Hub-and-spoke
		1.3.6.3 Publish - Subscribe
		1.3.7 DII Architecture Concepts
		1.3.7.1 Application Coupling
		1.3.7.2 Orchestration and Process Controls
		1.3.7.3 Enterprise Application Integration (EAI)
		1.3.7.4 Enterprise Service Bus (ESB)
		1.3.7.5 Service-Oriented Architecture (SOA)
		1.3.7.6 Complex Event Processing (CEP)
		1.3.7.7 Data Federation and Virtualization
		1.3.7.8 Data-as-a-Service (DaaS)
		1.3.7.9 Cloud-based Integration
		1.3.8 Data Exchange Standards
	2. Data Integration Activities
		2.1 Plan and Analyze
		2.1.1 Define Data Integration and Lifecycle Requirements
		2.1.2 Perform Data Discovery
		2.1.3 Document Data Lineage
		2.1.4 Profile Data
		2.1.5 Collect Business Rules
		2.2 Design Data Integration Solutions
		2.2.1 Design Data Integration Architecture
		2.2.1.1 Select Interaction Model
		2.2.1.2 Design Data Services or Exchange Patterns
		2.2.2 Model Data Hubs, Interfaces, Messages, and Data Services
		2.2.3 Map Data Sources to Targets
		2.2.4 Design Data Orchestration
		2.3 Develop Data Integration Solutions
		2.3.1 Develop Data Services
		2.3.2 Develop Data Flows
		2.3.3 Develop Data Migration Approach
		2.3.4 Develop a Publication Approach
		2.3.5 Develop Complex Event Processing Flows
		2.3.6 Maintain DII Metadata
		2.4 Implement and Monitor
	3. Tools
		3.1 Data Transformation Engine/ETL Tool
		3.2 Data Virtualization Server
		3.3 Enterprise Service Bus
		3.4 Business Rules Engine
		3.5 Data and Process Modeling Tools
		3.6 Data Profiling Tool
		3.7 Metadata Repository
	4. Techniques
	5. Implementation Guidelines
		5.1 Readiness Assessment / Risk Assessment
		5.2 Organization and Cultural Change
	6. DII Governance
		6.1 Data Sharing Agreements
		6.2 DII and Data Lineage
		6.3 Data Integration Metrics
	7. Works Cited / Recommended
chapter 9
Document and Content Management
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Content
		1.3.1.1 Content Management
		1.3.1.2 Content Metadata
		1.3.1.3 Content Modeling
		1.3.1.4 Content Delivery Methods
		1.3.2 Controlled Vocabularies
		1.3.2.1 Vocabulary Management
		1.3.2.2 Vocabulary Views and Micro-controlled Vocabulary
		1.3.2.3 Term and Pick Lists
		1.3.2.4 Term Management
		1.3.2.5 Synonym Rings and Authority Lists
		1.3.2.6 Taxonomies
		1.3.2.7 Classification Schemes and Tagging
		1.3.2.8 Thesauri
		1.3.2.9 Ontology
		1.3.3 Documents and Records
		1.3.3.1 Document Management
		1.3.3.2 Records Management
		1.3.3.3 Digital Asset Management
		1.3.4 Data Map
		1.3.5 E-discovery
		1.3.6 Information Architecture
		1.3.7 Search Engine
		1.3.8 Semantic Model
		1.3.9 Semantic Search
		1.3.10 Unstructured Data
		1.3.11 Workflow
	2. Activities
		2.1 Plan for Lifecycle Management
		2.1.1 Plan for Records Management
		2.1.2 Develop a Content Strategy
		2.1.3 Create Content Handling Policies
		2.1.3.1 Social Media Policies
		2.1.3.2 Device Access Policies
		2.1.3.3 Handling Sensitive Data
		2.1.3.4 Responding to Litigation
		2.1.4 Define Content Information Architecture
		2.2 Manage the Lifecycle
		2.2.1 Capture Records and Content
		2.2.2 Manage Versioning and Control
		2.2.3 Backup and Recovery
		2.2.4 Manage Retention and Disposal
		2.2.5 Audit Documents / Records
		2.3 Publish and Deliver Content
		2.3.1 Provide Access, Search, and Retrieval
