To generate a data validation summary you can interact with the validation resource. The dataSet resource is optimized for data entry clients for validating a data set / form, and can be accessed like this:
In addition to validate rules based on data set, there are two additional methods for performing validation: Custom validation and Scheduled validation.
Custom validation can be initiated through the “Data Quality” app, where you can configure the periods, validation rule groups and organisation units to be included in the analysis and if you want to send out notifications for and/or persist the results found. The result of this analysis will be a list of violations found using your criteria.
The way server jobs was scheduled was changed in 2.29. It now has options for configuration. See the Scheduling api for more information.
The first path variable is an identifier referring to the data set to validate. XML and JSON resource representations are supported. The response contains violations to validation rules. This will be extended with more validation types in coming versions.
To retrieve validation rules which are relevant for a specific data set, meaning validation rules with formulas where all data elements are part of the specific data set, you can make a GET request to to validationRules resource like this:
The validation rules have a left side and a right side, which is compared for validity according to an operator. The valid operator values are found in the table below.
|not_equal_to||Not equal to|
|greater_than_or_equal_to||Greater than or equal to|
|less_than_or_equal_to||Less than or equal to|
|compulsory_pair||If either side is present, the other must also be|
|exclusive_pair||If either side is present, the other must not be|
The left side and right side expressions are mathematical expressions which can contain references to data elements and category option combinations on the following format:
The left side and right side expressions have a missing value strategy . This refers to how the system should treat data values which are missing for data elements / category option combination references in the formula in terms of whether the validation rule should be checked for validity or skipped. The valid missing value strategies are found in the table below.
|SKIP_IF_ANY_VALUE_MISSING||Skip validation rule if any data value is missing|
|SKIP_IF_ALL_VALUES_MISSING||Skip validation rule if all data values are missing|
|NEVER_SKIP||Never skip validation rule irrespective of missing data values|