DQ Workbench documentation

Welcome! This document explains how to install and use the DQ Workbench tool for data quality monitoring in DHIS2. The DQ Workbench is a tool that allows you to define and run data quality stages, which can be used to monitor the integrity of your metadata, detect outliers, and validate data against predefined rules.

Getting started

• Installation
  • Intended workflow
  • Windows installer
  • Docker
  • Conda
  • Python virtual environment
  • Flask secret key
• Building the documentation
  • Prerequisites
  • Setup instructions
  • Building HTML
  • Building Slides
  • Building PDF
  • Cleaning
• Configuration
  • Server configuration
  • Maximum concurrent requests
  • Maximum results per request
  • Multiple root organisation units
  • Using environment variables for secrets
  • Creating a dedicated user account
  • Sample configuration file (UI view)

Monitoring

• 1. Integrity stages
  • 1.1. Create a new integrity stage
  • 1.2. Create missing integrity data elements
• 2. Outlier stages
  • 2.1. Defining a new outlier stage
• 3. Validation rule stages
  • 3.1. Defining a new validation rule stage

Min-max values

• 1. Min-max generation
  • 1.1. Editing a min-max generation stage

Indices and tables

• Index

• Search Page