Assessing Data Analytics Capabilities in Retail Organizations:
Insights into Mining, Predictive Analytics and Machine Learning
ROSARIO PARIONA-LUQUE1, a, ALEX PACHECO2, b, EDWIN VEGAS-GALLO3, c,
RUI ALEXANDRE CASTANHO4,5,6,d, FABIAN LEMA1,e, LIZ PACHECO-PUMALEQUE7,f,*,
MARCO AÑAÑOS-BEDRIÑANA8,g, WILSON MARIN9,h, EDWIN FELIX-POICON10,i
ANA LOURES4,j
1Sustainable Tourism and Hospitality Management,
Universidad Nacional Autonoma de Huanta,
Jr. Manco Cápac 497, Huanta, Ayacucho,
PERU
2Professional School of Systems Engineering,
Universidad Nacional de Cañete,
Av. Benavides 1370, San Vicente de Cañete, Lima,
PERU
3Universidad Peruana de Ciencias e Informática,
Av. Talara 752 Jesús María, Lima,
PERU
4VALORIZA - Research Center for Endogenous Resource Valorization,
Instituto Politécnico de Portalegre (IPP),
PORTUGAL
5College of Business and Economics,
The University of Johannesburg, P.O. Box 524 Auckland Park,
Johannesburg 2006,
SOUTH AFRICA
Advanced Research Centre,
6European University of Lefke, Lefke, Northern Cyprus,
TR-10 Mersin,
TURKEY
7Carrera de Administración, Facultad de Ciencias Empresariales,
Universidad San Ignacio de Loyola,
Av. La Fontana 550, La Molina, Lima,
PERU
8Universidad Nacional Autónoma de Chota,
Jr. José Osores 418, Chota, Cajamarca,
PERU
9Universidad Cesar Vallejo,
Av. Victor Larco 1770, Trujillo, La Libertad,
PERU
10Universidad Nacional de Jaen,
Carretera Jaén - San Ignacio KM 24 - Sect. Yanuyacu, Jaén, Cajamarca,
PERU
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1546
Volume 21, 2024
aORCiD: https://orcid.org/0000-0002-8468-7801
bORCiD: https://orcid.org/0000-0001-9721-0730
cORCiD: https://orcid.org/0000-0002-2566-0115
dORCiD: https://orcid.org/0000-0003-1882-4801
eORCiD: https://orcid.org/0000-0002-0168-8703
fORCiD: https://orcid.org/0000-0002-4323-1293
gORCiD: https://orcid.org/0000-0002-4737-4443
hORCiD: https://orcid.org/0000-0001-6175-8112
iORCiD: https://orcid.org/0000-0001-5536-2410
jORCiD: https://orcid.org/0000-0002-2146-4205
Abstract: - Nowadays, implementing data analytics is necessary to improve the collection, evaluation, analysis,
and organization of data that allow the discovery of patterns, correlations, and trends that improve knowledge
management, development of strategies, and decision-making in the organization. Therefore, this study aims to
provide an accurate and detailed assessment of the current state of data analytics in the retail sector, identifying
specific areas of improvement to strengthen knowledge management in organizations. The research is applied
with a quantitative approach and non-experimental design at a descriptive and propositional level. The survey
technique was used, and as a data collection instrument, a questionnaire addressed to 351 employees of
companies in the retail sector concerning the variable data analysis with the dimensions of data extraction,
predictive analysis, and machine learning and the variable management of the knowledge with the dimensions
knowledge creation and knowledge storage. The results show that 52.99% of collaborators indicate that the
level of data extraction is terrible, 57.83% indicate that the level of predictive analysis is wrong, and 54.99%
express that the level of machine learning is average, which contributes to the implementation of innovative
resources and solutions that promote the inclusion of a high-tech approach to address information management
problems and contribution to the development of knowledge in an institution.
Key-Words: - Data analytics; knowledge management; Data extraction; predictive analytics; machine learning;
knowledge creation; storage of knowledge.
Received: October 16, 2023. Revised: May 11, 2024. Accepted: June 12, 2024. Published: July 5, 2024.
