Data Quality

Data quality
Data quality monitors enable teams to quickly detect when features, predictions, or actual data points don't conform to what is expected.

Superwise offers you a number of Data quality monitoring policy templates:

Missing Values

You can monitor for missing values at the feature level for any segment.

Metric

Role

Segments

missing values

All Features role

Entire set

Out-of-Range

You can monitor for anomalies as a percent of outliers at the feature level (numeric only).

Metric

Role

Segments

outliers

All Numeric Features

Entire set

New Values

Superwise can also monitor anomalies as a percent of new values at the feature level (categorical only).

Metric

Role

Segments

new values

All Categorical Features

Entire set

Fixed Value

detect features that are constant on the entire set level

Metric

Role

Segments

std / entropy / prop

All Features role

Entire set


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