Chapter 9. Setting up Data Quality functionality

Table of Contents

9.1. Overview of data quality check
9.2. Data quality checks
9.3. Data quality check at the point of data entry
9.3.1. Setting the minimum and maximum value range manually
9.3.2. Generated min-max values
9.4. Validation Rule
9.5. Validation Rule Group
9.6. Scheduled Validation Runs

The data quality module provides means to improve the quality of the data in the system. This can be done through validation rules and various statistical checks.

9.1. Overview of data quality check

Ensuring data quality is a key concern in building an effective HMIS. Data quality has different dimensions including:

  • 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 health facilities/blocks/Taluka/districts 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 health facilities/blocks/Taluka/districts should be submitted at the appointed time.