Access to this full-text is provided by Wiley.
Content available from Mathematical Problems in Engineering
This content is subject to copyright. Terms and conditions apply.
Research Article
Human Resource Management of Energy Companies Based on Big
Data Analysis
Peng Gui
1
and Min Zhang
2
1
GongQing Institute of Science and Technology, Gongqingchengshi, Jiangxi 332020, China
2
Dean’s Office of Henan Polytechnic University, Henan Polytechnic University, Jiaozuo, Henan 454000, China
Correspondence should be addressed to Min Zhang; hejunyi@hpu.edu.cn
Received 16 March 2022; Accepted 7 May 2022; Published 20 May 2022
Academic Editor: Xiaoshuang Li
Copyright ©2022 Peng Gui and Min Zhang. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Human resource management mode refers to a comprehensive summary of management objectives, processes, content, methods,
and other elements. e more common two modes are control mode and commitment mode. e enterprise human resource
management model has many different types. e generation pair promotes the development of enterprise human resource
management from the traditional model to the platform model, processing complex data with the help of data-based technical
means, and realizing the integration and sharing of resource data. is paper takes an energy company as an example to carry out a
detailed study. e article takes the big data as the background and the company as the research object. From the perspective of
human resource management, this paper tries to find out the performance management, compensation and benefits management,
and other issues of the company in human resource management under the background of the big data era and puts forward
corresponding solutions for the current problems. In particular, the company gave certain opinions on how to build a human
resource management system in the context of the current big data era. By conducting field research on the company and issuing
questionnaires, this paper finds out the current problems of the company in human resource management and proposes
corresponding solutions for these problems.
1. Introduction
Big data and Internet information technology coexist, and
the development of Internet information technology
further promotes the application of big data in various
industries. In the context of big data, the traditional hu-
man resource [1] performance management model has
changed. Human resource performance management in
the context of big data can use data analysis instead of
relying solely on the subjective consciousness of human
resource managers [2]. Performance management can
effectively mobilize the enthusiasm of [3] employees and
bring economic benefits to enterprises. However, there are
many problems in [4] the implementation of traditional
performance management. erefore, in the context of big
data, optimizing the mode of human [5] resource per-
formance management is the key countermeasure to [6]
conform to the social trend and promote the healthy
development of the enterprise.
e term big data comes from “Big data” in English. It
was first proposed in 2008, by renowned information
processing experts Victor Mayer-Schonberger and Kenneth
Cookyer. With the popularization of the Internet and the
high-speed increase of end users, the amount of information
in the online world continues to increase [7] geometrically.
How users or search engines can effectively search for
correct and effective [8] information in massive data has
become an effective way to play in the new era. Traditional
search engines usually provide users with corresponding
data by means of commercial promotion or ranking based
[9] on clicks, which cannot meet the needs of [10] users in
the current environment [11]. At the same time, due to the
involvement of commercial factors, the search scope is
limited, and the searched information is difficult to be true
Hindawi
Mathematical Problems in Engineering
Volume 2022, Article ID 5489369, 7 pages
https://doi.org/10.1155/2022/5489369
and [12] objective. erefore, in the future development of
the Internet [13], using reasonable big data analysis to enable
users to obtain effective information will become the goal
that experts strive to pursue.
Talent is the lifeblood of an enterprise [14] and it
maintains the operation of the enterprise. With the devel-
opment of society, the demand of enterprises for high-tech
talents will increase. e quality of human resource man-
agement can even [15] determine the rise and fall of an
enterprise. Enterprises should use big data technology to
improve the quality of human resource management.
Whether big data technology can be better integrated [16]
with talent recruitment, training, and management system is
an important test for enterprises. is paper takes SEnergy
Company as an example. On the one hand, it systematically
points out the [17] existing problems in the company’s
human resource management. On the [18] other hands, it
designs a plan for how SEnergy Company can effectively
apply big data to human resource management. At the same
time [19], the human resource management level of SEnergy
Company provides a certain reference for other energy
companies.
