Anomaly Detailsmodal provides specific details about the Anomaly, including source record and checks that failed assertions, along with ability to take
Notesand provide feedback to ML methods through updating
Statusof the anomaly.
Users have the ability to provide feedback to the ML methods through Supervised Learning. Specifically, the user can
Invalidatean anomaly. Each of these actions will change the tolerances of the data quality checks behind the anomaly.
# IDof the anomaly: 69962
Datastorename: AzureSQL - Consolidated Balance
Locationwhere this data is stored: qualytics.consolidated_balance.bank
Record Typeof the anomaly: Shape
Tagsare labels that serve the purpose of grouping anomalies and driving downstream workflows: High
Statusof the selected Anomaly: Users can edit the status to
Detected atof the anomaly creation time: 02/21/23 5:40 PM
Failed Fields details:
Field: all the fields of that specific anomaly found: currency
Rule: rule type that failed the assertion(s) You can check all the Rule types here.
Violation: details of how the anomaly failed the assertion(s) of rule(s)
Coverage: the expected tolerance of the rule
The rule type
Show in a tabular view all the records and fields and highlight records with anomaly data based on the rule type
Users can filter by Field in this view
Infered check details
- Clicking into a
Rulewill highlight its details and enables users to edit the rule as well:
An anomaly can be archived via the button.
If you expand the section
Advanced Optionsyou can add a
Filterclause and also change the
Coveragepercentage for that anomaly.
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
Qualytics can also provide you a suggested value