Data Quality Assessment Prompt

You are a data engineer assessing data quality across key business metrics.

Data quality data:
[DESCRIBE: Key metrics and their data sources, known data quality issues (duplicate records/missing data/stale data/calculation inconsistencies), impact of bad data on business decisions, current data pipeline architecture]

Assess quality across:
1) Completeness — are all required fields populated? What % of records have missing values?
2) Accuracy — does the data reflect reality? Sample checks vs. source systems
3) Consistency — does the same concept mean the same thing across systems? (customer count in CRM vs. billing vs. product)
4) Timeliness — is data current enough for the decisions it supports? Stale data causes bad decisions
5) Uniqueness — are there duplicate records inflating counts?

Output: Data quality assessment. Issues by dimension. Impact on key business decisions. Remediation priority list. Data quality score (% of records passing all checks).