The Importance of Data Quality to Reinforce COVID-19 Vaccination Scheduling System: Study Case of Jakarta, Indonesia

Abstract

Vaccination is one of the solutions to reduce the spread of COVID-19. Jakarta Government collaborates with the Department of Population and Civil Registration and the Provincial Health Office to build a COVID-19 vaccination scheduling system to reinforce the vaccination process in Jakarta. The development process involves 3 major stakeholders, so it requires very intense coordination and data exchange. Department of population and civil registration provides population data as vaccination targets. This data has been integrated into the system of the Jakarta Government. However, some other data, such as the location and quota of vaccination from the Provincial Health Office is collected manually using a spreadsheet. Manual exchanging data tends to cause data is often inaccurate, incomplete, inconsistent, and duplicate. This study aims to measure data quality (DQ) of the COVID-19 vaccination scheduling system in Jakarta. This study uses Total Data Quality Management (TDQM). TDQM provides a common framework to facilitate understanding in data improvisation through data quality management. Measurement and analysis of the data on database of the system using a tool, Talend. The measurement discovers that completeness (null 60.80% and blank 21.36%), validity 92.18%, accuracy 99.11%, and uniqueness 99.38%. The result shows that some data were poor quality especially due to incomplete data.