Abstract: Managing and applying human resources data comprising aggregating employee transaction data for an organization. A number of human resources-related attributes are evaluated across heterogeneous transaction data. The employee transaction data is classified via statistical machine learning into a number of normalized codes according to the human resources-related attributes, a user interface is presented to adjust a number of organizational operating procedures according to the normalized codes.

Abstract: A predictive benchmarking of job wages is provided. Wage data is collected from a number of sources and preprocessed, wherein the wage data comprises a number of dimensions. A wide linear part of a wide-and-deep model is trained to emulate benchmarks and to memorize exceptions and co-occurrence of dimensions in the wage data. A deep part of the wide-and-deep model is concurrently trained to generalize rules for wage predictions across employment sectors based on relationships between dimensions. When a user request is received a number of wage benchmarks are forecast by summing linear coefficients produced by the wide linear part with nonlinear coefficients produced by the deep part according to parameters in a user request, and the wage benchmark forecasts are displayed.

Abstract: Managing and applying human resources data comprising aggregating employee transaction data for an organization. A number of human resources-related attributes are evaluated across heterogeneous transaction data. The employee transaction data is classified via statistical machine learning into a number of normalized codes according to the human resources-related attributes, a user interface is presented to adjust a number of organizational operating procedures according to the normalized codes.