At the start: You might be starting out without any data, or with a ton of data but no clear sense of which parts of it are useful to your particular problem. It’s not uncommon to have a lot of data, but to be lacking accurate labels for that data. So you can start and get great value from bootstrapping your model: label some data, train your model, use your model to suggest labels allowing you to speed up your labeling, iterating on and on in this way. Labeling data early on in the process also helps clarify and condense down your specific rules and standards. For example, you might realize that you need to have specific definitions for certain concepts so that your labeling efforts are consistent across your team.