		2.3.2 Deliver Through Acceptable Channels
	3. Tools
		3.1 Enterprise Content Management Systems
		3.1.1 Document Management
		3.1.1.1 Digital Asset Management
		3.1.1.2 Image Processing
		3.1.1.3 Records Management System
		3.1.2 Content Management System
		3.1.3 Content and Document Workflow
		3.2 Collaboration Tools
		3.3 Controlled Vocabulary and Metadata Tools
		3.4 Standard Markup and Exchange Formats
		3.4.1 XML
		3.4.2 JSON
		3.4.3 RDF and Related W3C Specifications
		3.4.4 Schema.org
		3.5 E-discovery Technology
	4. Techniques
		4.1 Litigation Response Playbook
		4.2 Litigation Response Data Map
	5. Implementation Guidelines
		5.1 Readiness Assessment / Risk Assessment
		5.1.1 Records Management Maturity
		5.1.2 E-discovery Assessment
		5.2 Organization and Cultural Change
	6. Documents and Content Governance
		6.1 Information Governance Frameworks
		6.2 Proliferation of Information
		6.3 Govern for Quality Content
		6.4 Metrics
		6.4.1 Records Management
		6.4.2 E-discovery
		6.4.3 ECM
	7. Works Cited / Recommended
chapter 10
Reference and Master Data
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Differences Between Master and Reference Data
		1.3.2 Reference Data
		1.3.2.1 Reference Data Structure
		1.3.2.1.1 Lists
		1.3.2.1.2 Cross-Reference Lists
		1.3.2.1.3 Taxonomies
		1.3.2.1.4 Ontologies
		1.3.2.2 Proprietary or Internal Reference Data
		1.3.2.3 Industry Reference Data
		1.3.2.4 Geographic or Geo-statistical Data
		1.3.2.5 Computational Reference Data
		1.3.2.6 Standard Reference Data Set Metadata
		1.3.3 Master Data
		1.3.3.1 System of Record, System of Reference
		1.3.3.2 Trusted Source, Golden Record
		1.3.3.3 Master Data Management
		1.3.3.4 Master Data Management Key Processing Steps
		1.3.3.4.1 Data Model Management
		1.3.3.4.2 Data Acquisition
		1.3.3.4.3 Data Validation, Standardization, and Enrichment
		1.3.3.4.4 Entity Resolution and Identifier Management
		1.3.3.4.4.1 Matching
		1.3.3.4.4.2 Identity Resolution
		1.3.3.4.4.3 Matching Workflows / Reconciliation Types
		1.3.3.4.4.4 Master Data ID Management
		1.3.3.4.4.5 Affiliation Management
		1.3.3.4.5 Data Sharing and Stewardship
		1.3.3.5 Party Master Data
		1.3.3.6 Financial Master Data
		1.3.3.7 Legal Master Data
		1.3.3.8 Product Master Data
		1.3.3.9 Location Master Data
		1.3.3.10 Industry Master Data – Reference Directories
		1.3.4 Data Sharing Architecture
	2. Activities
		2.1 MDM Activities
		2.1.1 Define MDM Drivers and Requirements
		2.1.2 Evaluate and Assess Data Sources
		2.1.3 Define Architectural Approach
		2.1.4 Model Master Data
		2.1.5 Define Stewardship and Maintenance Processes
		2.1.6 Establish Governance Policies to Enforce Use of Master Data
		2.2 Reference Data Activities
		2.2.1 Define Drivers and Requirements
		2.2.2 Assess Data Sources
		2.2.3 Define Architectural Approach
		2.2.4 Model Reference Data Sets
		2.2.5 Define Stewardship and Maintenance Processes
		2.2.6 Establish Reference Data Governance Policies
	3. Tools and Techniques
	4. Implementation Guidelines
		4.1 Adhere to Master Data Architecture
		4.2 Monitor Data Movement
		4.3 Manage Reference Data Change
		4.4 Data Sharing Agreements
	5. Organization and Cultural Change
	6. Reference and Master Data Governance
		6.1 Metrics
	7. Works Cited / Recommended
