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Human talent forecasting

Author

Listed:
  • Nedelcu Bogdan

    (University Politehnica of Bucharest, Bucharest, Romania)

Abstract

The demand for talent has increased while the offer has declined and these worrying trends don’t seem to show any sign of change in the near future. According to Bloomberg Businessweek, USA, Canada, UK, and Japan (among many others) will face varying degrees of talent shortages in almost every industry in the coming years. The performed study focuses on identifying patterns which relates to human skills. Recently, with the new demand and increasing visibility, human resources are seeking a more strategic role by harnessing data mining methods. This can be achieved by discovering generated patterns from existing useful data in HR databases. The main objective of the paper is to determine which data mining algorithm suits best for extracting knowledge from human resource data, when in it comes to determining how suited is a candidate for a specific job. First of all, it must be determined a way to evaluate a candidate as objective as possible and rate the candidate with a mark from 0 to 10. To do so, some data sets had to be generated with different numbers of values or different values and wore processed using Weka. The results had been plotted so that it would be easier to interpret. Also, the study shows the importance of using large volumes of data in order to take informed decisions has recently become extremely discussed in most organizations. While finances, marketing and other departments within a company receive data systems and customized analysis, human resources are still not supported by expert systems to process large data volumes. The software prototype designed for the experiment rates individuals (working for the company, or in trials) on a scale from 0 to 10, offering the decision makers an objective analysis. This way, a company looking for talent will know whether the person applying for the job is suited or not, and how much the hiring will influence the overall rating of the department.

Suggested Citation

  • Nedelcu Bogdan, 2017. "Human talent forecasting," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 11(1), pages 437-447, July.
    • Handle: RePEc:vrs:poicbe:v:11:y:2017:i:1:p:437-447:n:47
    DOI: 10.1515/picbe-2017-0047
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    References listed on IDEAS

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    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Jayanthi Ranjan & D.P. Goyal & S.I. Ahson, 2008. "Data mining techniques for better decisions in human resource management systems," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 3(5), pages 464-481.
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