Data Quality
The measure of data's fitness for its intended use in AI systems, encompassing accuracy, completeness, consistency, timeliness, validity and uniqueness.
In plain language
How good your data is for training AI. Garbage in, garbage out; if your data is incomplete, outdated or full of errors, your AI will learn the wrong things.
Why this matters
Data quality is the foundation of trustworthy AI and directly affects model reliability and fairness. Your governance framework must include data quality standards, measurement processes and accountability structures because poor data quality is the root cause of many AI failures and biases.
Relevance
ImplementationData quality is a critical control point that determines whether AI systems will perform reliably and fairly; requires ongoing measurement and governance throughout the system lifecycle.
Related terms
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