Abstract <p>It is known that the accuracy of data analysis and artificial intelligence models that trained and tuned on the basis of data is closely related to the quality of the data set. The quality of the data set depends on several factors, one of the most important of which is the absence or elimination of anomalous data in the data set. Anomalous data has such a property that artificial intelligence models work normally with a data set with this anomalous data. That is, artificial intelligence models do not notice at all that they are working with incorrect data. As a result, the artificial intelligence model returns an incorrect result, which may lead to incorrect conclusions about the object. Therefore, today, the detection of anomalous data in the datasets is one of the studies that has retained its relevance. This research paper discusses anomalous data, their negative consequences, and the types of anomalies in the data set. It also studies methods for detecting anomalous data in datasets and analyzes their use cases.</p>