Data Quality

The goal of monitoring and evaluation (M&E) systems is to produce data that are used to document progress towards goals and objectives and to improve health programs. However, the data produced by these systems is often incomplete, inaccurate, and tardy, due to insufficient capacity in the health system, or inadequate system design.

MEASURE Evaluation understands that data must be of high quality if they are to be relied upon for making good decisions on health policy, health programs, and allocation of scarce resources. Data give the picture of what is happening; bad data call the entire system into question. MEASURE Evaluation conducts data quality assessments and builds capacity to generate and use high-quality data.

Our tools help countries evaluate the capacity of their M&E systems to collect, compile, and report quality data for planning, measure the accuracy of reporting priority indicators, and troubleshoot data quality issues. We also offer training materials to help countries assess data quality and conduct research to help institutionalize data quality assurance techniques. Resources include:

  • The Data Quality Review (DQR) toolkit represents a collaborative effort of World Health Organization (WHO), The Global Fund, Gavi, and the USAID-funded MEASURE Evaluation project to promote a harmonized approach to assessing the quality of data reported from the health facility to the national level.
  • Our Data Quality Audit (DQA) tool permits formal auditing of data quality for priority HIV/AIDS, TB, and malaria indicators in programs or projects. It was developed for the President’s Emergency Plan for AIDS Relief (PEPFAR) and the Global Fund as an integral part of performance-based measure of data accuracy for selected indicators. MEASURE Evaluation has also produced a capacity-building and self-assessment version of the tool for health programs, NGOs, and donor-funded projects (the Routine Data Quality Assurance [RDQA] tool).
  • Our training curriculum, centered on the RDQA tool, has facilitator and participant guides, exercises, and case studies for a three-day training course. MEASURE Evaluation has conducted such trainings in Africa, Latin America, and Asia.

Finally, MEASURE Evaluation has documented an effort to integrate data quality assurance activities into standard operating procedures (SOPs) for HMIS at the country level. MEASURE Evaluation worked with the Government of Botswana to formalize data quality assurance for different levels of the health system and to develop policies and procedures to track and continuously improve data quality. These efforts have been documented in a case study on Botswana’s integration of data quality assurance into standard operating procedures.

Related Content

Data Quality Tools

Routine Health Information System (RHIS) Curriculum

PRISM: Performance of Routine Information System Management

Data Quality Course from the Global Health eLearning Center

mHealth for Health Information Systems in Low- and Middle-Income Countries – Challenges and Opportunities in Data Quality, Privacy, and Security

Scaling mHealth for Community-Based Health Information Systems – Lessons and Best Practices

Building Leadership for Data Demand and Use: A Facilitator's Guide

Tools for Data Demand and Use in the Health Sector: Quick Guide

Comparative Analysis of Data Quality Assessment Tools

Country-Led, Holistic Data Quality Assurance: Institutionalizing Data Quality through a National Technical Working Group and the Data Quality Review