chapter 11
Data Warehousing and Business Intelligence
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Business Intelligence
		1.3.2 Data Warehouse
		1.3.3 Data Warehousing
		1.3.4 Approaches to Data Warehousing
		1.3.5 Corporate Information Factory (Inmon)
		1.3.6 Dimensional DW (Kimball)
		1.3.7 DW Architecture Components
		1.3.7.1 Source Systems
		1.3.7.2 Data Integration
		1.3.7.3 Data Storage Areas
		1.3.8 Types of Load Processing
		1.3.8.1 Historical Data
		1.3.8.2 Batch Change Data Capture
		1.3.8.3 Near-real-time and Real-time
	2. Activities
		2.1 Understand Requirements
		2.2 Define and Maintain the DW/BI Architecture
		2.2.1 Define DW/BI Technical Architecture
		2.2.2 Define DW/BI Management Processes
		2.3 Develop the Data Warehouse and Data Marts
		2.3.1 Map Sources to Targets
		2.3.2 Remediate and Transform Data
		2.4 Populate the Data Warehouse
		2.5 Implement the Business Intelligence Portfolio
		2.5.1 Group Users According to Needs
		2.5.2 Match Tools to User Requirements
		2.6 Maintain Data Products
		2.6.1 Release Management
		2.6.2 Manage Data Product Development Lifecycle
		2.6.3 Monitor and Tune Load Processes
		2.6.4 Monitor and Tune BI Activity and Performance
	3. Tools
		3.1 Metadata Repository
		3.1.1 Data Dictionary / Glossary
		3.1.2 Data and Data Model Lineage
		3.2 Data Integration Tools
		3.3 Business Intelligence Tools Types
		3.3.1 Operational Reporting
		3.3.2 Business Performance Management
		3.3.3 Operational Analytic Applications
		3.3.3.1 Multi-dimensional Analysis – OLAP
	4. Techniques
		4.1 Prototypes to Drive Requirements
		4.2 Self-Service BI
		4.3 Audit Data that can be Queried
	5. Implementation Guidelines
		5.1 Readiness Assessment / Risk Assessment
		5.2 Release Roadmap
		5.3 Configuration Management
		5.4 Organization and Cultural Change
		5.4.1 Dedicated Team
	6. DW/BI Governance
		6.1 Enabling Business Acceptance
		6.2 Customer / User Satisfaction
		6.3 Service Level Agreements
		6.4 Reporting Strategy
		6.5 Metrics
		6.5.1 Usage Metrics
		6.5.2 Subject Area Coverage Percentages
		6.5.3 Response and Performance Metrics
	7. Works Cited / Recommended
chapter 12
Metadata Management
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Metadata vs. Data
		1.3.2 Types of Metadata
		1.3.2.1 Business Metadata
		1.3.2.2 Technical Metadata
		1.3.2.3 Operational Metadata
		1.3.3 ISO / IEC 11179 Metadata Registry Standard
		1.3.4 Metadata for Unstructured Data
		1.3.5 Sources of Metadata
		1.3.5.1 Application Metadata Repositories
		1.3.5.2 Business Glossary
		1.3.5.3 Business Intelligence (BI) Tools
		1.3.5.4 Configuration Management Tools
		1.3.5.5 Data Dictionaries
		1.3.5.6 Data Integration Tools
		1.3.5.7 Database Management and System Catalogs
		1.3.5.8 Data Mapping Management Tools
		1.3.5.9 Data Quality Tools
		1.3.5.10 Directories and Catalogs
		1.3.5.11 Event Messaging Tools
		1.3.5.12 Modeling Tools and Repositories
		1.3.5.13 Reference Data Repositories
		1.3.5.14 Service Registries
		1.3.5.15 Other Metadata Stores
		1.3.6 Types of Metadata Architecture
		1.3.6.1 Centralized Metadata Architecture
		1.3.6.2 Distributed Metadata Architecture
		1.3.6.3 Hybrid Metadata Architecture
		1.3.6.4 Bi-Directional Metadata Architecture
	2. Activities
		2.1 Define Metadata Strategy
