I want to extend my gratitude to all the participants who joined and actively engaged in this session! 
During the masterclass, I conducted several live polls, and I’m excited to share the key insights below.
Key Takeaways
1⃣ Organizations Recognize the Need for Data Governance but Struggle with Implementation
60 percent of organizations are actively working on a data governance framework:
36 percent in development
24 percent in implementation
However, only 16 percent have a fully operational framework, highlighting challenges in moving from planning to execution.
The gap? Many organizations face obstacles such as:
Resource constraints
Lack of clear strategies
Difficulties embedding governance into daily operations
Additionally:
16 percent still lack a framework altogether
8 percent operate in an ad hoc manner
These findings underscore the need for structured governance approaches!
2⃣ Most Organizations Recognize the Need for Change in Their Data Governance Framework
The majority acknowledge that their framework is either insufficient or nonexistent:
48 percent need to adjust their existing framework
44 percent must start from scratch
Only 4 percent believe no changes are required, signaling that mature and fully effective governance frameworks remain rare.
Another 4 percent are unsure about their organization’s stance—suggesting a lack of clarity or awareness.
3⃣ Key Attention Points When Developing or Adjusting a Governance Framework
For organizations building or re-building their governance framework, critical considerations include:
The Role of Data Governance in Data Management
I believe that data governance and data management follow a yin-yang duality. Data governance defines why an organization must formalize a data management framework and its feasible scope. Data management, in turn, designs and establishes the framework, while data governance controls its implementation. Read more on my perspective here: Yin & Yang of Data Management & Governance
Key Factors Influencing the Data Management Framework Structure:
Business model: Focused vs. diversified
Organization size & geographic reach
Data architecture: Centralized vs. decentralized
Organizational structure & culture
Integration of (meta)data & AI practices
Compliance with Data & AI legislation
Scope of Data Governance to be Created/Adjusted:
Enterprise-wide framework – Operating model, governing bodies, organizational structure, and role design
Governance components for each capability – Policies, processes, artifacts, RACI roles, IT tool requirements
Coordination mechanisms between various data management capabilities
Join the Conversation!
This topic always sparks great discussions in my workshops. Want to dive deeper?
Paid Workshop: Harmonizing Governance Frameworks for Data & AI Management
Register here: 
https://us02web.zoom.us/meeting/register/0MVTYG-QQvewM_iuEahJQw
Let’s Connect! Share your thoughts in the comments or message me directly. 
The post Key Takeaways from the Free Masterclass: Adapting Data Governance for Modern Data Architecture appeared first on Solutions Review Thought Leaders.