2. Research Objects and Methods
S Energy Company is located in the Economic Development
Zone of XCity, Shanxi Province. It is a comprehensive
energy enterprise with coal production, processing, sales,
and power generation as its main business. In the early days
of establishment, through overall planning and step-by-step
implementation, it was found that 1.011 billion tons of coal
could be mined in the company’s three wells with a total area
of 169.11 km
2
, accounting for 66% of the total reserves. In
this way, after the establishment of the supporting coal
preparation plant, the output of more than one million tons
can be formed and sold to the whole country. is paper
surveys nearly 3,000 employees of the company by means of
a questionnaire survey and analyzes the results. We surveyed
3000 employees and received around 2950 feedbacks.
2.1. Reliability Analysis. e Cronbach coefficient is the
most used reliability measure, and its calculation formula is
as follows:
α�k
k−11−S2
i
S2
x
,(1)
αis the reliability coefficient, Kis the number of test items, S
i
is the variance of the scores of all subjects on item i, and S
x
is
the variance of the total scores obtained by all subjects.
is paper uses the SPSS to test the reliability of all data.
e experimental results show that the reliability coefficient
of this questionnaire is 0.81, which is greater than 0.8, in-
dicating that the questionnaire method used in this paper
has good reliability.
2.2. Statistical Analysis. e formula used for statistical
analysis is as follows:
p�mi
n,(2)
piis the th proportion, miis the number of people who
answered the ith answer, and n is the sum of the total
number of people who answered all the people.
3. Results and Discussion
3.1. Problems in Human Resource Management of S Energy
Company
3.1.1. Human Resource Planning Issues. From Figure 1, only
9% of the human resource managers believe that big data has
a great impact on the company’s human resource manage-
ment, and 25% of the respondents believe that big data have a
relatively large impact on human resource management. 38%
of respondents believe that big data have an average impact
on human resource management, and 28% of respondents
believe that big data have no impact on human resource
management. It can be seen from this that the company’s
human resource managers and corresponding employees
have not yet formed a more mature big data awareness. ey
believe that the current big data technology has not had much
impact on the field of human resource management. But we
all know that in order to better apply big data technology to
the company’s human resource management, human re-
source management personnel must have large data aware-
ness. Only when most of the internal managers of the
enterprise are in a leading position in the formation of data
awareness can ordinary employees be better influenced,
thereby promoting the formation of data awareness among
employees of the entire enterprise.
From Figure 2, we can see that 78% of the interviewed
employees of the company say that the company should es-
tablish a big data platform for human resource management,
and 20% of the interviewed employees say that the company’s
human resource management does not need the support of the
big data platform. e company’s employees’ demand for a big
data platform is in contradiction with the company’s lack of a
big data platform for human resource management.
3.1.2. HR Recruitment Issues. From Figure 3, we can see that
35% of the company’s talent recruitment is still using
campus recruitment, 40% of the talent recruitment comes
from social on-site recruitment, and 9% of the talent re-
cruitment comes from headhunting. e proportion of
offline recruitment is still as high as 84%, while the current
company’s online recruitment only accounts for 12%. At
present, companies mainly use offline recruitment methods
in talent recruitment and use less online recruitment
methods, which is inconsistent with the current change in
talent recruitment methods in China under the background
of big data. Since the company still adopts more traditional
recruitment methods in talent recruitment, it has a strong
subjectivity in the process of talent recruitment. In the initial
test, HR conducts preliminary interviews with candidates.
However, HR is not very clear about the job requirements,
which will also lead to the loss of some available talents in the
2Mathematical Problems in Engineering
process. In the reexamination stage, the department leaders
conduct interviews with the interviewers. Since this inter-
view is only conducted verbally, the professional skills of the
interviewers cannot be well displayed, which will cause those
who are good at speaking but have no real skills to mix in.
e company has not recruited any real available talents.
