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Segmentation in Validio lets you validate metrics per segment of your data, similar to a GROUP BY in SQL, enabling granular analysis that catches anomalies hidden by aggregation. For step-by-step instructions on creating segmentations, see Segmentation Configuration.
The Segmentations tab of a source listing its configured segmentations

Segmentations configured on the gold__sales_summary source

Information loss occurs when aggregating data. Segmenting the data allows you to perform a more granular analysis. In Validio, you can configure Segmentation to validate metrics on segments of your data. You can also use Filters to narrow the records included in each segment, and Windows to control how data is aggregated. Segmentation works similar to GROUP BY operations in SQL. For example, if a Segmentation is specified for the field Marital status, metrics are validated for each distinct value within that field.
Segmentation with Non-Null valuesValidio displays only the Segmentations with non-Null values. Fields that include a Null value will not be shown.
A source table segmented on categorical fields, with one example tracking average age by marital status and another tracking average annual salary by education

Segmentation allows you to validate data within segments of your data.

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Multi-field segments

You can create Segmentations on multiple fields. For example, if a Segmentation is specified for the fields Country, Gender, and Marital status, the metric Average of "Annual salary" is validated for each combination of distinct values within Country, Gender, and Marital status.
Performance ImpactsThough it’s not uncommon to have thousands of segments, you can expect large numbers of segments to impact performance and metric retention.
Segmentation on the Country, Gender, and Marital status fields, tracking median annual salary for each combination of values

Example Segmentation on the three fields: Country, Gender, and Marital status, tracking average Annual salary for each of the segments.

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Segmentation example

A retail organization wants to validate their price data, to make sure their products are properly priced. For validating purposes, they want to use the fields price and currency. Because of differences in currency, the prices have different orders of magnitude, which means that only validating datapoints from the price column makes little sense. The data must be segmented based on currency, before performing a data quality validation to make sure there are no anomalies, Think of the difference in the order of magnitude if the same price for a specific item is expressed in USD versus Iranian Rial, where the conversion rate is ~ 1 USD = ~ 40 000 Iranian Rial. If no Segmentation is applied to validate price data in currency, the retail organization would be comparing apples with cars.
A metric graph for the Currency = IRR segment with an anomaly detected outside the threshold bounds

Example on an anomaly detected in the Currency = IRR segment.