Governed data catalogues like Data360 Govern are crucial to building an effective AI governance framework. These platforms offer a centralized repository for managing and governing data assets, providing valuable insights and facilitating responsible AI development. Here’s how Data360 Govern can be leveraged to build an AI governance framework:

1. AI Business Case
2. Data Inventory
3. Data Quality
4. AI Policies
5. Business Glossary
6. Data Stewardship
7. Risk Management
8. Transparency
9. Reporting
10. Collaboration

1. AI Business Case and Impact

• Use Information Value Management to link AI products and models to business goals, objectives and outcomes
• Engage critical business stakeholders and communicate progress to meeting their KPIs
• Prioritise AI products and underlying data management foundation based on business impact

2. Data Inventory and Classification:

• Use Data360 Govern to catalogue all data assets relevant to AI development and deployment.
• Classify data by type, sensitivity, and purpose.
• Identify data lineage, tracing its origin and movement throughout the AI lifecycle.

3. Data Quality and Cleansing:

• Utilize Data360 Govern’s data quality features to assess data quality and identify potential biases.
• Implement data cleansing and transformation processes to ensure high-quality data for AI models.
• Monitor data quality metrics over time to track progress and identify areas for improvement.

4. Policy Management and Enforcement:

• Define and document data access and usage policies within Data360 Govern.
• Implement role-based access controls to ensure data security and privacy.
• Monitor and enforce compliance with data policies to minimize risk.

5. Business Glossary and Standardization:

• Create a centralized business glossary within Data360 Govern to standardize data definitions and terms.
• Ensure consistent data usage across different AI projects and initiatives.
• Facilitate collaboration and communication between business and technical stakeholders.

6. Data Stewardship and Accountability:

• Assign data stewards within Data360 Govern to oversee specific data assets.
• Define clear roles and responsibilities for data governance.
• Track data lineage and ownership to ensure accountability for data usage.

7. Risk Assessment and Mitigation:

• Use Data360 Govern’s risk assessment features to identify potential risks associated with AI development and deployment.
• Develop and implement mitigation strategies to address these risks.
• Monitor risk mitigation efforts and update strategies as needed.

8. Transparency and Explainability:

• Leverage Data360 Govern’s data lineage and lineage visualization tools to understand how data is used in AI models.
• Explain AI model outputs and decisions to stakeholders.
• Foster trust and accountability through transparent data governance practices.

9. Reporting and Monitoring:

• Generate reports and dashboards within Data360 Govern to track key data governance metrics.
• Conduct regular audits to ensure compliance with policies and regulations.
• Continuously monitor and improve data governance practices based on insights gained from reporting and monitoring.

10. Collaboration and Communication:

• Utilize Data360 Govern’s collaboration tools to facilitate communication between business, technical, and legal stakeholders.
• Share data assets and insights through the platform.
• Foster a culture of data-driven decision-making across the organization.

By effectively leveraging Data360 Govern, or similar tools, we can help your organization to build a robust AI governance framework that promotes responsible development, deployment, and use of AI. This leads to increased efficiency, reduced risk, and ultimately, success in the AI landscape.

Here are some additional resources that you may find helpful:

• Precisely Data360 Govern: https://www.precisely.com/product/precisely-data360/data360-govern
• Data Governance Institute: https://datagovernance.com/
• The Data Foundation: https://www.datafoundation.org/