3.1.3. e Problem of Human Resource Allocation. From
Figure 4, we can see that at present, the company’s talent
allocation is mainly based on the skills of the employees
themselves, accounting for 40%. However, we can also notice
that the proportion of talent allocation based on the
subjective judgment of department leaders is also relatively
high, at 29%. In addition, 21% of employees are allocated
talents based on their majors, while only 4% are allocated
based on employees’ career data assessments. It can be seen
from this that the company still has strong subjectivity in
talent allocation, and the data evaluation and application
technology of talent allocation are relatively weak.
3.1.4. Human Resource Training and Development Issues.
From Figure 5, we can see that when the company for-
mulates the company’s training plan, 80% of the plans are
formulated according to the arrangement of the relevant
departments of the company, 10% are formulated according
to the wishes of the company’s employees, and only 5% are
formulated based on the analysis of the employee’s work
data. It can be seen from this that the company does not
actually understand the real needs of the employees when
formulating the training plan for the company employees
but only trains the employees from the company level, which
makes the company’s training plan not really recognized by
the employees. e human resources department’s analysis
of training needs is not in place.
3.1.5. Human Resource Performance Management Issues.
From Figure 6, we can see that when a company formulates
the performance plan, 44% of the companies take the their
business goals as the basis for the performance plan, and 38%
rely on the managers’ own experience and intuition. Only
15% of management objectives are used as the basis for
performance planning. It can be seen from this that there is
still a certain deviation and gap between the performance
plan and the company’s strategic goals when the company is
formulating performance plans, which makes the formu-
lation of performance plans lack the support of data in the
context of big data. A performance plan; that is, inconsistent
with the strategic goal will hinder the realization of the
strategic development of the enterprise.
From Figure 7, we can see that when the company
evaluates employees, 32% comes from mutual evaluation
among employees, 40% comes from the evaluation of su-
periors to subordinates, 20% comes from self-evaluation of
employees, and only 5% comes from employees’ daily work.
0
5
10
15
20
25
30
35
40
45
Campus
Recruiting
Social On-Site
Recruitment
Head
hunting
Online
recruitment
Other
Percentage (%)
Figure 3: Analysis of the company’s talent recruitment channels.
0
5
10
15
20
25
30
35
40
45
Profession Skill Data evaluation Competent
judgment
other
Percentage (%)
Figure 4: e company’s talent allocation.
0
5
10
15
20
25
30
35
40
Very large Relatively large Average No effect
Percentage (%)
Figure 1: Views of stakeholders on the importance of applying big
data to human resource management.
0
10
20
30
40
50
60
70
80
90
Need No need Others
Percentage (%)
Figure 2: Whether companies should build a big data platform for
human resource management.
Mathematical Problems in Engineering 3
It can be seen from this that the company still has a lot of
subjectivity in the performance appraisal of employees, and
the performance appraisal lacks the support of data. For
example, in the mutual evaluation among employees, it is
very likely that the two employees will give each other fa-
vorable comments because of the good relationship between
them, or they may give each other negative comments be-
cause of some nonwork conflicts between two employees,
which wipes out the employees themselves. Due perfor-
mance is not conducive to the company’s more objective
performance management of employees.
3.1.6. Human Resources Compensation and Benefits Man-
agement Issues. From Figure 8, we can see that 80% of S
Energy Company adopts the management system of equal pay
for the same position, and only 15% adopt the performance-
based salary management system. It can be seen from this, that
at present, when the company conducts salary management
for employees, it is mainly managed through equal pay for the
same position, and the proportion of salary distribution by
employee performance is relatively small. However, in the
same position, different people can play different values in the
same position. If the same position is given the same salary, it
will cause those with high ability to be reluctant to give full play
to their talents. Reducing their enthusiasm for work is not
conducive to the long-term development of the company.
