Data-driven Analysis On Growth Laws Of Employees In The Enterprise

With the development of big data and artificial intelligence,human resource management(HRM)steps into the era of data.Data-driven HRM is able to create innovative management mode,bringing breakthroughs.As for employee growth study,it plays an important role in HRM and helps managers to make rational decisions for newcomer recruitment,personnel selection,employee training and talent retention.However,tradition researches only obtain limited information by questionnaries and interviews,which cannot adapt to the fast-changing environment.Therefore,this study introduces thoughts of data-driven into HRM to replace qualitative analysis.Techniques of big data and artificial intelligence are applied to explore the growth of excellent staff.The main research results of this study are listed as follows:(1)The research designs growth labels and features to standardize the description of employees.The data come from the HRM information system of a state-owned enterprise in China.Growth of employees is defined as people’s increases in rank.According to growth range and growh rate,employees are diveided into four groups with different growth status by clustering.Individuals are depicted by basic features in five dimensions: personal background,working competence,education situation,organizational environment and social relationship.(2)The research discovers the growth laws of employees based on quantitative data analysis.The study finds correlations between basic features and growth status,and then growth laws are digged for the aim of developing first-class staff.Statistic method is used to discover influence factors while complex network is used to discvoer growth path.It is found that the laws concerning internal factors involve time accumulation,achievement amassing,comprehensive development and so on.External laws comtain facilitation from platform,promotion from transfer and assimilation from surroundings.(3)The research proposes an effective forecasting model for growth potential.Features,growth laws and machine learning method are combined to construct predictive models.Meanwhile,a feature selection strategy and several data balance ways are added to improve model performance.It is shown in experiments that the final model can accurately forecast the growth potential of employees.This study not only verifies the the effectiveness of the model and growth laws,but also provides an exact approach for decision support of HRM.