Data Governance: The Definitive Guide

Spis treści książki

• Preface
  • Why Your Business Needs Data Governance in the Cloud
  • Framework and Best Practices for Data Governance in the Cloud
    • Data Governance Framework
    • Operationalizing Data Governance in Your Organization
    • The Business Benefits of Robust Data Governance
  • Who Is This Book For?
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. What Is Data Governance?
  • What Data Governance Involves
    • Holistic Approach to Data Governance
    • Enhancing Trust in Data
    • Classification and Access Control
    • Data Governance Versus Data Enablement and Data Security
  • Why Data Governance Is Becoming More Important
    • The Size of Data Is Growing
    • The Number of People Working and/or Viewing the Data Has Grown Exponentially
    • Methods of Data Collection Have Advanced
    • More Kinds of Data (Including More Sensitive Data) Are Now Being Collected
    • The Use Cases for Data Have Expanded
    • New Regulations and Laws Around the Treatment of Data
    • Ethical Concerns Around the Use of Data
  • Examples of Data Governance in Action
    • Managing Discoverability, Security, and Accountability
    • Improving Data Quality
  • The Business Value of Data Governance
    • Fostering Innovation
    • The Tension Between Data Governance and Democratizing Data Analysis
    • Manage Risk (Theft, Misuse, Data Corruption)
    • Regulatory Compliance
      • Regulation around fine-grained access control
      • Data retention and data deletion
      • Audit logging
      • Sensitive data classes
    • Considerations for Organizations as They Think About Data Governance
      • Changing regulations and compliance needs
      • Data accumulation and organization growth
      • Moving data to the cloud
      • Data infrastructure expertise
  • Why Data Governance Is Easier in the Public Cloud
    • Location
    • Reduced Surface Area
    • Ephemeral Compute
    • Serverless and Powerful
    • Labeled Resources
    • Security in a Hybrid World
  • Summary
• 2. Ingredients of Data Governance: Tools
  • The Enterprise Dictionary
    • Data Classes
    • Data Classes and Policies
      • Per-Use-Case Data Policies
    • Data Classification and Organization
    • Data Cataloging and Metadata Management
    • Data Assessment and Profiling
    • Data Quality
    • Lineage Tracking
    • Key Management and Encryption
      • A Sample Key Management Scenario
    • Data Retention and Data Deletion
    • Workflow Management for Data Acquisition
    • IAMIdentity and Access Management
    • User Authorization and Access Management
  • Summary
• 3. Ingredients of Data Governance: People and Processes
  • The People: Roles, Responsibilities, and Hats
    • User Hats Defined
      • Legal (ancillary)
      • Privacy tsar (governor)
        • Privacy tsar, work example 1: Community mobility reports
        • Privacy tsar, work example 2: Exposure notifications
      • Data owner (approver/governor)
      • Data steward (governor)
      • Data analyst/data scientist (user)
      • Business analyst (user)
      • Customer support specialists (user/ancillary)
      • C-suite (ancillary)
      • External auditor (ancillary)
    • Data Enrichment and Its Importance
  • The Process: Diverse Companies, Diverse Needs and Approaches to Data Governance
    • Legacy
    • Cloud Native/Digital Only
    • Retail
    • Highly Regulated
    • Small Companies
    • Large Companies
  • People and Process Together: Considerations, Issues, and Some Successful Strategies
    • Considerations and Issues
      • Hats versus roles and company structure
      • Tribal knowledge and subject matter experts (SMEs)
      • Definition of data
      • Old access methods
      • Regulation compliance
    • Processes and Strategies with Varying Success
      • Data segregation within storage systems
      • Data segregation and ownership by line of business
      • Creation of views of datasets
      • A culture of privacy and security
  • Summary
• 4. Data Governance over a Data Life Cycle
  • What Is a Data Life Cycle?
  • Phases of a Data Life Cycle
    • Data Creation
    • Data Processing
    • Data Storage
    • Data Usage
    • Data Archiving
    • Data Destruction
  • Data Life Cycle Management
    • Data Management Plan
      • Guidance 1: Identify the data to be captured or collected
      • Guidance 2: Define how the data will be organized
      • Guidance 3: Document a data storage and preservation strategy
      • Guidance 4: Define data policies
      • Guidance 5: Define roles and responsibilities
  • Applying Governance over the Data Life Cycle
    • Data Governance Framework
    • Data Governance in Practice
      • Data creation