		2.2 Understand Metadata Requirements
		2.3 Define Metadata Architecture
		2.3.1 Create MetaModel
		2.3.2 Apply Metadata Standards
		2.3.3 Manage Metadata Stores
		2.4 Create and Maintain Metadata
		2.4.1 Integrate Metadata
		2.4.2 Distribute and Deliver Metadata
		2.5 Query, Report, and Analyze Metadata
	3. Tools
		3.1 Metadata Repository Management Tools
	4. Techniques
		4.1 Data Lineage and Impact Analysis
		4.2 Metadata for Big Data Ingest
	5. Implementation Guidelines
		5.1 Readiness Assessment / Risk Assessment
		5.2 Organizational and Cultural Change
	6. Metadata Governance
		6.1 Process Controls
		6.2 Documentation of Metadata Solutions
		6.3 Metadata Standards and Guidelines
		6.4 Metrics
	7. Works Cited / Recommended
chapter 13
Data Quality
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Data Quality
		1.3.2 Critical Data
		1.3.3 Data Quality Dimensions
		1.3.4 Data Quality and Metadata
		1.3.5 Data Quality ISO Standard
		1.3.6 Data Quality Improvement Lifecycle
		1.3.7 Data Quality Business Rule Types
		1.3.8 Common Causes of Data Quality Issues
		1.3.8.1 Issues Caused by Lack of Leadership
		1.3.8.3 Issues Caused by Data Processing Functions
		1.3.8.4 Issues Caused by System Design
		1.3.8.5 Issues Caused by Fixing Issues
		1.3.9 Data Profiling
		1.3.10 Data Quality and Data Processing
		1.3.10.1 Data Cleansing
		1.3.10.2 Data Enhancement
		1.3.10.3 Data Parsing and Formatting
		1.3.10.4 Data Transformation and Standardization
	2. Activities
		2.1 Define High Quality Data
		2.2 Define a Data Quality Strategy
		2.3 Identify Critical Data and Business Rules
		2.4 Perform an Initial Data Quality Assessment
		2.5 Identify and Prioritize Potential Improvements
		2.6 Define Goals for Data Quality Improvement
		2.7 Develop and Deploy Data Quality Operations
		2.7.1 Manage Data Quality Rules
		2.7.2 Measure and Monitor Data Quality
		2.7.3 Develop Operational Procedures for Managing Data Issues
		2.7.4 Establish Data Quality Service Level Agreements
		2.7.5 Develop Data Quality Reporting
	3. Tools
		3.1 Data Profiling Tools
		3.2 Data Querying Tools
		3.3 Modeling and ETL Tools
		3.4 Data Quality Rule Templates
		3.5 Metadata Repositories
	4. Techniques
		4.1 Preventive Actions
		4.2 Corrective Actions
		4.3 Quality Check and Audit Code Modules
		4.4 Effective Data Quality Metrics
		4.5 Statistical Process Control
		4.6 Root Cause Analysis
	5. Implementation Guidelines
		5.1 Readiness Assessment / Risk Assessment
		5.2 Organization and Cultural Change
	6. Data Quality and Data Governance
		6.1 Data Quality Policy
		6.2 Metrics
	7. Works Cited / Recommended
chapter 14
Big Data and Data Science
	1. Introduction
		1.1 Business Drivers
		1.2 Principles
		1.3 Essential Concepts
		1.3.1 Data Science
		1.3.2 The Data Science Process
		1.3.3 Big Data
		1.3.4 Big Data Architecture Components
		1.3.5 Sources of Big Data
		1.3.6 Data Lake
		1.3.7 Services-Based Architecture
		1.3.8 Machine Learning
		1.3.9 Sentiment Analysis
		1.3.10 Data and Text Mining
		1.3.11 Predictive Analytics
		1.3.12 Prescriptive Analytics
		1.3.13 Unstructured Data Analytics
		1.3.14 Operational Analytics
		1.3.15 Data Visualization
		1.3.16 Data Mashups
	2. Activities
		2.1 Define Big Data Strategy and Business Needs
		2.2 Choose Data Sources