3.2. e Strategy of Enterprise Human Resource Management
Mode in the Era of Big Data. In order to solve the above
problems in the company’s human resource management, we
need to design a human resource management system and
make a reasonable plan through in-depth research on big data.
3.2.1. Forming Awareness of Big Data. Promoting the
company’s employees to form big data awareness is an
important step in the company’s current big data devel-
opment. Only when the internal employees of the company
have formed a strong sense of data, can they consciously
analyze the data during data collection rather than per-
functory. is requires the company to strengthen the
training of the company’s internal employees’ big data
awareness in their usual work, mainly through the following
two aspects: first, the company needs to invite external well-
known big data lecturers to train the company’s employees,
mainly to train employees big data awareness, tell them the
importance of applying big data to their daily work and how
to apply big data at work to better handle their work. e
second is to carry out a big data seminar within the company
to bring together employees from relevant departments so
that everyone can discuss examples of applying big data to
specific work in their own work and give material rewards to
those who have a better application, which can cultivate
employees’ big data awareness through two ways of com-
bining internal and external.
0
5
10
15
20
25
30
35
40
45
50
Based on
strategic
goals
Based on business
goals
Intuition Other
Percentage (%)
Figure 6: e basis for companies to develop performance plans.
0
5
10
15
20
25
30
35
40
45
Mutual
evaluation
Evaluation of
superiors to
inferiors
Self-
evaluation
Evaluation
based on data
other
Percentage (%)
Figure 7: Analysis of the form of company performance appraisal.
0
10
20
30
40
50
60
70
80
90
Equal pay for equal post Performance pay Other
Percentage (%)
Figure 8: e company’s salary distribution management.
0
10
20
30
40
50
60
70
80
90
According to
will
According to data According to
arrangement
Other
Percentage ( %)
Figure 5: e basis for companies to develop training programs.
4Mathematical Problems in Engineering
3.2.2. Build a Big Data Information Platform. At present, in
the process of company development, human resource
management can generate a large amount of data, such as
external industry salary data, internal labor cost data, talent
market supply and demand information, salary and welfare
data, performance appraisal, as well as management data,
and employee demand analysis. erefore, in order to let the
company’s managers get out of the complex data, it is
necessary to build a huge data information platform in
accordance with the principle of unified planning and step-
by-step and place the company’s internal information on the
data platform, so as to better solve problems in the com-
pany’s human resource management process. e specific
methods and steps are as follows.
(a) Do a Good Job oF Planning in the Early Stage of
Platform Construction.SEnergy Company has a
certain information foundation and uses many in-
formation management systems in the management
of the enterprise, but there is no corresponding
information management system in human resource
management, which leads to the human resource
management system used by the company and its
business segments. e system cannot achieve ef-
fective docking, resulting in combined data, which
cannot be transmitted and shared in real-time.
erefore, SEnergy Company should step up the
construction of the big data platform at the moment
to realize its operability of the big data platform. At
the same time, with the growth of Senergy’s data, the
company also has a long-term plan for the infra-
structure of the data center to ensure that the data
information on the big data platform can be effec-
tively used to achieve the goal of the human resource
management data center, as shown in Figure 9.
erefore, the platform planning must be fully
considered, and the design is reasonable. On the
other hand, the economic aspects of platform con-
struction should also be considered. Specifically, S
Energy Company should make a scientific and
reasonable estimate of the company’s data volume in
the next 3–5 years according to the company’s
current development. e actual development of the
situation to do specific optimization.
