Background of Big Data
Disucuss about the Opportunities and Challenges of Big Data in Human Resource Management.
Humanity creates a relentless stream of data from the time of birth. Data is typically in every aspect of everyday life, and work and individuals are relying on colossal data mining and application, indicating the dawn of Big Data, its challenges, and opportunities (LaSalle et al. 2011). The current trend of Big Data has infiltrated into every aspect of everyday management activities; include human resources (HR) management. The management of HR requires handling a variety of tasks including reports, multiple resumes, and statistics. Many HR managers still depend on the conventional approaches to data management systems which make it harder to determine future movements, employee growth curves, and predict employee turnover efficiently. Nonetheless, employing the Big Data philosophy in HR can help to improve HR management strategies and result in better outcomes with increased objectivity, accuracy and efficiency (LaSalle et al. 2011). Consequently, this paper discusses the background of Big Data, its features and its application in human resource management, the benefits and challenges faced as well as the corresponding solutions.
Big Data is a relatively new concept, and like many other emerging concepts, researchers are yet to agree on a unit definition of Big Data. However, many scholars have identified five adjectives in describing Big Data effectively. The adjectives are huge, diverse, high growth, a new approach, and a more convincing outcome (LaSalle et al. 2011; Zing and Ye 2015). Although researchers have different views on the definition of Big Data, they all agree that Big Data has four fundamental features, namely: Volume, Variety, Velocity, and Value (Also known as the four-V characteristics) (Qazi and Sher 2016 ).
The most basic feature of Big Data is ‘large-scale.’ Recently, three main reasons have been identified for the continuous increment of data. First is the increased application of the Internet which eases sharing and acquisition of data remotely and across physical boundaries. Second is the increased capacity of individual and business to acquire more real and comprehensive data in timely and highly efficient manner. At the same time, the concepts and methods of individual processing of data have evolved from using sample data to make general analysis to using the general data to make direct analyses, and the difference between the two approaches is huge (Qazi and Sher 2016).
The complexity of the data is an important feature of Big Data. Although Big Data has been there in the past, the datasets are largely structured, and therefore, the methods of processing data are also fixed. However, with the rapid growth and development of the internet and sensor technologies, people can acquire data that is more real and more comprehensive which is one of the areas of focus in Big Data management. This data type is rare and poses significant challenges to conventional data processing techniques (Assunção et al. 2015).
Features of Big Data
Although Big Data is unstructured thereby retaining every detail in data, there is a lot of insignificant material and sometimes even fake information finds its way into the data. Therefore, compared to structured data, Big Data is found to be having lower value density. Still, the value and density of data are both discrete and relevant. However, insignificant details in data may sometimes cause massive impacts (Assunção et al. 2015).
Talent Recruitment
Currently, the competition among businesses is the completion for talent recruitment which is the primary task of the HR department in every enterprise. The conventional recruitment of talents is characterised by the following steps. First, the heads of the various business sections report the need for talent. Second, the recruitment memo is posted on the career portal of the corporation. Then interested applicants would then submit their resumes upon reading the requirements of the vacancy. Upon closure of the application, the HR would then read the applications and select the most relevant applicant for interviewing until the best candidate for the job is found. In addition to the specified prequalification requirements, the experience of the interviewer is also critical. However, in reality, the outcomes are often biased. Because many times the interviewers could not get comprehensive information about the interviewee as they depended on the information issued by the interviewee.
The one-sidedness of the situation leads to highly deviated results. However, Big Data has come to salvage the current situation as it provides a broader platform under the internet through which enterprises can undertake their recruitment activities. For instance, more than two-thirds of Chinese corporations recruit talents from online (Hashem et al. 2015). The companies integrate recruitments into social networks and continuous receive and retrieve resumes and applicant information culminating into a foundation for Big Data analysis. Furthermore, companies can continue to gain more resumes and information about candidates into their databases even when they are not looking to recruit. Additionally, the integration of recruitment and social networks allows the recruiting agency to gain more sensitive information about the candidate such as their social skill and relationships, videos, living conditions, abilities, and many more; giving them a vivid illustration to match the individual to the job post. Concomitantly, the candidate can also learn about the recruitment processes and their degree of qualification more transparently and openly. As such, both the candidate and the enterprise benefit.
Talent Training
HR managers need to undertake staff training to ensure that they sustain the development of the enterprise. It is therefore important to carry out continuous training of the staff members to enhance knowledge and skill acquisition for improved work performance as employees are often the backbone of the company (Mammadova and Jabrayilova 2016). This way, companies can use the HR department to remain relevant amid fierce competition.
