Data Imputation is a statistical technique used to fill in missing values in a dataset. When data is incomplete, it can lead to biased results or reduced accuracy in analyses. Imputation helps maintain the integrity of the dataset by estimating the missing values based on available information. There are various methods for data imputation, including mean substitution, where the average of existing values is used, and more complex techniques like multiple imputation or k-nearest neighbors. Choosing the right method depends on the nature of the data and the extent of the missing values.