Data Governance
The overall management of data availability, usability, integrity, and security in an organization. It includes policies, standards, and practices for how data is collected, stored, and used.
Why It Matters
Strong data governance is the prerequisite for trustworthy AI. You cannot have responsible AI without responsible data practices.
Example
A company's data governance program requiring data classification, access controls, retention policies, and lineage tracking for all datasets used in AI training.
Think of it like...
Like city planning for data — zoning laws (policies), building codes (standards), and inspections (audits) ensure everything is organized, safe, and well-maintained.
Related Terms
Data Quality
The degree to which data is accurate, complete, consistent, timely, and fit for its intended use. Data quality directly impacts the reliability and performance of AI models.
Data Privacy
The right of individuals to control how their personal information is collected, used, stored, and shared. In AI, data privacy concerns arise from training data, user interactions, and model outputs.
AI Governance
The frameworks, policies, processes, and organizational structures that guide the responsible development, deployment, and monitoring of AI systems within organizations and across society.
Compliance
The process of ensuring AI systems meet regulatory requirements, industry standards, and organizational policies. AI compliance is becoming increasingly complex as regulations proliferate.
Data Lineage
The tracking of data's origins, transformations, and movements throughout its lifecycle. Data lineage answers the question 'Where did this data come from and what happened to it?'