		2.3 Acquire and Ingest Data Sources
		2.4 Develop Data Hypotheses and Methods
		2.5 Integrate / Align Data for Analysis
		2.6 Explore Data Using Models
		2.6.1 Populate Predictive Model
		2.6.2 Train the Model
		2.6.3 Evaluate Model
		2.6.4 Create Data Visualizations
		2.7 Deploy and Monitor
		2.7.1 Expose Insights and Findings
		2.7.2 Iterate with Additional Data Sources
	3. Tools
		3.1 MPP Shared-nothing Technologies and Architecture
		3.2 Distributed File-based Databases
		3.3 In-database Algorithms
		3.4 Big Data Cloud Solutions
		3.5 Statistical Computing and Graphical Languages
		3.6 Data Visualization Tools
	4. Techniques
		4.1 Analytic Modeling
		4.2 Big Data Modeling
	5. Implementation Guidelines
		5.1 Strategy Alignment
		5.2 Readiness Assessment / Risk Assessment
		5.3 Organization and Cultural Change
	6. Big Data and Data Science Governance
		6.1 Visualization Channels Management
		6.2 Data Science and Visualization Standards
		6.3 Data Security
		6.4 Metadata
		6.5 Data Quality
		6.6 Metrics
		6.6.1 Technical Usage Metrics
		6.6.2 Loading and Scanning Metrics
		6.6.3 Learnings and Stories
	7. Works Cited / Recommended
Data Management Maturity Assessment
	1. Introduction
		1.1 Business Drivers
		1.2 Goals and Principles
		1.3 Essential Concepts
		1.3.1 Assessment Levels and Characteristics
		1.3.2 Assessment Criteria
		1.3.3 Existing DMMA Frameworks
		1.3.3.1 CMMI Data Management Maturity Model (DMM)
		1.3.3.2 EDM Council DCAM
		1.3.3.3 IBM Data Governance Council Maturity Model
		1.3.3.4 Stanford Data Governance Maturity Model
		1.3.3.5 Gartner’s Enterprise Information Management Maturity Model
	2. Activities
		2.1 Plan Assessment Activities
		2.1.1 Define Objectives
		2.1.2 Choose a Framework
		2.1.3 Define Organizational Scope
		2.1.4 Define Interaction Approach
		2.1.5 Plan Communications
		2.2 Perform Maturity Assessment
		2.2.1 Gather Information
		2.2.2 Perform the Assessment
		2.3 Interpret Results
		2.3.1 Report Assessment Results
		2.3.2 Develop Executive Briefings
		2.4 Create a Targeted Program for Improvements
		2.4.1 Identify Actions and Create a Roadmap
		2.5 Re-assess Maturity
	3. Tools
	4. Techniques
		4.1 Selecting a DMM Framework
		4.2 DAMA-DMBOK Framework Use
	5. Guidelines for a DMMA
		5.1 Readiness Assessment / Risk Assessment
		5.2 Organizational and Cultural Change
	6. Maturity Management Governance
		6.1 DMMA Process Oversight
		6.2 Metrics
	7. Works Cited / Recommended
Data Management Organization and Role Expectations
	1. Introduction
	2. Understand Existing Organization and Cultural Norms
	3. Data Management Organizational Constructs
		3.1 Decentralized Operating Model
		3.2 Network Operating Model
		3.3 Centralized Operating Model
		3.4 Hybrid Operating Model
		3.5 Federated Operating Model
		3.6 Identifying the Best Model for an Organization
		3.7 DMO Alternatives and Design Considerations
	4. Critical Success Factors
		4.1 Executive Sponsorship
		4.2 Clear Vision
		4.3 Proactive Change Management
		4.4 Leadership Alignment
		4.5 Communication
		4.6 Stakeholder Engagement
		4.7 Orientation and Training
		4.8 Adoption Measurement
		4.9 Adherence to Guiding Principles
		4.10 Evolution Not Revolution
	5. Build the Data Management Organization
