Page Comparison

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Tools - EA, SA, Archimate, Plantuml, MDM repo ( NOT commercial ones but maybe integrated w Oracle )

RajG on Data Governance 

Deloitte on Data Governance Challenges

https://www.linkedin.com/posts/rajkgrover_datagovernance-generativeai-dataprivacy-activity-7145740183915720704-FQyX?utm_source=share&utm_medium=member_desktop

Top 15 #DataGovernance Challenges in Context of the #GenerativeAI and How to Mitigate Them
 
1.    Bias and Fairness
2.    Ethical Use and Misinformation
3.    #DataPrivacy
4.    #DataQuality Assurance
5.    Explainability
6.    Transparency
7.    Verification and Authenticity
8.    Regulatory Compliance
9.    #DataSecurity Risks and Malicious Use
10. #IntellectualProperty and Copyrights Issues
11. End-User Accountability
12. #HumanAICollaboration Guidelines
13. Public Perception and Trust
14. Community and Stakeholder Engagement
15. Real Time Monitoring and Control
 
Addressing these data governance challenges requires multidisciplinary, holistic and proactive approach that involves collaboration between data scientists, ethicists, legal experts, and stakeholders.
 
How to Mitigate the Above Challenges:
 
1.    Bias Audits
2.    Diverse Training Data
3.    #ExplainableAI
4.    Transparency Guidelines
5.    Anonymization and Privacy Preserving Techniques
6.    Human Review of the Quality Assurance
7.    Encryption and Access Controls
8.    Regular Audits by Legal Expertise
9.    Clear Ownership Policies
10. Intervention Protocols for Real Time Monitoring
 
Image Source: Deloitte
 

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RajG Data Governance Concepts for Generative AI

Why is #datagovernance important in #GenerativeAI?
Data governance plays a pivotal role in fostering #innovation in the evolving AI landscape by ensuring responsible data practices, mitigating biases, and safeguarding privacy. A robust data governance strategy is the key to unlocking the full potential of your Generative AI use cases.

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Other DG and EDM Concepts

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