      • Data processing
      • Data storage
      • Data usage
      • Data archiving
      • Data destruction
    • Example of How Data Moves Through a Data Platform
      • Scenario
  • Operationalizing Data Governance
    • What Is a Data Governance Policy?
    • Importance of a Data Governance Policy
    • Developing a Data Governance Policy
    • Data Governance Policy Structure
    • Roles and Responsibilities
    • Step-by-Step Guidance
    • Considerations for Governance Across a Data Life Cycle
      • Deployment time
      • Complexity and cost
      • Changing regulation environment
      • Location of data
      • Organizational culture
  • Summary
• 5. Improving Data Quality
  • What Is Data Quality?
  • Why Is Data Quality Important?
    • Data Quality in Big Data Analytics
    • Data Quality in AI/ML Models
  • Why Is Data Quality a Part of a Data Governance Program?
  • Techniques for Data Quality
    • Scorecard
    • Prioritization
    • Annotation
    • Profiling
      • Data deduplication
      • Data outliers
      • Lineage tracking
      • Data completeness
      • Merging datasets
      • Dataset source quality ranking for conflict resolution
  • Summary
• 6. Governance of Data in Flight
  • Data Transformations
  • Lineage
    • Why Lineage Is Useful
    • How to Collect Lineage
    • Types of Lineage
    • The Fourth Dimension
    • How to Govern Data in Flight
  • Policy Management, Simulation, Monitoring, Change Management
  • Audit, Compliance
  • Summary
• 7. Data Protection
  • Planning Protection
    • Lineage and Quality
    • Level of Protection
    • Classification
  • Data Protection in the Cloud
    • Multi-Tenancy
    • Security Surface
    • Virtual Machine Security
  • Physical Security
    • Network Security
    • Security in Transit
  • Data Exfiltration
    • Virtual Private Cloud Service Controls (VPC-SC)
    • Secure Code
    • Zero-Trust Model
  • Identity and Access Management
    • Authentication
    • Authorization
    • Policies
    • Data Loss Prevention
    • Encryption
    • Differential Privacy
    • Access Transparency
  • Keeping Data Protection Agile
    • Security Health Analytics
    • Data Lineage
    • Event Threat Detection
  • Data Protection Best Practices
    • Separated Network Designs
    • Physical Security
    • Portable Device Encryption and Policy
    • Data Deletion Process
      • Electronic medical device and OS software upgrades
      • Data breach readiness
  • Summary
• 8. Monitoring
  • What Is Monitoring?
  • Why Perform Monitoring?
  • What Should You Monitor?
    • Data Quality Monitoring
      • Process and tools for monitoring data quality
    • Data Lineage Monitoring
      • Process and tools for monitoring data lineage
    • Compliance Monitoring
      • Process and tools for monitoring compliance
    • Program Performance Monitoring
      • Process and tools for monitoring program performance
    • Security Monitoring
      • Process and tools for monitoring security
  • What Is a Monitoring System?
    • Analysis in Real Time
    • System Alerts
    • Notifications
    • Reporting/Analytics
    • Graphic Visualization
    • Customization
  • Monitoring Criteria
  • Important Reminders for Monitoring
  • Summary
• 9. Building a Culture of Data Privacy and Security
  • Data Culture: What It Is and Why Its Important
  • Starting at the TopBenefits of Data Governance to the Business
    • Analytics and the Bottom Line
    • Company Persona and Perception
  • Intention, Training, and Communications
    • A Data Culture Needs to Be Intentional
      • Whats important
    • Training: Who Needs to Know What
      • The who, the how, and the knowledge
      • Communication
      • Top-down, bottom-up, and everything in between
  • Beyond Data Literacy
    • Motivation and Its Cascading Effects
      • Motivation and adoption
  • Maintaining Agility
    • Requirements, Regulations, and Compliance
    • The Importance of Data Structure
    • Scaling the Governance Process Up and Down
  • Interplay with Legal and Security
    • Staying on Top of Regulations
    • Communication
    • Interplay in Action
    • Agility Is Still Key
  • Incident Handling
    • When Everyone Is Responsible, No One Is Responsible
  • Importance of Transparency
    • What It Means to Be Transparent
    • Building Internal Trust
    • Building External Trust
    • Setting an Example
  • Summary
• A. Googles Internal Data Governance
  • The Business Case for Googles Data Governance
  • The Scale of Googles Data Governance
  • Googles Governance Process
  • How Does Google Handle Data?
    • Privacy SafeADH as a Case Study
• B. Additional Resources
• Index

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