What is Data Quality Monitoring in ML Systems?

Definition
Data quality monitoring validates that input data meets expected standards before reaching ML models, catching issues like missing values, schema violations, and value range anomalies.
WHY DATA QUALITY MATTERS
ML models are only as good as their data. A model trained on clean data will fail silently when fed garbage. Unlike traditional software that crashes on bad input, ML models produce predictions—they just produce wrong predictions.
Data quality issues are the leading cause of ML system failures in production. Studies show 60-80% of ML pipeline failures trace back to data issues: missing features, schema changes, value range violations, or upstream pipeline failures.
QUALITY DIMENSIONS
Completeness: Are all expected values present? Null rates, missing features, partial records.
Consistency: Do values follow expected formats and constraints? Categorical values match expected set, numerical values in valid ranges.
Timeliness: Is data fresh enough? Stale data (features computed on old data) can be as harmful as missing data.
Accuracy: Do values represent reality? Hardest to measure—requires ground truth comparison or domain knowledge.
DATA QUALITY VS DATA DRIFT
Data quality and data drift are related but distinct. Data quality issues are bugs—the data is wrong. Data drift is the world changing—the data is correct but different from training. A feature becoming all nulls is a quality issue. A feature distribution shifting because user behavior changed is drift.
Different responses: quality issues need bug fixes; drift needs model adaptation. Detecting which you are dealing with is crucial for taking the right action.
💡 Key Insight: Data quality monitoring is your first line of defense. Catch bad data before it reaches the model, rather than debugging wrong predictions later.

💡 Key Takeaways

✓60-80% of ML pipeline failures trace to data issues: missing features, schema changes, value range violations

✓Quality dimensions: completeness (nulls), consistency (formats), timeliness (freshness), accuracy (correctness)

✓Quality issues are bugs needing fixes; drift is the world changing needing model adaptation—distinguish them

📌 Interview Tips

1Interview Tip: Explain why ML models fail silently on bad data—they produce predictions, just wrong ones.

2Interview Tip: Distinguish data quality from data drift: nulls = quality issue; distribution shift = drift.

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