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Anomaly Analysis

The Anomaly Details modal provides specific details about the Anomaly, including source record and checks that failed assertions, along with ability to take Notes and provide feedback to ML methods through updating Status of the anomaly.

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Users have the ability to provide feedback to the ML methods through Supervised Learning. Specifically, the user can Acknowledge, Resolve or Invalidate an anomaly. Each of these actions will change the tolerances of the data quality checks behind the anomaly.

Anomaly Header

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  1. The # ID of the anomaly: 32102
  2. The Datastore name: SAP Financial Exports
  3. The Location where this data is stored: s3a://qualytics-demo-data/orders/demo/customer-20220223.csv
  4. The Record Type of the anomaly: Shape
  5. The Tags are labels that serve the purpose of grouping anomalies and driving downstream workflows: Migration Low
  6. The Status of the selected Anomaly: Users can edit the status to Acknowledged, Resolved or Invalid.
  7. The Detected at of the anomaly creation time: 1 year ago

Failed Checks:

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All the fields of that specific anomaly found: company_name


Rule type that failed the assertion(s)

You can check all the Rule types here.


Details of how the anomaly failed the assertion(s) of rule(s)

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The expected tolerance of the rule

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Infered or Authored

Coverage Screenshot Screenshot

The expected tolerance of the rule


The rule type Tag Screenshot Screenshot

Source Records

Show in a tabular view all the records and fields and highlight records with anomaly data based on the rule type.

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Comparison Source Records

Anomalies generated by data quality checks of rule type Is Replica Of with Row Identifiers configured, show a tailored source record visualization that provides more comparison information about each row retrieved accounting for row key matching.

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Each field is divided between left or right, which represents respectively the target and reference table/file. When the right value differs the left value the cell is highlighted to indicate an anomalous value.

The _qualytics_diff column informs the status of each row, which can be one of the following:

Row Status Description
added Row missing in the left side but found in the right side.
removed Row found in the left side but missing in the right side.
changed Row found on both sides but there is at least one field value differing.

Infered Check Details

  • Clicking into a Rule will highlight its details and enables users to edit the rule as well:

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  • An anomaly can be archived pressing the Check Actions menu and Archive.

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  • If you expand the section Advanced Options you can add a Filter clause and also change the Coverage percentage for that anomaly.


The Filter clause is a WHERE statement against your table. For example:
price != 33 or price > discount + 20


You can create a computed table and use multiple fields from different tables in a filter clause

Suggested Remediation

Qualytics can also provide you a suggested value


Download Source Records

Download the source records for further analysis or external use.

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Last update: May 30, 2024