| Table of Contents |
|---|
Key Points
...
| Reference_description_with_linked_URLs_______________________ | Notes______________________________________________________________ |
|---|---|
What_is_Data_Governance_and_Why_Does_it_Matter_updated.pdf link |
...
pdf link What_is_Data_Governance_and_Why_Does_it_Matter_updated.pdf. file | |
dbx_data-and-ai-governance-6sept2023.pdf. - databricks link dbx_data-and-ai-governance-6sept2023.pdf. - databricks file | |
Key Concepts
What_is_Data_Governance_and_Why_Does_it_Matter_updated.pdf link
Data governance (DG) is the process of managing the availability, usability, integrity and
security of the data in enterprise systems, based on internal data standards and policies that
also control data usage. Effective data governance ensures that data is consistent and
trustworthy and doesn't get misused. It's increasingly critical as organizations face new data
privacy regulations and rely more and more on data analytics to help optimize operations
and drive business decision-making.
A well-designed data governance program typically includes a governance team, a steering
committee that acts as the governing body, and a group of data stewards. They work
together to create the standards and policies for governing data, as well as implementation
and enforcement procedures that are primarily carried out by the data stewards. Ideally,
executives and other representatives from an organization's business operations take part,
in addition to the IT and data management teams.
While data governance is a core component of an overall data management strategy,
organizations need to focus on the expected business benefits of a governance program for
it to be successful,
Data Governance problems
Without effective data governance, data inconsistencies in different systems across an organization might not get resolved. For example, customer names may be listed differently in sales, logistics and customer service systems. That could complicate data integration efforts and create data integrity issues that affect the accuracy of business intelligence (BI), enterprise reporting and analytics applications. In addition, data errors might not be identified and fixed, further affecting BI and analytics accuracy.
Data Governance programs
compliance with data privacy and protection laws, such as the European Union's GDPR and the California Consumer Privacy Act
(CCPA). An enterprise data governance program typically includes the development of
common data definitions and standard data formats that are applied in all business systems,
boosting data consistency for both business and compliance uses.
<<key < FACTUR3DT.IO - measure Data Governance Value in the organization against defined OKRs, KPIs in the Data Governance Program
<<key < key benefits possible include
data access controls
compliance with data management policies and regulations
easy data availability for users and services
automation of data management tasks
data valuation based on usage vs objectives
dbx_data-and-ai-governance-6sept2023.pdf. - databricks link
Jim DG, EDM keys
My career is managing data anywhere: goalie, janitor, whisperer, therapist
...
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
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.
Other DG and EDM Concepts
...