1. A method for imputing a missing value for each of one or more predictor variables, comprising: with each mapper from a set of mappers that receives data from a different data source, building an imputation model based on information of a target variable and that data source; with a reducer, randomly extracting validation samples from each different data source to create a global validation sample; with each mapper, scoring the imputation model built at that mapper based on the global validation sample and the imputation model built at each other mapper; with the reducer, selecting a top number of imputation models based on the scoring of each imputation model to form an ensemble model; and with each mapper, determining a type of imputation model to construct; imputing the missing value for each of the one or more predictor variables using the data from each different data source, each ensemble model, and the determined type of imputation model to generate a completed data set; and performing prediction, discovery, and interpretation of relationships between the target variable and the one or more predictor variables using the completed data set.