Big Data in Talent Recruitment
Conventional training events are typically organized by the firm and conducted by an in-house trainer or an external professional. No matter the choice of training, the company will still incur high financial expenses to facilitate an effective training program. Moreover, the trainings take the classroom setting which may not meet the needs of some of the participants efficiently. Consequently, the impact of conventional training cannot be guaranteed. However, this problem has been successfully managed with the genesis of Big Data. With Big Data access to information is easy and users can share information from wherever they are whenever they want. At the same time, companies have developed their training manuals and shared them through their professional network. Interested companies can buy and customize the manuals to suit their needs. Such programs allow employee to take online training at their convenience and the company can monitor everyone’s participation, and the system can also self-monitor and assess the performance of the participants. Al the company has to do is choose their model of teaching. This way, the employee can develop their own talents while the company achieves training efficiency. The platform allows employees to access feedback at any time which could encourage their interest for training and ensure effective learning. The program is also capable of identifying the areas of strength and those that need improving (Mammadova and Jabrayilova 2016). Further, managers can monitor the performance of employees from the background,
Talent Assessment
Talent management is a valued human resource management activity. Currently, many personnel assessments take the form of expert evaluation, comprehensive assessment among others; but these procedures have proven to be extremely subjective. Scholars have thus studied various concerns surrounding the application of Big Data technology in personnel recruitment, performance evaluation, and classification (Marler and Fisher 2013). The findings of the studies have reported an improvement in the assessment methods as Big Data provided new tools and approaches for personnel management. For example, to build competency, the conventional approach is to go through a series of steps including undertaking interviews, questionnaires, coding, analysis and so forth. However, under the Big Data model enterprises can employ colossal employee information into advanced technology to analyse the performance of different employees in a more accurate approach. The distinction in assessment is subject to technical expertise and physiological or personal indices. Therefore, the revolution of the competency model may from new standards for employee selection. In essence, the reliance on Big Data by the HR management system can continuously enhance the development of talent assessment and the tools for competency analysis to develop the processes of the HR department and the skills and knowledge of employees (Marler and Fisher 2013).
Big Data in Employee Training
Pay-Performance
Employees are basically attracted to employment opportunities by the payment being offered, and payment is primarily the goal of employees participating in work; however, for company managers, payment is a means to encourage and motivate employees to work harder toward achieving the organizational objectives. Nonetheless, the situation on the ground depicts a payment system that is often facing problems, and the performance system which is at the core is also characterized by similar problems. Concomitantly, the practice of accounting is equally complicated (Angrave et al 2016). The conventional system of enterprise payment is mainly qualitative with less quantitative terms, and performance and payment are not linked. The salaries of employees do not reflect the difference between high performances and otherwise because of diffused responsibilities. Even in the application of performance models like the KPI, it is still difficult for HR managers to calculate the performance appraisals thereby making the appraisal system of many companies to seem irrelevant.
However, with Big Data thinking, firms can easily record daily performance activities of every employee including daily workload and task achievement, and then utilize cloud computing to analyze the data (Assunção et al. 2015). Finally, regarding pay-performance standards, the combination of Big Data and cloud computing can be automated to calculate wages. With these operations, firms are guaranteed to achieve better work efficiency and reduced investment in human capital.
Personal career objectives and aspirations are closely linked to Big Data. Using quantitative analysis to evaluate all the data received about the employee, employers can better understand the interests of the employee on job promotion, career planning, professional performance and experience, and other data that the human resource department could use to better understand the employee and their aspirations for improved assistance with their career planning and performance management (Angrave et al. 2016). As such, companies can combine both the conventional and Big Data systems to explore the career paths of their employees and offer personalized guidance. This way, HR managers can reduce employee turnover and achieve a win-win situation for the company and workers. See Appendix A for more potential opportunities for Big Data in HR management.
Despite the advantages of the Big Data and technologies discussed above, demerits exist in every condition. The Big Data theory is still immature as the development of its concepts, methods, and techniques are still at the initial stages and steps will need to be taken towards achieving its establishment (Xiao, Song, and Chen 2012). Therefore, the HR still face various challenges in the application of Big Data. Three challenges are clear to note.
