Concept
Data Quality
The degree to which data is fit for its intended operational, analytical, or AI use.
Position in the Trusted Data Framework
Data Quality
Living Graph View
Data Quality in the concept network
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Relationship Matrix
Data Quality as a network node
Uses metadata to understand how data was created and whether it is fit for use.
Provides measurable evidence that data can be relied on.
Reduces poor AI outputs caused by weak source information.
Concept Relationships
How Data Quality works with other concepts
Uses metadata to understand how data was created and whether it is fit for use.
Provides measurable evidence that data can be relied on.
Reduces poor AI outputs caused by weak source information.
Definition
Data Quality covers accuracy, completeness, timeliness, consistency, validity, uniqueness, and fitness for purpose.
Role in the Trusted Data Framework
Acts as a trust gate before information becomes knowledge or decision support.
Practical Examples
- Completeness checks
- Duplicate detection
- Validity rules
- Freshness monitoring
Prototype Direction
- ai-ready-assessment
Consulting Questions
- What problem does this concept help diagnose in the client environment?
- Which upstream concepts must be in place before this concept becomes reliable?
- What evidence would prove that this concept is working in practice?