4 Control data quality

4.1 About data quality checks

The Data Quality app contains tools to validate the accuracy and reliability of the data in the system. You can assess different dimensions of data quality as outlined in the table below:

Dimension

Description

Correctness

Data should be within the normal range for data collected at that facility. There should be no gross discrepancies when compared with data from related data elements.

Completeness

Data for all data elements for all reporting organisation units should have been submitted.

Consistency

Data should be consistent with data entered during earlier months and years while allowing for changes with reorganization, increased work load, etc. and consistent with other similar facilities.

Timeliness

All data from all reporting organisation units should be submitted at the appointed time.

You can verify data quality in different ways, for example:

  • At point of data entry, DHIS 2 can check the data entered to see if it falls within the minimum maximum value ranges of that data element (based on all previous data registered).

  • By defining validation rules, which can be run once the user has finished data entry. The user can also check the entered data for a particular period and organization unit(s) against the validation rules, and display the violations for these validation rules.

  • By analysing data sets, that is, examine gaps in the data.

  • By data triangulation, that is, comparing the same data or indicator from different sources.