1 Introduction
Nowadays, data analytics is becoming a
fundamental practice in extracting information as it
allows the use of relevant and quality data to
improve the decision-making process in
organizations, [1]. Also, technology is a critical
element in data analytics as it allows the creation of
platforms that provide modern data analysis
functions and patterns; however, they can generate a
counterproductive effect if the platform provides
inadequate or difficult-to-understand information
[2], [3]. Similarly, the benefits of data analytics are
revenue growth, cost savings, increased market
share, customer satisfaction and corporate market
value gains, and the discovery of hidden patterns,
unknown correlations, trends, and customer
preferences, [4]. Finally, data analytics promotes
quality improvement, process optimization, and
early detection of parameter deviation, which
provides an economic advantage over other
organizations in terms of productivity and efficiency
gains, [5].
In the business sector, the enhancement of data
analytics is not just a choice, but a necessity. It
empowers organizations to bolster their knowledge
management, producing quality reports that
significantly enhance decision-making capabilities.
As [6] suggests, knowledge management is a
process of creating, organizing, evaluating,
transmitting, and applying knowledge to solve
practical problems. Similarly, [7], outlines that
knowledge management involves planning,
conducting, monitoring, and evaluating actions and
decisions related to the acquisition, transmission,
preservation, retrieval, creation, application, and
dissemination of data, information, and knowledge.
[8], further argues that knowledge management is
essential in organizations to foster the creation of
new knowledge, share information with all staff,
and improve employee performance. The benefits of
knowledge management are profound, strengthening
the organization's flexibility by promoting the free
dissemination of information, increasing
institutional value, and improving competitiveness,
[9].
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1547
Volume 21, 2024
Despite the undeniable benefits of data analytics,
significant challenges remain in the practice
globally. In China, problems such as low accuracy
in analysis, incomplete information extraction, and
poor implementation of traditional analytics
methods have been identified, restricting the
effectiveness of data analytics in formulating
competitive strategies in organizations, [10].
Similarly, in Malaysia, there is evidence of
inadequate student preparation in data analytics,
highlighting the need to enhance specialized skills
in data visualization, data storytelling, and the
handling of data analytics tools, [11]. In addition,
India recognizes the urgent need for some
companies to implement technologies in data
analytics to manage, analyze, store, and process
massive volumes of data in shorter times, [12].
Finally, in Colombia, it is reported that many
companies need help in decision-making and
strategy formulation due to the limited capacity to
process the overwhelming amount of data generated
by the market, highlighting the imperative need to
adopt new technologies, [13].
1.1 Knowledge Gap, Objectives, and Scope
of the Research
Previous studies have found that implementing data
analytics is essential to optimize data collection,
evaluation, and organization processes, thus
facilitating the identification of patterns,
correlations, and trends crucial for effective
knowledge management. In this regard, some case
studies have shown that companies that effectively
incorporate data analytics experience significant
improvements in strategic decision-making and
operational efficiency.
However, the literature needs more solid, correct,
and up-to-date evidence on the specific perception
and application of data analytics in the retail sector
and its direct relationship with knowledge
management. This paper aims to fill this gap by
analyzing in detail the level of implementation of
data analytics in retail companies, focusing on
critical dimensions such as data mining, predictive
analytics, and machine learning and their impact on
knowledge management.
Contextually, this paper shows that more than
half of the surveyed employees express
dissatisfaction with the level of data mining,
predictive analytics, and machine learning in their
organizations. Therefore, there is a clear need to
implement innovative resources and solutions that
integrate a high-tech approach to effectively address
information management challenges and contribute
to developing knowledge in the business
environment.
Therefore, this study aims to provide an accurate
and detailed assessment of the current state of data
analytics in the retail sector, identifying specific
areas of improvement to strengthen knowledge
management in organizations. This research
contributes to the field by providing a deeper
understanding of the retail sector's challenges and
opportunities in implementing data analytics,
highlighting the importance of innovative strategies
for data-driven decision-making and organizational
knowledge growth.
