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) tool, developed by the World Health Organization (WHO) and MEASURE Evaluation, evaluates data quality across program areas as a part of a larger assessment of health facility service availability and readiness. It provides DQR guidance documents, data collection tools, training materials, and an Excel-based tool to facilitate assessments.
  • 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 facilities (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 Nepal.

Finally, MEASURE Evaluation has documented an effort to integrate data quality assurance activities into standard operating procedures (SOPs) at the country level. For example, 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 for Monitoring and Evaluation

Routine Data Quality Assessment Tool (RDQA): Guidelines also available in FrenchPortuguese, and Spanish

Routine Data Quality Assessment (RDQA) Curriculum Materials

Multi-Indicator Routine Data Quality Assessment Tool (M-RDQA): Excel Workbook also available in FrenchPortuguese and Spanish

More Data Quality Assurance Tools

Data Quality Course from the Global Health eLearning Center