Data Quality & AI Performance
Data Quality is the Heart of Successful AI
Understanding data quality impact on AI performance and techniques for high-quality data creation
issues from poor data
performance improvement
data preparation time
project success rate
"Garbage In, Garbage Out" Principle
Core Concept
AI performance directly depends on data quality. No matter how sophisticated the algorithm, poor quality data leads to inaccurate results
Good Data = Accurate AI
High-quality data leads to reliable results
Bad Data = Wrong Results
Poor quality data causes AI to make wrong decisions
Key Statistics
Data Quality Dimensions
Accuracy
Data must correctly reflect reality
- • Verify correctness
- • Fix errors
- • Double-check validation
- • Reliable reference data
Completeness
Data is complete as required
- • No missing data
- • Cover all cases
- • Sufficient data
- • Handle null values
Consistency
Data must be consistent across systems
- • Same format
- • Same units
- • No contradictions
- • Same standards
Timeliness
Data must be current and timely
- • Latest data
- • Regular updates
- • Relevant timeframe
- • Prevent stale data
Validity
Data must be in correct format and range
- • Correct format
- • Appropriate range
- • Business rules
- • Constraint validation
Relevance
Data must be relevant to the purpose
- • Select important data
- • Filter irrelevant data
- • Prioritize data
- • Analyze requirements
Ready to Build High-Performance AI Systems?
Start with high-quality data and build AI that delivers accurate results