2 Literature Review
The comprehensive literature review explores the
fundamental dimensions of data analytics and
knowledge management. In this context, we focus
on the importance of data mining, predictive
analytics, and machine learning in data analytics.
First, the data mining dimension of the data
analytics variable is conceptualized as a process
responsible for extracting and compiling
information from semi-structured and unstructured
sources to streamline data analysis and reporting
processes, [14]. Likewise, it is known that to
improve data extraction, it is essential to work with
specialized programs that include the construction
of the sensor hardware, the development of the
perception algorithm, and the scenario data, [15]. In
that sense, a study conducted in Australia developed
a data mining algorithm that allowed them to
improve data processing through flexible, efficient,
and accessible data mining, [16].
Secondly, [17], indicate that predictive analytics
is an advanced study method responsible for deeply
examining data, reports, and content to predict some
market sector's risks, opportunities, or behaviors.
Likewise, [18], mentions that nowadays, predictive
analytics uses statistical modeling techniques and
new technologies, such as big data and machine
learning, in the elaboration of predictions to achieve
continuous performance improvement and
information flow management. Similarly, research
on oil handling operations in Russia showed that
predictive analytics helps detect environmental risks
and operating personnel's physical condition, [19].
Machine learning, [20], points out that it is a
branch of artificial intelligence that endows
computers with the ability to learn and act according
to the needs and information about the situation. In
addition, [21], [22], states that machine learning
works based on computer programs that allow the
performance of non-explicitly programmed actions
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1548
Volume 21, 2024
based on the information available and the patterns
found in the data. A study in China, which included
machine learning for forest fire forecasting, showed
that it is helpful for disaster prediction, considering
each region's particular characteristics, [23].
On the other hand, the knowledge creation
dimension of the knowledge management variable is
defined as a collective process involving actors
participating in exchanging and integrating different
knowledge to realize innovative ideas, [24], [25].
Similarly, knowledge creation is essential for
organizations to achieve continuous improvement
and become more competitive in the market, as the
new strategies and innovations generated allow
them to satisfy customers in the face of market
changes, [26], [27]. In that framework, research in
Finland stated that knowledge creation requires the
analysis of past and present data to develop new
knowledge that better understands customer needs,
[28].
According to [29], knowledge storage involves
organizing and distributing knowledge in various
databases, intranets, extranets, and information
systems that enable organizations to have a
knowledge map. Likewise, [30], argues that
knowledge storage is fundamental for the
consolidation of an organization's knowledge, as it
allows new theories, patterns, ideas, and information
to be stored, creating a collaborative network that
enables the institution's workers to interact with the
information in order to increase the level of
productivity. In this sense, a study conducted in
Thailand showed that organizations need to carry
out knowledge storage to have the necessary
information available to innovate, lead, and direct
strategies, [31].
3 Methodology
3.1 Design
The planning of this research, which is applied and
non-experimental, focused on a correlational-causal
approach. This enabled a detailed exploration of the
interrelationships between the various dimensions of
data analytics and the critical aspects linked to
knowledge management, [32]. This
methodology was chosen due to its ability to
identify patterns and connections inherent in the
data without disturbing the participants' natural
environment, thus preserving the authenticity of the
work context in the construction industry.
3.2 Inclusion and Exclusion Criteria
The study's intentional sample comprised 351
collaborators, distributed between 189 men and 162
women. Thereby, to obtain this result, specific
inclusion criteria were applied: (a) the age of the
participants had to be between 25 and 50 years, (b)
they had to give their consent to participate in the
research, and (c) they were required to be permanent
workers with at least ten months of work experience
in the retail sector. The choice of this sector for
research is due to its relevance and significant
presence in the business environment, providing an
ideal context to examine the implementation of data
analytics in a dynamic and competitive business
environment. Exclusion criteria, on the other hand,
included (a) submission of incomplete
questionnaires and (b) unwillingness to continue
participating in the study, ensuring the quality and
consistency of the data collected.
