Introduction
AI runs on data. This framework ensures data is trustworthy, accessible to those who need it, and protected from misuse. Without data governance, AI outputs cannot be trusted.
Phase | Guide |
Lens | Systems |
Purpose | Define data ownership, quality, and protection |
Output | Data Governance Framework |
Who's Involved | Data stewards, architects, compliance, security |
Duration | 4-8 hours |
Steps
Define data domains and assign ownership
Establish data quality standards and measurement
Specify access controls and security requirements
Create data lifecycle policies (retention, archival, deletion)
Define compliance and regulatory requirements
Design monitoring and remediation processes
Template
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