Corporate data Governance

Corporate Data Governance is the comprehensive framework of policies, processes, standards, and controls that ensures an organization's data is managed as a strategic asset. It establishes clear accountability for data, defining who can take what action, with which data, in what situations, and using what methods. This systematic approach aims to guarantee data quality, security, usability, and compliance, providing the trustworthy and reliable foundation essential for both the technical implementation of data systems in computer science and the extraction of meaningful insights in data science.

1. Introduction to Corporate Data Governance
   1. Defining Data Governance
      1. Historical Context of Data Governance
      2. Evolution from Data Management to Data Governance
      3. Key Objectives of Data Governance
      4. Scope of Data Governance in Organizations
      5. Data Governance vs Data Administration
   2. Data vs Information vs Knowledge
      1. Definitions and Distinctions
      2. Data Lifecycle Stages
      3. Transforming Data into Information
      4. Knowledge Creation from Information
      5. Wisdom as Applied Knowledge
   3. Data as a Strategic Asset
      1. Value Proposition of Data
      2. Data-Driven Decision Making
      3. Competitive Advantage through Data
      4. Data as an Organizational Resource
      5. Data Asset Valuation Methods
   4. Core Principles of Data Governance
      1. Accountability
      2. Transparency
      3. Integrity
      4. Stewardship
      5. Standardization
      6. Compliance
      7. Accessibility
      8. Security
   5. Business Drivers for Data Governance
      1. Risk Mitigation
         1. Data Breach Prevention
         2. Minimizing Operational Risks
         3. Reputational Risk Management
      2. Regulatory Compliance
         1. Legal Obligations
         2. Industry-Specific Regulations
         3. Audit Requirements
      3. Operational Efficiency
         1. Streamlining Data Processes
         2. Reducing Redundancy
         3. Cost Optimization
      4. Improved Decision-Making
         1. Enhancing Data Accessibility
         2. Supporting Analytics and Reporting
         3. Real-Time Decision Support
      5. Data Monetization
         1. Data as a Revenue Stream
         2. Data Sharing and Partnerships
         3. Data Product Development
   6. Relationship to Data Management
      1. Data Governance vs Data Management
      2. Integration with Data Management Functions
      3. Overlapping Responsibilities
      4. Complementary Roles