Knowledge Distillation (KD) has been established as an effective technique for reducing the resource requirements of models when tackling computer vision tasks. Prior work has studied how to distill the knowledge of a teacher model better, but it overlooks how data affects the distillation result. This work examines the impact of data in knowledge distillation from two perspectives: (i) quantity of knowledge and (ii) quality of knowledge. Our examination finds that faster knowledge distillation can be achieved by using data with a large amount of high-quality knowledge in distillation. Based on the findings, this work proposes an efficient adaptive sampling method called KDAS for faster knowledge distillation, which enhances the distillation efficiency by selecting and applying 'good' samples for the distillation. This work shows that our adaptive sampling methods can effectively accelerate the training efficiency of a student model when combined with existing KD methods.