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Book Description

As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency.

With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You’ll explore how to create a strategy and tooling to support the democratization of data and governance principles.

Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how.

  • Enable auditable legal and regulatory compliance with defined and agreed-upon data policies
  • Employ better risk management
  • Establish control and maintain visibility into your company’s data assets, providing a competitive advantage
  • Drive top-line revenue and cost savings when developing new products and services
  • Implement your organization’s people, processes, and tools to operationalize data trustworthiness

Table of Contents

  1. 1. What Is Data Governance?
    1. What Data Governance Involves
      1. Holistic Approach to Data Governance
      2. Enhancing Trust in Data
      3. Classification and Access Control
      4. Data Governance vs. Data Enablement and Data Security
    2. Why Data Governance Is Becoming More Important
      1. Size of Data Is Growing
      2. The Amount of People Working the Data and/or Viewing the Data Has Grown Exponentially
      3. Methods of Data Collection Have Advanced
      4. More kinds of data (including more sensitive data) are now being collected.
      5. The Use Cases for Data Have Expanded
      6. New Regulations and Laws Around the Treatment of Data
      7. Ethical Concerns Around the Use of Data
    3. Examples of Data Governance in Action
      1. Managing Discoverability, Security, and Accountability
      2. Improving Data Quality
    4. The Business Value of Data Governance
      1. Fostering Innovation
      2. The Tension Between Data Governance and Democratizing Data Analysis
      3. Manage Risk (Theft, Misuse, Data Corruption)
      4. Regulatory Compliance
      5. Considerations for Organizations as They Think About Data Governance
    5. Why Data Governance Is Easier in the Public Cloud
      1. Location
      2. Reduced Surface Area
      3. Ephemeral Compute
      4. Serverless and Powerful
      5. Labeled Resources
      6. Security in a Hybrid World
      7. Organization of This Book
  2. 2. Ingredients of Data Governance: Tools
    1. The Enterprise Dictionary
      1. Enterprise Dictionary: Data Classes
      2. Enterprise Policy Book
      3. Per-Use Case Data Policies
    2. Data Classification and Organization
    3. Data Cataloging and Metadata Management
    4. Data Assessment and Profiling
    5. Data Quality
    6. Lineage Tracking
    7. Key Management and Encryption
      1. A Sample Key Management Scenario
    8. Data Retention and Data Deletion
    9. Workflow Management for Data Acquisition
    10. IAM—Identity and Access Management
    11. User Authorization and Access Management
    12. Summary
  3. 3. Ingredients of Data Governance: People and Processes
    1. Overview
    2. The People: Roles, Responsibilities and “Hats”
      1. User “Hats” Defined
      2. An Important Consideration: Data Enrichment and its Importance
    3. The Process: Diverse Companies, Diverse Needs and Approaches to Data Governance
      1. Legacy
      2. Cloud Native/Digital Only
      3. Retail
      4. Highly Regulated
      5. Small Companies
      6. Large Companies
    4. People + Process Together: Considerations, Issues, and Some Successful Strategies
      1. Considerations and Issues
      2. Processes and Strategies with Varying Success
    5. Conclusion
  4. 4. Data Governance Over a Data Life Cycle
    1. What is a data lifecycle?
    2. Phases of a data lifecycle
      1. Data Creation
      2. Data Processing
      3. Data Storage
      4. Data Usage
      5. Data Archiving
      6. Data Destruction
    3. Data lifecycle management
      1. Data management plan
    4. Applying governance over the data lifecycle
      1. Data governance framework
      2. Data governance in practise
      3. Example scenario
    5. Operationalizing data governance
      1. What is a data governance policy?
      2. Importance of a data governance policy
      3. Developing a data governance policy
      4. Data governance policy structure
      5. Roles and responsibilities
      6. Step-by-step guidance
      7. Considerations for governance across a data lifecycle
    6. Conclusion
      1. References/ Resources
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