To enhance accuracy in diverse applications, many engineers have been embracing data-centric AI which differs from the traditional model-centric approach. What seems to be a prompt for most engineers to reassess their priorities and workflows is the abundance of available data and recognition of the advantages offered by reliable data. By acknowledging that a model's performance heavily relies on the quality of the training data, this emphasis on data has empowered engineers to enhance model accuracy without engaging in the repetitive process of constantly adjusting parameters.