3.3 Procedure
The research was carried out from October to
December 2023, during which the participants were
recruited continuously using convenience sampling
until they reached the desired sample size (351
workers), considering there was no incomplete
questionnaire. The data collection technique was the
survey, applying a structured questionnaire using
the Google Forms tool to measure opinion about the
data analytical variable according to its dimensions:
data extraction, predictive analysis, and machine
learning with a total of 15 questions and ten
questions to measure opinion regarding the
knowledge management variable and the
dimensions: knowledge creation and knowledge
storage, using the Likert scale according to the
values good, average and bad. The methodology of
this study emphasizes a detailed understanding of
the various dimensions of data analytics, integrating
them as central axes in the analysis. Integrating
these three dimensions in our methodological
approach allows a comprehensive evaluation of how
data analytics contributes to improving knowledge
management in companies in the retail sector. By
focusing our analysis on these dimensions, we seek
to offer a comprehensive perspective on the benefits
and challenges of properly implementing data
analytics, highlighting its potential to improve
efficiency and effectiveness in knowledge
management.
3.4 Analysis of Data
This study organized the collected data into a
tabulation matrix and processed it using SPSS v25
and Excel statistical software. In this regard, the
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1549
Volume 21, 2024
dimensions of data analytics (data mining,
predictive analytics, and machine learning) should
be measured. Cronbach's Alpha reliability tests were
conducted for the variables related to data analytics
and knowledge management, resulting in a
coefficient of 0.911. This value reflects a high
reliability in the measurements, guaranteeing the
internal consistency of the answers collected
through the questionnaire. During the research
process, descriptive statistics were applied to
perform the frequency distribution of the
dimensions related to data mining, predictive
analytics, and machine learning, as well as the
dimensions of knowledge creation and storage in
organizations. This approach provided a clear
understanding of the general trends in participants'
perceptions and experiences regarding the
implementation of data analytics.
3.5 Ethical Considerations
Ethical principles of research were followed,
ensuring confidentiality and informed consent for all
participants. Personal information and responses
were handled confidentially and used exclusively
for research purposes.
4 Findings
Figure 1 shows the results of the data mining
dimension of the data analytics variable. Some
52.99% of contributors indicate that the level of data
mining could be better, suggesting a severe
deficiency in the organizations' ability to obtain
relevant information from their systems. This poor
data mining performance can significantly limit the
effectiveness of subsequent analysis and informed
decision-making. Only 26.21% consider the level
reasonable, indicating that less than a third of
employees perceive that their organizations
adequately handle this crucial stage of the analytical
process. In addition, 20.80% of employees say the
level is fair, reflecting a general perception that
there is much room for improvement in this area.
Fig. 1: Data extraction dimension level
Figure 2 shows the results of the predictive
analytics dimension of the data analytics variable,
with 57.83% of employees indicating that the level
of predictive analytics is poor. Thus, this indicates
that most employees perceive their organization's
need to leverage predictive tools to anticipate future
trends and behaviors. The fact that only 18.52%
consider the level to be good underlines a worrying
lack of confidence in current predictive analytics
capabilities. 23.65% of respondents believe the level
is fair, suggesting that while some organizations are
on the right track, they still face challenges in
achieving an optimal level of predictive analytics.
Fig. 2: Level of the predictive analytics dimension
Figure 3 shows the results of the machine
learning dimension of the data analytics variable.
54.99% of respondents indicate that machine
learning is fair, suggesting that, although
organizations use this technology, its
implementation and effectiveness are not
consistently high. Furthermore, 23.93% of
respondents consider the level poor, indicating that
essential areas need significant improvement. Only
21.08% say the level is good, highlighting the need
to strengthen machine learning capabilities so that
organizations can fully benefit from its advantages.
Fig. 3: Machine learning dimension level
Figure 4 shows the results of the knowledge
creation dimension of the knowledge management
variable. 41.88% of the contributors indicate that the
level of knowledge creation is fair, suggesting that
although organizations are generating knowledge,
this process could be more effective. 31.05% of the
employees consider the level poor, highlighting the
need to improve strategies and methods for
knowledge creation. On the other hand, 27.07% say
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1550
Volume 21, 2024
the level is good, indicating that some organizations
are achieving good results, although there is still
ample room for widespread improvement.
