Data annotation is the process of labeling data with relevant information to make it useful for machine learning algorithms. It involves adding annotations, tags, or labels to raw data to help categorize, classify, and train models. Data annotation can be done manually by human annotators or through automated tools. Some common types of data annotation include image labeling, text classification, sentiment analysis, object detection, and speech recognition. Data annotation is crucial for improving the accuracy and effectiveness of machine learning models, and it plays a critical role in many applications such as natural language processing, computer vision, and speech recognition.