(b) ree Levels of Building a Big Data Platform. At
present, the construction of the company’s big data
platform can be constructed in accordance with the
“3 + 1” model; that is, in the order of collection and
storage platforms, mining and analysis platforms,
processing and decision-making platforms, and data
security platforms covering the world, which can be
shown in Figure 10 below.
e main task of the collection and storage platform is
to collect and store the company’s big data. e ul-
timate purpose of the mining and analysis platform is
to analyze the big data information collected by the
enterprise and mine the company’s data to realize the
development of the company’s big data. e decision-
making platform mainly uses the analysis results of
big data to give a certain reference to the company’s
decision-making and adopts corresponding policies
according to the corresponding data display. e
main purpose of the data security platform is to
ensure the security of data within the company. Only
by ensuring the security of the company’s data we can
ensure that the company’s assets are not infringed and
can promote the development of the company’s big
data. Companies do not have their own big data
analysis and mining platforms, so they can rent third-
party professional tools. However, they must have the
corresponding processing, decision-making, and ac-
tion capabilities. At the same time, the company must
also have its own data platform to store data within
the company and ensure the company’s ownership of
the data. Based on making full use of the enterprise’s
Target
Unified and
complete
information
model
Consistent
data entry
and exit
Control
center
Unified
data source
and data
caliber
Figure 9: SEnergy Company human resources and big data center
construction goals.
Data security
platform
Mining and
analysis platform
Processing and
decision platform
Acquisition and
storage platform
Figure 10: ree levels of big data for human resource manage-
ment in companies.
Mathematical Problems in Engineering 5
big data platform, the human resources department
only needs to extract key data from the information
system and analyze the corresponding data according
to its own the work efficiency of the company’s hu-
man resources management and can make the
company’s human resource management more
scientific.
3.2.3. Improving the Basic Management Level of Big Data
Applications. For the application of S Energy’s big data, the
most critical step is to build a good big data platform, to
realize the structured collection, storage, processing, anal-
ysis, and application of the company’s data, and enrich the
company’s data resources. After the company’s data are
mined, the next most critical link in the maintenance of this
data. At present, there are not many big data talents in the
company, so the management of big data is still relatively
lacking. Only by maintaining data management and su-
pervising various processes of data processing can personnel
meet the needs of human resource management in the big
data environment.
In the future, Scompany should actively cultivate the
company’s big data talents and realize the overall security of
the company’s database through the establishment and
management of enterprise database security by big data
talents.
4. Conclusion
(1) At present, SEnergy Company does not use big data
enough in human resource management, and there
are many problems. First, in terms of big data
awareness, the company’s human resource managers
and company leaders have not formed a big data
management awareness; second, there is no pro-
fessional data system in talent recruitment, and
talent recruitment has strong subjectivity; third, in
terms of talent allocation, due to the lack of data
evaluation of talents, the company’s talent allocation
needs do not match the company’s development
strategy; fourth, in terms of talent resource training
and development, the content of talent training is
inconsistent with talent needs; fifth, there was no
data evaluation in human resources performance
management, and there was a strong subjectivity;
and sixth, in terms of human resources compensa-
tion and welfare management, both compensation
and welfare management lacked data support.
(2) Under the background of big data, Scompany can
build a big data platform for human resources and a
data-based management system to realize scientific
human resource management and solve the existing
problems. In this system, the company can effectively
carry out talent resource planning, use the big data
platform for talent screening, use the data model for
talent allocation, use data analysis to mine employee
training needs, and use the company’s internal data
for effective performance management.
Data Availability
e data used to support the findings of this study are in-
cluded in the article.
Conflicts of Interest
e authors declare that they have no conflicts of interest.
Acknowledgments
e authors would like to thank the financial supports from
the Key Project of Higher Education Reform in Jiangxi
Province of the year 2020, ideological and political research,
and the practice of E-Commerce courses in higher voca-
tional colleges in the mode of industry-education integration
(Grant No. JXJG-2020-94-1); and the Educational Science
and Technology Project in Jiangxi Province of the year 2020,
Research on the innovative function of libraries in higher
vocational colleges in the mode of industry-education in-
tegration (Grant No. GJJ206804).
References
[1] V. Chaudhri and J. Wang, “Communicating corporate social
responsibility on the internet: a case study of the top 100
information technology companies in India,” Management
Communication Quarterly, vol. 21, no. 2, pp. 232–247, 2007.