		5.1 Identify Current Data Management Participants
		5.2 Identify Committee Participants
		5.3 Identify and Analyze Stakeholders
		5.4 Involve the Stakeholders
	6. Interactions Between the DMO and Other Data-oriented Bodies
		6.1 The Chief Data Officer
		6.2 Data Governance
		6.3 Data Quality
		6.4 Enterprise Architecture
		6.5 Managing a Global Organization
	7. Data Management Roles
		7.1 Organizational Roles
		7.2 Individual Roles
		7.2.1 Executive Roles
		7.2.2 Business Roles
		7.2.3 IT Roles
		7.2.4 Hybrid Roles
	8. Works Cited / Recommended
Data Management and Organizational Change Management
	1. Introduction
	2. Laws of Change
	3. Not Managing a Change: Managing a Transition
	4. Kotter’s Eight Errors of Change Management
		4.1 Error #1: Allowing Too Much Complacency
		4.1.1 Examples in Information Management Context
		4.2 Error #2: Failing to Create a Sufficiently Powerful Guiding Coalition
		4.3 Error #3: Underestimating the Power of Vision
		4.3.1 Example in Information Management
		4.4 Error #4: Under Communicating the Vision by a Factor of 10, 100, or 1000
		4.5 Error #5: Permitting Obstacles to Block the Vision
		4.6 Error #6: Failing to Create Short-Term Wins
		4.6.1 Examples in Information Management Context
		4.7 Error #7: Declaring Victory Too Soon
		4.7.1 Example in Information Management Context
		4.8 Error # 8: Neglecting to Anchor Changes Firmly in the Corporate Culture
		4.8.1 Example in Information Management Context
	5. Kotter’s Eight Stage Process for Major Change
		5.1 Establishing a Sense of Urgency
		5.1.1 Sources of Complacency
		5.1.2 Pushing up the Urgency Level
		5.1.3 Using Crisis with Care
		5.1.4 The Role of Middle and Lower-level Managers
		5.1.5 How Much Urgency is Enough?
		5.2 The Guiding Coalition
		5.2.1 The Importance of Effective Leadership in the Coalition
		5.2.2 Example in Information Management Context
		5.2.3 Building an Effective Team
		5.2.4 Combating Group Think
		5.2.5 Examples in Information Management Context
		5.2.6 Common Goals
		5.3 Developing a Vision and Strategy
		5.3.1 Why Vision is Essential
		5.3.2 The Nature of an Effective Vision
		5.3.3 Creating the Effective Vision
		5.4 Communicating the Change Vision
		5.4.1 Examples in Information Management Context
		5.4.2 Keeping it Simple
		5.4.3 Use Many Different Forums
		5.4.4 Repetition, Repetition, Repetition
		5.4.5 Walking the Talk
		5.4.6 Example in Information Management Context
		5.4.7 Explaining Inconsistencies
		5.4.8 Example in Information Management Context
		5.4.9 Listen and Be Listened To
		5.4.10 Example in Information Management Context
	6. The Formula for Change
	7. Diffusion of Innovations and Sustaining Change
		7.1 The Challenges to be Overcome as Innovations Spread
		7.2 Key Elements in the Diffusion of Innovation
		7.3 The Five Stages of Adoption
		7.4 Factors Affecting Acceptance or Rejection of an Innovation or Change
	8. Sustaining Change
		8.1 Sense of Urgency / Dissatisfaction
		8.2 Framing the Vision
		8.3 The Guiding Coalition
		8.4 Relative Advantage and Observability
	9. Communicating Data Management Value
		9.1 Communications Principles
		9.2 Audience Evaluation and Preparation
		9.3 The Human Element
		9.4 Communication Plan
		9.5 Keep Communicating
	10. Works Cited / Recommended
Acknowledgements
Index