# Data Quality Monitoring

> Continuous validation and monitoring of data quality metrics in production pipelines.

- **Category**: Data Science
- **Subcategory**: Data Quality
- **Canonical URL**: https://designpattern.fyi/patterns/data-quality-monitoring/

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## Description
**Context**: Poor data quality leads to incorrect insights and model failures. Continuous monitoring ensures data meets quality standards throughout the pipeline.


## Use Cases
Production data pipelines where data quality directly impacts business decisions and model performance.



## Implementation Example

```python
# Data Quality Monitoring class DataQualityMonitor: def __init__(self): self.rules = []
def add_rule(self, rule): self.rules.append(rule)
def validate(self, data): issues = [] for rule in self.rules: if not rule.check(data): issues.append(rule.description) return issues
class CompletenessRule: def __init__(self, column): self.column = column self.description = f"Missing values in {column}"
def check(self, data): return data[self.column].notna().all()
```



## Trade-offs


### Advantages

- - Early detection of data issues

- - Improved trust in data

- - Automated quality enforcement

- - Reduced manual inspection




### Considerations & Drawbacks

- - Additional infrastructure

- - Alert fatigue if not tuned properly

- - False positives/negatives

- - Maintenance overhead







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**Reference**: [Original Source](https://www.greatexpectations.io/)