Webinar Summary: Data Governance 101
Hosted by the Data Privacy and Governance Society of Kenya
1. Speaker Introduction
- Kibwana (Speaker):
- 20+ years in banking, specializing in data governance/stewardship.
- Certifications: CDMP (in progress), AI/cloud governance, data ethics.
- Experience: Trained executives, presented at international forums (e.g., CDO Conference, South Africa).
2. Key Concepts
A. Data Governance vs. Data Management
| Aspect | Data Governance | Data Management |
|---|---|---|
| Scope | Policies, frameworks, accountability. | Operational handling (storage, quality, lifecycle). |
| Objective | Ensure trust, compliance, ethics. | Ensure accessibility, usability. |
| Tools | RACI metrics, stewardship models. | Master Data Management (MDM), data warehouses. |
B. Why Data Governance Matters
- Ensures data quality (accuracy, completeness).
- Facilitates regulatory compliance (GDPR, Kenya DPA).
- Mitigates risks (breaches, silos).
- Drives data-driven culture (literacy, accountability).
C. Common Pitfalls
- Lack of executive buy-in.
- Treating governance as an IT project (vs. business strategy).
- Overly ambitious scope (start small: 1 dataset at a time).
3. Frameworks & Models
A. DAMA DMBOK (14 Knowledge Areas):
- Data Quality, Metadata, Security, Warehousing, etc.
- Pyramid Approach: Foundation → Advanced Analytics (AI/ML).
B. Peter Aiken’s Pyramid (4 Phases):
- Phase 1: Basic integration (databases).
- Phase 2: Data quality + architecture.
- Phase 3: Governance + MDM.
- Phase 4: AI/ML maturity.
C. Tools
- Microsoft Purview: Cataloging, lifecycle management.
- OpenMetadata (open-source): Lineage, quality tracking.
4. Implementation Roadmap
30-60-90 Day Plan:
- 30 Days: Assess goals, map data sources, identify pain points.
- 60 Days: Establish governance council, draft policies, prioritize 1 dataset.
- 90 Days: Fix data errors, measure impact, iterate.
Maturity Journey:
- Short-term (1–2 yrs): Foundational policies, quick wins.
- Medium-term (3–5 yrs): Scale MDM, deploy analytics.
- Long-term (5+ yrs): AI-driven insights, full data literacy.
5. Q&A Highlights
Q1: Is Microsoft Purview enough for governance?
- A: No. Supplement with tools like OpenMetadata for lineage/quality.
Q2: Where should SMEs start?
- A: Begin with 1 critical dataset (e.g., customer data). Address quality gaps first.
Q3: How to secure leadership buy-in?
- A: Tie governance to business outcomes (e.g., cost savings, compliance).
6. Closing
- Key Takeaway: Data governance is a continuous journey, not a one-time project.
- Recording/Slides: Shared post-webinar.
- Next Session: Deep-dive into AI governance (August 12).
Host: “Thank you, Kibwana and attendees! Let’s build a data-accountable future.”
Poll Results:
- 45% attendees had prior governance experience.
- 55% were new to the topic.