Application of Big Data in Talent Management
Unstructured data is not dominant over structured data especially in certain applications where structured data is considered to be more effective. However, many large corporations can easily analyze Big Data to optimize production, but it is still not necessary for companies to use Big Data in HR (Kaur et al. 2016). If the HR can solve certain problems using the conventional model, there is no need to adopt the Big Data strategy as the technology is yet to be established fully.
Cybercrime is increasingly a menace that needs to be addressed especially with the relentless development and adoption of internet technology in communication and information sharing. It is, therefore, necessary to establish cybersecurity strategies and even personnel in some cases to mitigate the incidence of cybercrime. Still, with all this in place, it is hard to guarantee the full protection of personal data from the breach (Kaur et al. 2016).
Big Data can be used to make predictions, but the conclusion may not always reflect the truth. Because Big Data may be marred by unreal data, it could lead to wrong conclusions. Therefore, we can rely fully on Big Data to make future predictions. Therefore, we can rely fully on Big Data to make future predictions. See Appendix B for more challenges of Big Data in HR management.
Conclusion
In conclusion, Big Data avails new approaches and theories for HR, but it is also inherent of setback. Subsequently, the HR should take full advantage of the pros of using Big Data while staying conscious of the negative implications, to ensure that Big Data works for both the enterprise and the workers.
References
Angrave, David, Andy Charlwood, Ian Kirkpatrick, Mark Lawrence, and Mark Stuart. 2016. “HR and analytics: why HR is set to fail the big data challenge.” Human Resource Management Journal 26, no. 1 (January): 1-11. https://doi.org/10.1111/1748-8583.12090
Assunção, Marcos D., Rodrigo N. Calheiros, Silvia Bianchi, Marco AS Netto, and Rajkumar Buyya. 2015. “Big Data computing and clouds: Trends and future directions.” Journal of Parallel and Distributed Computing 79, no. 80 (May): 3-15. https://doi.org/10.1016/j.jpdc.2014.08.003
Hashem, Ibrahim Abaker Targio, Ibrar Yaqoob, Nor Badrul Anuar, Salimah Mokhtar, Abdullah Gani, and Samee Ullah Khan. 2015. “The rise of “big data” on cloud computing: Review and open research issues.” Information Systems 47 (January): 98-115. https://doi.org/10.1016/j.is.2014.07.006
Kaur, Kamalinder, Kaur Iqbaldeep, Kaur Navneet, Tanisha, Gurmeen and Deepi. 2016. “Big data management: Characteristics, challenges, and solutions.” International Journal of Computer Science and Technology 7, no. 4 (October-December): 54-57. https://www.ijcst.com/vol74/1/12-iqbaldeep-kaur.pdf
LaValle, Steve, Eric Lesser, Rebecca Shockley, Michael S. Hopkins, and Nina Kruschwitz. 2011. “Big data, analytics and the path from insights to value.” MIT Sloan management review 52, no. 2 : 21. https://tarjomefa.com/wp-content/uploads/2017/08/7446-English-TarjomeFa.pdf
Mammadova, Masuma H., and Zarifa G. Jabrayilova. 2016. “Opportunities and challenges of big data utilization in the resolution of human resource management.” Problems of Information technology, no. 1: 33–40. https://www.researchgate.net/profile/Zarifa_Jabrayilova2/publication/318340238
Marler, Janet H., and Sandra L. Fisher. 2013.”An evidence-based review of e-HRM and strategic human resource management.” Human Resource Management Review 23, no. 1 (March): 18-36. https://doi.org/10.1016/j.hrmr.2012.06.002
Qazi, Raza Ur Rehman, and Ali Sher. 2016. “Big Data Applications in Businesses: An Overview.” The International Technology Management Review 6, no. 2: 50. https://scholar.googleusercontent.com/scholar?q=cache:uiMT7QQba9wJ:scholar.google.com/+Qazi,+Raza+Ur+Rehman,+and+Ali+Sher.+%22Big+Data+Applications+in+Businesses:+An+Overview.%22+The+International+Technology+Management+Review+6,+no.+2+(2016):+50.&hl=en&as_sdt=0,5
Xiao, Zhen, Weijia Song, and Qi Chen. 2012. “Dynamic resource allocation using virtual machines for cloud computing environment.” IEEE transactions on parallel and distributed systems 24, no. 6 (September): 1107-1117. https://doi: 10.1109/TPDS.2012.283
Zing, Say, and Maolin Ye. 2015. “Human Resource Management in the Era of Big Data.” Journal of Human Resource and Sustainability Studies 3, no. 01 (March): 41-45. https://doi: 10.4236/jhrss.2015.3100
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