Get started quickly with a pre-built data quality metrics dashboard

Schema changes like new attributes are added or removed

Incomplete data received like null values, empty strings, NA etc

Volume drifts like lower or higher row counts than expected.

Alert on unexpected duplicate values.

Identify drifts in statistical distribution for categorical.

Syntactical patterns for well formatted attributes like SSN, & Zip Code

Validate acceptable values for a controlled lists like ISO codes, ICD codes, Gender , Address_Type etc

Flag inaccurate data based on multiple metrics like numeric values, is_email? , is_URL, length of strings, tokens etc.

Users defined custom metrics and expectations using our proprietary Human-in-loop approach.