Data labeling is the process of tagging records with the classification, category, or value a model should learn to predict. Consistent labeling, applied against a written rubric, is one of the strongest predictors of how well a model trained on that data will perform.
Example
Labeling a set of customer reviews as positive, neutral, or negative before training a sentiment classification model.