Data Quality
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Last updated
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Most companies detect issues after their team has used bad data to make decisions or trigger campaigns. Quickly take action on every invalid event with in-app reporting and daily email digests.
In the interface, you will be able to define the schema of your data and define the validation rules that will feed your data quality workflow.
Writing event specification allows you to automate the QA process, to feed the in the dashboard, but also to define realtime alerts to react quickly when errors occur on your data.
To react quickly to data errors, while your IT team corrects the problem at source, you can rely on the , aka .
Having a good data quality on each source is essential, but being able to check also the is at least as important. For each destination, you can view the , quickly identify errors and define with a personnalized thresold.
In case of doubt or to further investigate a data problem, you can access the logs of the events sent. You benefit both from and to search for a specific event, analyse it's data and better understand the issue.
