International Journal of Business and Applied Social Science (IJBASS) ©Center for Promoting Education and Research (CPER) USA www.cpernet.org VOL: 4, ISSUE: 12 December/2018 https://ijbassnet.com/ E-ISSN: 2469-6501 BIG DATA ANALYTICS OF LABOR COST OVER THIRTY YEARS MOUSUMI BHATTACHARYA Charles F. Dolan School of Business Fairfield University Fairfield, CT 06824 Tel: (203) 254-4000 ext.2893 Fax: (203) 254-4105 E-mail: mbhattac@fairfield.edu USA ABSTRACT Businesses need to analyze labor cost so that they understand what components of businesses affect the amount of resources spent on employees. Interpreting this will help make managerial decisions in adjusting costs of production and develop new strategies regarding investing in workers. Labor cost can vary due to many factors such as employee numbers, assets, liabilities, sales, and debt. In this study, I analyzed labor cost in relation to other firm characteristics, using a large panel data set over thirty years with over 24,000 firm- year observations. I used two different statistical software, R and SPSS and the results are compared. Results show that they both give the same output when running a multivariate regression. Both software are powerful enough to analyze large datasets; however the form of input data and treatment of missing values, matter in determining which one is more efficient. Managers and practitioners can use business analytics with big data to draw conclusions and make important managerial decisions. Future study should include big data analysis using more complex analytical techniques. Keywords: HR Analytics, Labor cost Acknowledgement: I want to thank Siddharth Jain, Research Assistant for his help in data analysis. INTRODUCTION The application of big data analysis in human resource (HR) management is currently at its early stage. Big data analytics is growing fast as organizations are beginning to leverage these to gain competitive advantage (Grover and Kar,2017). This paper examines the relevance of analyses of big data on labor cost and whether different types of software make a difference in the analytics. I applied R software and SPSS statistical package to a panel and cross-sectional data set on companies from the COMPUSTAT North America database from Wharton Research Data Services (WRDS) over the past thirty years. I ran multivariate regressions on labor cost and labor cost variability, as related to several other variables, to compare the results of the two statistical software. Although the statistical results where similar, we found differences in the process of computing, which might affect the choice of analytics software and technique. Big data analytics offers various benefits to the organizations, by providing visual tools and multiple- loop analysis to give fine-grained results that enhance the quality of decisions taken (Li, Tao, Cheng, and Zhao, 2015). So far, the issue is that statistical packages like SPSS may not be able to find fine-grained relationships in the data because it analyses data using single-loop modeling. This means that it develops relationships between variables in the form of mathematical equations. In contrast, R software uses machine learning techniques to perform the statistical analyses. This is a significant research area because machine learning is an algorithm that can learn from data without relying on rules-based programming. It can detect smaller relationships between variables, which 63