Treat data quality as a lifecycle
Apply data quality dimensions across the full lifecycle of a Python automation.
The move: validate for the decision your automation supports. Data quality is not a generic score. It is whether the data is fit for this use. A churn dashboard, payroll export, and email list each need different checks because each fails in a different way. Plan Ask what bad input would make the output misleading. Pick the dimensions that matter: completeness, validity, consistency, timeliness, uniqueness, or accuracy. Run Validate early. Separate rejected records from transformed records. Log counts and reasons so someone can act. Communicate Do not hide quality issues behind a clean chart. Show what was excluded, what was…
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