Chapter 3. Using Data Quality functionality

Table of Contents

3.1. Overview of data quality checks
3.2. Data quality checks
3.3. Running Validation Rule Analysis
3.4. Std Dev Outlier Analysis
3.5. Min-Max Outlier Analysis
3.6. Gap Analysis
3.7. Follow-Up Analysis

The data quality module provides means to improve the accuracy and reliability of the data in the system. This can be done through validation rules and various statistical checks. All the functionality described below can be accessed from the left side menu in the Services->Data Quality module.

3.1. Overview of data quality checks

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 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 orgunits should be submitted at the appointed time.