One of the biggest data problems is not lack of data. It’s lack of understanding. When teams don’t know what data means, where it comes from, or how it should be used, trust breaks down and AI becomes risky. I’ve just added a new resource to the classroom: Metadata & Lineage Starter Map This template helps you: - clarify business definitions, - map data flows end to end, - reduce misinterpretation, - build explainable and reusable data foundations. You can access it now in the Data Governance from Scratch classroom. As always, start small and focus on impact.

Hey team! 👋 I am putting the final touches on the "CDMP Accelerator: Pass in 4 Weeks course". My goal is simple: turn this 600-page monster (the DMBOK) into a clear battle plan to get certified in under a month. But to make this surgical, I don't want to guess. I want to build exactly what you need right now. To help me calibrate the content (More basics? More exam traps? More strategy?), please tell me honestly where you are in your CDMP journey: 👇 Click the option that matches your current status:

Hello friends,I wanted to share a reflection sparked by a recent talk from Seun Ebenezer from Greencon on GenAI assisted data stewardship. One idea really stuck with me: GenAI in governance is no longer a future concept. It is already here. But its success depends less on the technology itself and much more on foundations like data quality, shared semantics, and human accountability. As we say in Spanish, you cannot build the house from the roof. No data. No AI. I would love to hear how others in this group are thinking about human in the loop models, AI ready data, and the shift from reactive to proactive governance. What is working in practice, and where are you still seeing friction? Looking forward to learning from your perspectives. https://lnkd.in/dgMk3cBg

Given the current state where application data is distributed across MongoDB (NoSQL/document store) and PostgreSQL (relational/SQL), we recommend a federated data governance model that respects each database's strengths while establishing unified governance controls. This approach balances centralized policy with decentralized implementation. 2. Guiding Principles Database-Agnostic Policies: Define governance requirements independent of underlying technology Proportional Controls: Implement controls based on data sensitivity, not database type Unified Metadata Layer: Single source of truth for data lineage, classification, and ownership Minimal Application Impact: Governance implementation should not require major application rewrites 3. Proposed Governance Framework 3.1 Organizational Structure Data Governance Council (Strategic) ↓ Data Stewards (Business Domain Owners) ↓ Database Custodians (Technical Implementation) ↓ Data Consumers (Application Teams)

Yesterday I had the chance to attend a DAMA-led webinar discussing recent work developed in collaboration with Gaia-X. For those less familiar with the European context, Gaia-X is an initiative focused on enabling federated and trustworthy data spaces, allowing organizations to share and reuse data across ecosystems while maintaining control, meaning, and accountability. The discussion reinforced a key idea I see often in practice: data governance is not a blocker, but an enabler. Shared semantics, clear ownership, and fit-for-purpose quality are what make collaboration and AI at scale actually work. I’ve shared a short reflection inspired by the webinar and my own experience below. Looking forward to hearing how these topics are being approached in different regions and organizations. https://www.linkedin.com/pulse/data-governance-enabler-practitioners-reflection-trust-sopalda-ffuqf