Data Governance & Quality
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# Data Governance & Quality ## 🎯 Learning Objectives - Define data governance and its organizational importance - Assess data quality across multiple dimensions - Understand GDPR, CCPA, and data privacy requirements - Implement data cataloging and lineage tracking ## 📖 Core Content ### 6.1 Data Governance Framewor...
Data Governance & Quality
🎯 Learning Objectives
- Define data governance and its organizational importance
- Assess data quality across multiple dimensions
- Understand GDPR, CCPA, and data privacy requirements
- Implement data cataloging and lineage tracking
📖 Core Content
6.1 Data Governance Framework
(Diagram)
6.2 Data Quality Dimensions
| Dimension | Definition | Measurement |
|---|---|---|
| Accuracy | Data reflects reality | % records matching truth source |
| Completeness | All required data present | % non-null for critical fields |
| Consistency | Same data across systems | % matching values across sources |
| Timeliness | Data is current | Age of data, update frequency |
| Uniqueness | No duplicates | % duplicates detected |
| Validity | Adheres to format rules | % records passing validation |
6.3 Data Privacy Regulations
| Regulation | Scope | Key Requirements |
|---|---|---|
| GDPR | EU residents | Consent, right to be forgotten, data portability |
| CCPA | California residents | Right to know, opt-out, deletion |
| HIPAA | US healthcare | Protected health information safeguards |
| PCI-DSS | Payment card data | Encryption, access control, monitoring |
📝 Practice Questions
Q1: What is a data steward vs data owner?Data Owner: Senior executive accountable for a data domain (e.g., VP of Sales owns customer data). Makes decisions about data usage, quality standards, and access. Data Steward: Operational role implementing data policies. Monitors quality, resolves issues, documents metadata. Data owners decide WHAT; data stewards execute HOW. Q2: Why is data lineage important?Data lineage tracks the origin and transformations of data. It helps: (1) debugging data quality issues (find where errors were introduced), (2) regulatory compliance (prove data provenance), (3) impact analysis (if a source system changes, what reports are affected), (4) building trust in data by showing its journey. Q3: What is the "right to be forgotten" under GDPR?Individuals can request deletion of their personal data from an organization's systems. The organization must delete data across all systems (production, backups, data lakes) within a specified timeframe. This creates technical challenges: how do you delete from immutable backups? How do you handle machine learning models trained on that data? Join Discord PreviousNoSQL DatabasesNextBI Tools