Data Quality

Purpose

Catch bad data before it reaches a dashboard, a model, or a customer. A pipeline that silently propagates corrupt data is worse than one that fails, because the failure is discovered downstream, later, by someone who trusts the number.

When to Use

• Ingesting data from a source you do not control.
• Building quality gates into a pipeline.
• Investigating a metric that looks wrong.
• Auditing a dataset before it is used for analysis or training.

Capabilities