🧭 Data Governance and Quality
Info
Data governance ensures that data remains private, accurate, secure, available, and usable. It defines how data is managed throughout its lifecycle using roles, policies, and processes.
👥 Key Roles and Concepts
- Data Stewards: Experts who maintain data quality and integrity
- Governance Framework: Policies for data access, usage, storage, and disposal, aligned with laws and standards
📊 Dimensions of Data Quality
| Dimension | Description |
|---|---|
| Accuracy | Matches a trusted source |
| Completeness | Sufficient for decisions |
| Consistency | Aligned across systems |
| Timeliness | Available when needed |
| Validity | Meets formatting or domain expectations |
| Uniqueness | No unnecessary duplicates |
Tip
Maintain data quality by defining policies, securing access, and auditing regularly.
✅ Benefits
- Supports compliance and data-driven decisions
- Increases trust and reduces risk
- Maintains high data integrity
🌍 Data Sovereignty and Governance in the Cloud
Info
As cloud adoption grows, understanding where data resides is critical for legal and compliance reasons.
🌐 Key Concepts
| Term | Meaning |
|---|---|
| Data Sovereignty | Data must comply with the laws of its storage location |
| Data Residency | Geographical location of stored data |
| Data Localization | Law requiring local data storage |
Note
Sovereignty laws vary by country—cloud providers must support region-specific controls.
🛠️ Governance Actions
- Conduct data audits
- Choose providers with regional support
- Map sovereignty requirements and align policies
🎯 Benefits
- Reduces regulatory risk
- Ensures lawful data storage and handling
- Supports operational resilience and transparency
🔍 Data Discovery
Info
Data discovery involves identifying and analyzing data across the organization to uncover insights, assess risk, and support governance.
📌 Key Concepts
| Concept | Description |
|---|---|
| Data Discovery | Locating and analyzing data across systems |
| Data Governance | Ensuring lawful, secure data management |
| Data Sovereignty | Legal compliance with storage regulations |
Warning
Sensitive Data Discovery:
- Focus on PII, financial records, PHI, IP
- Tools: DLP engines (scan, mask, classify)
- Ensure encryption, access control, and retention compliance
💡 Example Use Cases
- Financial institutions mask sensitive data in email
- Healthcare providers protect PHI during cloud migration
- Multinationals enforce sovereignty by region using audits and encryption
✅ Benefits
- Enhances visibility and transparency
- Validates secure data storage in the cloud
- Reduces misclassification and governance gaps
🗂️ Data Retention
Info
Data retention refers to storing data for a defined period to meet legal, regulatory, or business needs.
📌 Core Concepts
| Concept | Description |
|---|---|
| Data Retention | Storing data for a specific duration |
| Retention Period | Defined storage timeline |
| Data Retention Policy | Rules governing storage, access, and disposal |
Check
Steps to create a data retention policy:
- Research legal and industry requirements
- Consult legal teams
- Align with business needs
- Design for flexibility and enforcement
Danger
Risks:
- Over-retained data increases breach exposure
- Expired data should be deleted or replaced
- Cloud retention settings must be automated and enforced
✅ Benefits
- Reduces liability
- Ensures compliance
- Improves data lifecycle management
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