Integration of Data Mining in Human Resource

By Anish M.

Data mining is the practice of examining large data sets in order to generate new information. In human resources, data mining is an essential tool in order to compete with the rapidly growing competition such as artificial intelligence and the technological advancement of automated programs. Human resources role in an organization is in the form of skills and knowledge of people, which makes it difficult to measure sometimes. “Human resource improves with age and experience, which no other resource can do naturally” (Dictionary, 2016). The purpose of this article is how data mining is integrated into Human Resources. It is done by the growing interest in using worker-based behavioral statistics to make predictions and to acquire information at a fast rate which helps with the decision making process in the human resource management.

A main advantage for the use of data mining in human resources is the emergence of online recruitment. Before, the traditional way of recruitment was the advertisement of job opening in need of talent, and if the applicant was interested then the applicant submits a paper resume and goes through an interview process. But the birth of Internet has changed the game of recruitment in this era. Ye (2015) suggests that companies integrate recruitment into social networking such as Facebook and gather resume information and application information from third party companies such as LinkedIn, which provided more opportunities to the practice of data mining analysis for human resource recruitment process. LinkedIn is an example of data mining for human resources, because now employers can search for attributes, talents and achievements by toggling filter options or specifying the desired traits in the data bank of resumes to find the best fit for the job.

In addition to online recruitment, through data mining, human resource managers have been able to keep track of absenteeism. Absenteeism is the practice of regularly staying away from work without good reason and approval from management (Dictionary, 2016). According to Bernik (2015) many organizations regularly keep track of unapproved daily absences of which are not due to the reasons of ill health. Some examples of unapproved daily absences are job satisfaction, poor relations and family problems. Although data mining in human resources can be very resourceful it does have its limitations. Human resource information systems such as data mining do not provide a data entity that would describe the reasons for being absent but only the statistical aspect such as patterns, types and time of absences(Bernik, 2015). For example the famous manufacturer of baby products, Gerber had used a decision tree obtained by data mining, in which it provided statistical patterns of its employee’s absenteeism. Therefore the company’s human resource management was better able to determine payroll, benefits, and performance development(Johne, 2013).

Data mining can be used as simple as a helpful tool of patterns for comparison and evaluation purposes. The use of big data especially helps if companies are planning on expanding their organizations to unfamiliar regions, providing several aspects. Overall data mining in human resources improves the quality of employee performance and for the management decision-making process, which then helps sustain a competitive advantage. Hence data mining still provides an advantage edge by implementing algorithms and obtaining useful data from previous data. This makes data mining in a human resource entity the most vital resource in an organizations’ arsenal because an organization is as strong as its human resources.

Bernik, M. (2015). Knowledge Management and Information Technology in Analyzing Human Resource Processes. Retrieved from

Dictionary, B. (2016). What is Human Resource? Retrieved from

Johne, M. (2013, April). Mining data to find and keep the right people. Retrieved from

Ye, M. (2015, February). Human Resource Management in the Era of Big Data. Retrieved from

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