The increasing use of data driven analysis techniques in data science & AI requires a great deal of expert knowledge. When the data are incomplete, this knowledge must also include the exact specification of statistical imputation models to solve for the missingness. This project proposes mindless imputation, a data-driven automated technique for solving incomplete data problems that minimizes the need for user intervention. With mindless imputation, applied researchers can focus on the analysis, knowing that the used imputation technique allows for valid inferences and reliable predictions in the context of incomplete data.