A value is considered an outlier if it deviates significantly from most other values in the column. The presence of outliers can have a skewing effect on the data and can interfere with the effectiveness of transformations such as normalization or binning.
Outlier treatment methods such as trimming or clipping can be implemented to minimize the effect of outliers.
Outliers represent problematic data, for example, a bad reading due to the abnormal condition of an instrument. However, in some cases, especially in the business arena, outliers are perfectly valid. For example, in census data, the earnings for some of the richest individuals can vary significantly from the general population. Do not treat this information as an outlier, since it is an important part of the data. You need domain knowledge to determine outlier handling.