Data parallelism is a technique in computing where a dataset is divided into smaller chunks, and computations on these chunks are executed simultaneously across multiple processors to expedite processing time. It is commonly used in parallel computing environments to optimize performance, especially in tasks like machine learning model training and large-scale data processing.