Data quality management: a vital foundation for robust monitoring and evaluation

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Data quality management: a vital foundation for robust monitoring and evaluation

Data quality is a cornerstone of accountability in program reporting. Although we are often focused on reporting in the international development sector, ensuring the quality of the data that we report is critical for our credibility with our partners, our donors, and our beneficiaries. Data quality management is an essential requirement for a strong monitoring and evaluation system; however, it is not often understood how to best adhere to these standards.

This Module seeks to explain data quality management concepts in a straight-forward, easy to understand manner, incorporating practical exercises that build toward the formulation of your program’s data quality management plan. This Module was updated and revised in 2014 to reflect field experience with Routine Data Quality Assessments (RDQAs) and Pact’s own internal expertise in improving data quality. It introduces concepts of data quality, how to ensure data quality in all stages of the data cycle, how to implement RDQAs, and how to ultimately construct data quality management plans for programs to follow.

The Module also recommends use of a slightly modified version of the MEASURE Evaluation’s RDQA Excel-based tool that was created with input from a consortium of organizations including the Global Fund to Fight Aids, Tuberculosis and Malaria, Office of the Global AIDS Coordinator, PEPFAR, USAID, WHO, and UNAIDS. The tool evaluates indicator data quality as well as data management and reporting systems for the central program, intermediary reporting sites, and partner organizations. We adapted the tool to allow for assessing up to four indicators per RDQA, added some extra questions corresponding to data quality concepts, and neutral language so the tool can be applicable to all types of programs, not only programs focusing on health.  Answering questions in the tool creates automated dashboards with charts for a holistic review of data quality at all levels of your program that can serve as the basis of action plans for strengthening data quality.

So what are you waiting for?  Get out there and check on the quality of your data! 

 

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