Data labeling is assigning meaningful tags or categories to raw data — images, text, audio, or video — so supervised learning models can learn from it. A label might mark an image as containing a cat, tag sentiment as positive, or identify an object's bounding box. Labeling is often the most time-consuming step in an AI project, which is why companies use labeling platforms, crowdsourcing, and AI-assisted annotation to scale.