If your data quality dashboard shows green and your AI system is still underperforming, the problem is not your data. It is what you are measuring.
1. Schema Stability Rate
How often does a dataset schema change without prior notice? Every undocumented change is a potential silent failure. High-performing data organizations keep unannounced breaking changes below 5%.
2. Semantic Drift Score
The same field name can mean different things in different systems. Customer ID in the CRM may not join cleanly to Customer ID in the warehouse. Semantic drift measures the gap between how a field is documented and how it is actually used.
3. Contract Violation Rate
What percentage of pipeline runs trigger a contract violation? Zero violations is not the goal. The goal is that violations are caught before they reach downstream consumers. A low violation rate with enforcement is far safer than zero violations with no enforcement.
4. Feature Freshness Lag
How old is the data when it reaches the model? A freshness SLA defined in a contract is enforceable. Without it, you are relying on hope. Measure the P99 lag for every feature your production models consume.
5. Business Logic Coverage
What percentage of critical business rules are expressed as tested, version-controlled code versus undocumented tribal knowledge? This is the most predictive metric for long-term AI reliability. Organizations that shift to these measures see 50% reduction in build time and 75% reduction in maintenance effort.
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