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