The era of digitalization leads to redesigning not only business models of organizations, but to rethinking the HR paradigm to harness the power of state-of-the-art technology for Human Capital Management (HCM) optimization. Cognitive processing, predictive analytics, and computational intelligence will bring transformative change to HCM. This paper deals with issues of decision support in HCM based on the models of predictive workforce analytics (WFA) and Business Intelligence (BI). The implementation of predictive Data Mining models complements the study of OLAP-cubes, the formation and visualization of advanced reporting by force of BI systems. Several models of effective integration of information systems for predictive WFA are proposed, their advantages and disadvantages are analyzed. As an example, integration of HCM system, the analytics platform (IBM SPSS Modeler) and BI systems (IBM Cognos Analytics, IBM Planning Analytics, IBM Watson Analytics) for predicting the employee attriction is shown. This integration provides a cycle ‘prediction – planning – performance review – causal analysis’ to support data-driven decision making in proactive HCM.