Opportunities and Challenges of Big Data in Human Resource Management

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. Learn about the opportunities and challenges of Big Data in Human Resource Management. Discover how Big Data can improve talent recruitment, training, assessment, pay-performance, and employee career management.

1
Opportunities and Challenges of Big Data in Human Resource Management
Student Name
Student ID
Course
Instructor
Author’s Note

2
Introduction
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.
The Background and Characteristics of Big Data
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 ).

3
Volume
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).
Variety
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).
Value
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).

4
Opportunities of Big Data in HR Management
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

5
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.
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

6
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).
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

7
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.
Employee Career Management
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

8
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.
Challenges of Big Data
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.
The combined use of Big Data and Structured Data
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.
Assurance of Personal Security
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).

9
Use of Findings
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.

10
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.
http://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.
http://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

11
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.
http://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.2015.31006

12
Appendices
Appendix A: Potential Opportunities for Big Data in Human Resource Management –
Mean Summary

13
Appendix B: Challenges of Big Data in Human Resource Management – Mean
Summary

14