Data drift refers to the phenomenon where the statistical properties of the data used to train machine learning models shift over time. This study examines the unique characteristics of various types of data drift and provides a systematic evaluation of the strengths and weaknesses of different detection methodologies. Furthermore, through simulations utilizing public administrative datasets, the research proposes practical measures to enhance the reliability and sustainability of data-driven governance within the health and welfare domain.