[2] J. Wang, K. Meng, J. Cao, Z. Cheng, L. Gao, and C. Lin,
“Information technology for energy internet: a survey,”
Journal of Computer Research and Development, vol. 52, no. 5,
p. 1109, 2015.
[3] J. Carlo, K. Lyytinen, and G. Rose, “Internet computing as a
disruptive information technology innovation: the role of
strong order effects 1,” Information Systems Journal, vol. 21,
no. 1, pp. 91–122, 2011.
[4] M. Raman, R. Stephenaus, N. Alam, and M. Kuppusamy,
“Information technology in Malaysia: E-service quality and
uptake of internet banking,” Journal of Internet Banking and
Commerce, vol. 13, no. 2, pp. 1–18, 1970.
[5] E. Goss and J. Phillips, “How information technology affects
wages: evidence using internet usage as a proxy for IT skills,”
Journal of Labor Research, vol. 23, no. 3, pp. 463–474, 2002.
[6] M. Chung and K. Jaehyoun, “e internet information and
technology research directions based on the fourth industrial
revolution,” KSII Transactions on Internet and Information
Systems (TIIS), vol. 10, no. 3, pp. 1311–1320, 2016.
[7] B. Brown, M. Chui, and J. Manyika, “Are you ready for the era
of ‘big data,” McKinsey Quarterly, vol. 4, no. 1, pp. 24–35,
2011.
[8] T. Harford, “Big data: a big mistake,” Significance, vol. 11,
no. 5, pp. 14–19, 2014.
[9] D. Fisher, R. DeLine, M. Czerwinski, and S. Drucker, “In-
teractions with big data analytics,” Interactions, vol. 19, no. 3,
pp. 50–59, 2012.
[10] T. Bednall, K. Sanders, and P. Runhaar, “Stimulating informal
learning activities through perceptions of performance ap-
praisal quality and human resource management system
strength: a two-wave study,” e Academy of Management
Learning and Education, vol. 13, no. 1, pp. 45–61, 2014.
[11] R. Schonberger, “Human resource management lessons from
a decade of total quality management and reengineering,”
6Mathematical Problems in Engineering
California Management Review, vol. 36, no. 4, pp. 109–123,
1994.
[12] J. Perdomo, J. Gonz´
alez, and J. Galende, “An analysis of the
relationship between total quality management-based human
resource management practices and innovation,” Interna-
tional Journal of Human Resource Management, vol. 20, no. 5,
pp. 1191–1218, 2009.
[13] S. Wood, “Human resource management and performance,”
International Journal of Management Reviews, vol. 1, no. 4,
pp. 367–413, 1999.
[14] M. Nocker and V. Sena, “Big data and human resources
management: the rise of talent analytics,” Social Sciences,
vol. 8, no. 10, p. 273, 2019.
[15] J. Garcia and A. Osca, “Big data contributions to human
resource management: a systematic review,” International
Journal of Human Resource Management, vol. 32, no. 20,
pp. 4337–4362, 2021.
[16] P. Li, “On the application of big data technology in human
resource management in the new era,” Journal of Physics:
Conference Series, vol. 1915, no. 4, Article ID 042038, 2021.
[17] B. Noack, “Big data analytics in human resource management:
automated decision-making processes, predictive hiring al-
gorithms, and cutting-edge workplace surveillance technol-
ogies,” Psychosociological Issues in Human Resource
Management, vol. 7, no. 2, pp. 37–42, 2019.
[18] J. Zeng, “Application of big data processing technology in
human resource management information system,” Journal of
Physics: Conference Series, vol. 1881, no. 3, Article ID 032029,
2021.
[19] W. Zhu, “Reconstruction of human resource management
under big data and artificial intelligence,” Journal of Physics:
Conference Series, vol. 1533, no. 4, Article ID 042016, 2020.
Mathematical Problems in Engineering 7
