SDMX 2.1 transform level validation
i.e. if we receive 100 'rows / records' of data as an input, we output those 100 'rows / records'.
Ultimately we are implementing data quality metrics by monitoring for anomalies, outliers, missing values, or unexpected duplicates etc. to ensure the integrity and accuracy of the data.
By using the Notification component(s) we are alerting about the detected anomalies or issues for code or data remediation steps.
What to do:
Implement testing & validation sections in the sdmx 2.1 transform code to ensure the transform code was correctly written and the output tidy csv is correct.
Implement the notification components to signal where invalid transforms are captured / identified in the sdmx 2.1 transform code
Acceptance Criteria
[ ] Document designs in Confluence & in-line code comments.
[ ] Implement validation routines.
[ ] Implement the notification component(s) where issues need to be raised.
What is this?
SDMX 2.1 transform level validation i.e. if we receive 100 'rows / records' of data as an input, we output those 100 'rows / records'.
Ultimately we are implementing data quality metrics by monitoring for anomalies, outliers, missing values, or unexpected duplicates etc. to ensure the integrity and accuracy of the data.
By using the Notification component(s) we are alerting about the detected anomalies or issues for code or data remediation steps.
What to do:
Implement testing & validation sections in the sdmx 2.1 transform code to ensure the transform code was correctly written and the output tidy csv is correct.
Implement the notification components to signal where invalid transforms are captured / identified in the sdmx 2.1 transform code
Acceptance Criteria