Data augmentations are important for training high-performance 3D object detectors that use point clouds. Despite recent efforts on designing new data augmentations, perhaps surprisingly, most current state-of-the-art 3D detectors only rely on a few simple data augmentations. In particular, different from 2D image data augmentations, 3D data augmentations need to account for different representations of input data and require being customized for different models, which introduces significant overhead. In this paper, we propose LidarAugment, a practical and effective data augmentation strategy for 3D object detection. Unlike previous methods, which require tuning all augmentation policies in an exponentially large search space, we propose to factorize and align the search space of each data augmentation, which cuts down the 20+ hyperparameters to 2, and significantly reduces the search complexity. We show LidarAugment can be easily adapted to different model architectures with different input representations by a simple 2D grid search, and consistently improve a range of detectors including both convolution-based UPillars/StarNet/RSN and transformer-based SWFormer. Furthermore, Lidar Augment mitigates overfitting and enables 3D detectors to scale up to larger capacities. When combined with the latest 3D detectors, Lidar Augment achieves a new state-of-the-art 74.8 mAPH L2 on the Waymo Open Dataset.