Fig. 4: Level of the knowledge creation dimension
Figure 5 shows the results of the knowledge
storage dimension of the knowledge management
variable, where a worrying 61.25% of employees
indicate that the level of knowledge storage is poor.
This result highlights a severe deficiency in the
ability of organizations to store and organize
knowledge effectively, which can lead to significant
loss of valuable information and duplication of
effort. Only 26.78% consider the level good, and
11.97% consider it fair, suggesting that current
knowledge storage practices are primarily
inadequate, and that significant improvement is
needed to optimize knowledge management.
Fig. 5: Level of the knowledge storage dimension
4.1 Normality Test
Table 1 shows the results of the Kolmogorov-
Smirnov and Shapiro-Wilk normality tests,
indicating that all dimensions (data mining,
predictive analytics, machine learning, knowledge
creation, and knowledge storage) have a non-
parametric distribution (p < 0.05 for all dimensions).
Because of this, it is recommended to use non-
parametric correlations, such as Spearman's or
Kendall's correlation, instead of Pearson's
correlation, to analyze the relationships between
these variables, as non-parametric tests do not
assume normality in the data.
Table 1. Normality test for Data Analysis and
Knowledge Management dimensions
4.2 Spearman Correlation
As shown in Table 2, Spearman correlation results
have been obtained that provide information on the
relationships between the different dimensions
analyzed in this study. A statistically significant
positive correlation was found between the
dimensions of data mining and knowledge creation
(r=,889, p< .001). This indicates that as the levels of
data extraction improve, workers report a better
level of knowledge creation. Also, a very strong
positive correlation was found between the
predictive analytics and knowledge creation
dimensions (r=,943, p< .001). This suggests that the
better the predictive analytics, the better the
knowledge creation.
Similarly, it was evident that there is a moderate
positive correlation between data mining and
knowledge storage dimensions (r=,498, p< .001).
This indicates that as data extraction improves,
workers report better data storage. Finally, a strong
positive correlation was found between predictive
analytics and knowledge storage (r=,706, p< .001).
This means that as levels of predictive analytics
improve, workers report improved knowledge
storage.
Table 2. Relationship between the dimensions of the
data analytics and work knowledge management
variables
Pruebas de normalidad
Kolmogorov-Smirnova
Stadistic
gl
Sig.
Stadistic
gl
Sig.
Data extraction
,275
351
,000
,800
351
,000
Predictive
analysis
,342
351
,000
,637
351
,000
Machine
learning
,466
351
,000
,541
351
,000
Knowledge
creation
,251
351
,000
,807
351
,000
Knowledge
storage
,316
351
,000
,748
351
,000
a. Corrección de significación de Lilliefors
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1551
Volume 21, 2024
5 Proposal
Based on the survey results, the following data
analytics model is proposed to improve knowledge
management. This model allows evaluating the
current state, applying the model, and obtaining
suitable results.
Figure 6 presents a data analytics model
designed to improve knowledge management in
retail companies. It highlights the transition from the
"Real State" to the "Ideal State" through a series of
specific interventions.
Fig. 6: Strategic proposal for improving data
analytics
In the Real estate, retail companies face several
significant challenges. Inadequate management and
lack of exploitation of available information prevent
organizations from fully utilizing their data. In
addition, this data needs to be integrated more into
developing competitive strategies, which limits their
ability to stay ahead of the market. Finally, more
relevant and valuable information must be provided
to ensure the decision-making process, positively
affecting organizational efficiency and
effectiveness.
The Intervention proposed in the model includes
three key components: data mining, predictive
analytics, and machine learning. Data mining
focuses on obtaining relevant information from
various sources, ensuring the data is complete and
valuable. Predictive analytics uses advanced
techniques to forecast future trends and behaviors,
allowing companies to anticipate market changes.
