Key Points
References
| Reference_description_with_linked_URLs_______________________ | Notes______________________________________________________________ |
|---|---|
What_is_Data_Governance_and_Why_Does_it_Matter_updated.pdf link | |
Key Concepts
Jim DG, EDM keys
My career is managing data anywhere: goalie, janitor, whisperer, therapist
Role keys- alignment on IT architecture, plans, priorities, business partnerships internal & external
SDP - virtual teams > discovery > assessment > plan > design > test > train > rollout > support
From IT EA, EDM, Digital Transformation programs, create related program for EDG that is aligned, integrated w BUs, clients, vendor services to meet goals on EDM quality & services OKRs, DT, Compliance ( internal and external audits )
EDM - Enterprise Data Mgt - 4 R keys - data & services are: RIGHT, RELIABLE, RESPONSIVE, REACTIVE across the enterprise, clients and vendor services
DTCC DLT Architect part of EA services tower w other IT lines > governance support on DLT, data
EDM app services, infrastructure, support tools
Collibra for MDM or ?
Couchbase for distributed NoSQL w JDBC tools, reporting
Pulsar & Solace on global events
DLT w Firefly, Fabric
MySQL, Postgres, Oracle, Snowflake, MongoDB, Aws Aurora
Messaging > Kafka, MQ, ActiveMQ, Artemis ?
Tools - EA, SA, Archimate, Plantuml, MDM repo ( NOT commercial ones but maybe integrated w Oracle )
RajG on Data Governance
Deloitte on Data Governance Challenges
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
Who should own Data Quality?
Ownership of data quality is a critical aspect of effective #datamanagement within an organization. While the specific team or role that owns data quality may vary depending on the organizational structure, size, and industry, there are several common approaches:
#DataGovernance Team:
Role: A dedicated Data Governance Team or Office is responsible for defining and enforcing data management policies, including data quality standards.
Responsibilities:
-Establishing data quality policies and procedures.
-Defining data quality metrics and benchmarks.
-Monitoring and enforcing data quality standards.
#DataStewardship Team:
Role: Data stewards are individuals or a team responsible for the management and oversight of specific sets of data.
Responsibilities:
-Ensuring data quality at the operational level.
-Resolving data quality issues and discrepancies.
-Collaborating with business units to improve data quality.
IT or Data Management Team:
Role: The IT or Data Management Team, including database administrators and data engineers, may be responsible for technical aspects of data quality.
Responsibilities:
-Implementing data quality tools and technologies.
-Monitoring and optimizing data quality processes.
-Collaborating with business units to understand data requirements.
#BusinessAnalysts or #DataAnalysts:
Role: Business analysts or data analysts who work closely with business units and understand data requirements can play a role in ensuring data quality.
Responsibilities:
-Profiling and analyzing data to identify quality issues.
-Collaborating with data stewards to address data quality concerns.
-Participating in the definition of data quality rules.
Quality Assurance (QA) Team:
Role: In organizations with a strong QA function, the QA team may be involved in ensuring data quality for systems and applications.
Responsibilities:
-Applying QA principles to data-related processes.
-Conducting data validation and testing.
-Collaborating with data owners and stewards.
Business Units and Data Owners:
Role: In a decentralized model, business units or data owners may have ownership of data quality for the data they generate and use.
Responsibilities:
-Defining and maintaining data quality requirements.
-Taking ownership of data quality improvement initiatives.
-Collaborating with data stewards and IT teams.
#ChiefDataOfficer (#CDO) or Chief Analytics Officer (CAO):
Role: The CDO or CAO may have a strategic role in setting the overall vision for data quality and ensuring alignment with business goals.
Responsibilities:
-Setting the strategic direction for data quality.
-Advocating for data quality best practices.
-Collaborating with executive leadership to prioritize data quality initiatives.
Image Source: Eckerson Group
Analytics COE Models for Enterprises
Sample Digital Transformation Project Roadmap Checkpoints
Potential Value Opportunities
Potential Challenges
Candidate Solutions
Step-by-step guide for Example
sample code block