Data Quality Framework — Systematic Approach to Data Quality

Systematic approach to data quality is foundation of trustworthy analytics. Six quality dimensions, automated checks and processes for continuous improvement.

Six Dimensions of Data Quality¶

• Completeness — missing values (% non-null)
• Uniqueness — duplicates (% unique keys)
• Validity — values in allowed range/format
• Accuracy — correctness against reality
• Consistency — agreement between systems
• Timeliness — data is sufficiently current

Data Quality Score¶

# DQ score calculation
def calculate_dq_score(checks_results):
    passed = sum(1 for c in checks_results if c.passed)
    total = len(checks_results)
    return (passed / total) * 100

# Example output:
# Completeness: 99.8%
# Uniqueness:   100%
# Validity:     98.5%
# Timeliness:   100%
# Overall DQ Score: 99.6%

Automation¶

• Prevention — schema enforcement, validation during ingestion
• Detection — Great Expectations, Soda, dbt tests
• Alerting — Slack/email on control failures
• Remediation — automatic fixing or quarantine

Summary¶

DQ framework with six dimensions, automated checks and DQ score ensures systematic data quality management.