Using HR Analytics to Support Managerial Decisions

Liyuan Liu · Sanjoosh Akkineni · Paul A. Story · Clay Davis

16 citations0/0 files storedACM Southeast Regional Conference (ACMSE)

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AI and HR Technologies Online Learning and Analytics Big Data and Business Intelligence

Abstract

Human Resource (HR) Analytics enables HRs to make strategic contributions and support managerial decisions. However, in most of the industry, HRs should have been on board with data analysis. There are several challenges: the HR data is messy and imbalanced, it is hard to harness both structured and unstructured data, some HR managers lack data mining skills and the lack of related empirical research that gives a detailed analytics guideline. The contribution of this study is that we develop a framework to support an industrial aluminum company to make the decisions and to improve strategy execution. The framework includes descriptive analysis, predictive analysis, and entity sentiment analysis. We analyzed an industrial aluminum company's data and found some actionable issues. Then we employed machine learning algorithms to predict employees' turnover and found risk factors. Moreover, we applied the entity sentiment analysis on the unstructured data collected from employees' engagement survey.

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References

2016HR and analytics: why HR is set to fail the big data challenge 2005Human resource information systems (HRIS) and technology trust 2018Employee Turnover Prediction with Machine Learning: A Reliable Approach 2012Analyzing the impact of HRIS implementations on HR personnel’s job satisfaction and turnover intention 1946Book Review: The Challenge of Red China 2019An Improved Random Forest Algorithm for Predicting Employee Turnover 2016Human Resource Predictive Analytics (HRPA) For HR Management In Organizations 2012Raging debates in HR analytics

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