Using Data Quality Audit as a Strategy for Improved Sexual Reproductive Health and Rights Programming
Year: 2022
Type: article
Abstract: Data Quality Audit is a critical process that entails constant assessment of a program’s data, identifies gaps, and informs correction for improved data quality. A majority of donor-funded programs rely on targets and reporting on achievements at the end of the implementation period to track progress. For this reason, it is critical for such institutions to report data that is accurate and complete, as this informs the next steps of the program in achieving the program’s aim. The quality of data generated from a program is a critical function of the program’s M&E systems and data verification processes. Tropical Institute of Community Health and Development (TICH) implemented a project for young people- Get Up Speak Out (GUSO) with an aim to achieve enjoyment of young people’s sexual and reproductive health and rights. The TICH-GUSO project adopted the USAID Guidelines to evaluate the M&E system, data verification processes and the data quality reported by program outcomes. The DQA was done twice at an interval of six months. Each DQA process entailed a two-stage process that entailed objective measurement of the M&E system and a data verification process to assess the data accuracy and completeness. The DQA process was done at the institution (data centre), where all the primary and secondary data are stored. The DQA process evidenced that audit and feedback facilitate learning and improvement. The second DQA recorded an improvement across all the sectors (M&E system, data verification process, and data quality). DQA processes are critical components of program implementation since they help identify weaknesses hence informing the type of correctional intervention needed to produce quality data, reports, and evidence for strengthening program implementation, future programming, policy recommendation and further research where needed. It is primary that programs and institutions at large adopt DQA processes for continuous improvement.
Language: English
Topic: Data Quality and Management
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4281740720