Clean data is essential for meaningful analysis, but data quality challenges often begin long before analysis starts.
What happens when a data entry user leaves a field blank? Does it mean the information is unknown, was not provided, or something else?
In this video, I explore why validation rules matter and demonstrate how to create them in DHIS2.
Validation rules can help identify inconsistencies in data, supporting greater transparency and trust in the information we use to make decisions.
Watch the tutorial:
How have you used validation rules in your DHIS2 implementation?