Data Quality Management

Data Quality Fundamentals

High-quality data is essential for reliable analysis.

Dimensions

Completeness: no missing values. Accuracy: correct values. Consistency: no contradictions. Timeliness: up-to-date.

Profiling

Data profiling analyzes patterns: distributions, nulls, duplicates. Great Expectations: Python data quality library.

pandas-profiling: automatic profiling report.

Validation

Schema validation: correct types, formats. Range validation: values in expected range. Reference validation: foreign key integrity.

Key Takeaways

1. Quality dimensions: completeness, accuracy, consistency
2. Profiling reveals data characteristics
3. Validation prevents bad data