Human capital evaluation in knowledge-based organizations based on big data analytics

Abstract

Starting from a Human Capital Analysis Model, this work introduces an original methodology for evaluating the performance of employees. The proposed architecture, particularly well suited to the special needs of knowledge-based organizations, is articulated into a framework able to manage cases where data is missing and an adaptive scoring algorithm takes into account seniority, performance, and performance evolution trends, allowing employee evaluation over longer periods. We developed a flexible software tool that gathers data from organizations in an automatic way – through adapted connectors – and generates abundant results on the measurement and distribution of employees’ performances. The main challenges of human resource departments – quantification of human resource performance, analysis of the distribution of performance, and early identification of employees willing to leave the workforce – are handled through the proposed IT platform. Insights are presented on different granularity levels, from organization view down to department, group, and team.