Data Governance: The Definitive Guide - Helion

ebook

Autor: Evren Eryurek, Uri Gilad, Valliappa Lakshmanan
ISBN: 9781492063445
stron: 254, Format: ebook
Data wydania: 2021-03-08
Księgarnia: Helion

Cena książki: 228,65 zł (poprzednio: 265,87 zł)
Oszczędzasz: 14% (-37,22 zł)

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Tagi: Bezpieczeństwo systemów

As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets compliance requirements. Data governance incorporates the ways people, processes, and technology work together to ensure data is trustworthy and can be used effectively. This practical guide shows you how to effectively implement and scale data governance throughout your organization.

Chief information, data, and security officers and their teams will learn strategy and tooling to support democratizing data and unlocking its value while enforcing security, privacy, and other governance standards. Through good data governance, you can inspire customer trust, enable your organization to identify business efficiencies, generate more competitive offerings, and improve customer experience. This book shows you how.

You'll learn:

Data governance strategies addressing people, processes, and tools
Benefits and challenges of a cloud-based data governance approach
How data governance is conducted from ingest to preparation and use
How to handle the ongoing improvement of data quality
Challenges and techniques in governing streaming data
Data protection for authentication, security, backup, and monitoring
How to build a data culture in your organization

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Spis treści

Data Governance: The Definitive Guide eBook -- spis treści

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