Descriptive to prescriptive process mining analytics: The employee journey

This project is a collaboration with HR service provider Acerta:Clients of Acerta want to investigate how their employees move internally and if there is a difference between genders for example. Because of this Acerta wants to start researching the internal mobility of employees. Additionally, the research will focus on closing the gap between data science- and HR teams. The combination of data science and HR expertise will make HR management more fact-based and should allow for better decision-making. To do this, strong visualizations are necessary, which are easy to interpret and create insights on which actions can be taken by HR management. During this research project, it will be investigated if process mining can answer these demands. It is hypothesized that process mining can create strong visualizations of employees' career paths. In a later phase of the research predictive machine learning methods will be added/developed to predict the internal movements of employees. In the last phase, the predictive models will make place for causal models, that model the effect of a treatment. This all will lead to a more informed HR management that can base its decisions on data and not just on gut feeling.

Keywords:process mining, career management, data analytics, HR management

Disciplines:Data mining, Workflow, process and database management, Personnel economics

Project type:PhD project