In the age of machine learning and data-driven scientific computing, computational scientists and mathematicians want to solve larger and larger computational problems. To meet this need, researchers have developed sketching, a randomized dimensionality reduction technique that promises to solve large linear algebra problems with ease. Sketching has been widely used and studied, yet questions remain about when and if it works. This talk will critically investigate the efficacy of sketching for least-squares problems. After demonstrating deficiencies of some sketching-based methods, this talk will present new research showing that the iterative sketching method is fast, accurate, and stable for least-squares problems. This answers the title question "Does sketching work?" with a qualified "yes”.