Machine learning applies algorithms that allow
machines to learn from data and improve their
predictions and decisions over time. These
components are designed to improve two critical
dimensions of knowledge management: knowledge
creation and knowledge storage. Knowledge
creation involves generating new insights from
analyzed data, while knowledge storage refers to
storing and organizing this information efficiently
for future use.
These interventions aim to reach an Ideal State
where data analytics is a fundamental tool for
budget management, profit growth, and industry
participation. In this ideal state, companies can
discover hidden patterns, unknown correlations,
trends, and preferences, using these insights to
develop better business strategies. In addition,
information extraction and the management of
relevant and quality data will be improved,
facilitating informed and effective decision-making.
The contributions of this study are significant for
both theory and practice in the field of knowledge
management and data analytics in the retail sector.
Theoretically, the proposed model provides a clear
and structured framework that integrates data
mining, predictive analytics, and machine learning,
highlighting their impact on knowledge creation and
storage. This provides a solid foundation for future
research exploring or expanding these dimensions.
At the practical level, the implications of this study
are profound. Retail organizations can significantly
improve their information management and
decision-making by identifying deficiencies and
proposing specific interventions. Implementing
these practices can lead to better budget
management, increased profits, and more significant
market share, fostering an organizational culture
based on accurate data and knowledge. In addition,
by improving the ability to uncover hidden patterns
and trends, companies can develop more effective
and competitive strategies, strengthening their
position in the industry.
6 Discussion
In Figure 1, 52.99% of collaborators indicate that
the level of data extraction in the institution is poor,
which shows that the institution has difficulties in
collecting information from databases and
repositories due to compatibility problems, data not
structured, and of poor quality that impair the
processing of information. This agrees with [14] and
[16] who mentioned that data extraction is a
flexible, efficient, and accessible process that brings
together information from semi-structured and
unstructured sources to speed up data analysis and
reporting. Likewise, [15], states that it is necessary
to work with specialized programs to improve data
extraction through the construction of the sensor
hardware, the development of the perception
algorithm, and the scenario data.
In Figure 2, 57.83% of collaborators indicate
that the level of predictive analysis in companies in
the retail sector is poor, which indicates that staff
must be trained to analyze and evaluate the data
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1552
Volume 21, 2024
handled by the company to anticipate the future. and
discover new trends that generate positive results.
Coinciding with [17], [19] who indicate that
predictive analysis is an advanced study system that
focuses on deeply examining a set of data, reports,
and content to predict risks, opportunities, or
behavior in the market. Likewise, [18] maintains
that currently statistical modeling techniques and
new technologies such as big data and machine
learning are tools used by predictive analysis to
make predictions that promote continuous
improvement and improve flow management.
information.
In Figure 3, 54.99% of collaborators maintain
that the level of machine learning in the institution
is regular, showing that the institution has qualified
teams for the development of basic data collection
and organization tasks. However, it is necessary to
implement more complex programs to improve the
response to tasks not explicitly programmed and the
adaptability of the equipment according to the data
stored in the system. This coincides with [20], [23]
who mention that machine learning is part of
artificial intelligence influencing the ability to learn
and act of a computer in relation to the needs that
are present and the information available for the
machine to elaborate a new answer. In addition,
[21], adds that machine learning is carried out by
means of computer programs that use the
information of the system and the patterns present in
the data to carry out unprogrammed actions.
In Figure 4, 41.88% of collaborators affirm that
the level of knowledge creation is regular, which
shows that there is a strong intention to exchange
data and valuable information among the members
of the institution. However, there are difficulties in
the data transfer and processing channels, which
limits the ability to innovate and include new ideas
in the organization's strategies. This agrees with
[24], [28] who mention that the creation of
knowledge is a collective process that includes the
participants in a space to exchange and integrate all
the knowledge they possess with the objective of
formulating new ideas, which allows us to overcome
the competition through constant innovation.
Similarly, [26] points out that the creation of
knowledge is essential to achieve continuous
improvement, increase competitiveness in the
market, and generate innovative strategies to meet
the needs of the organization and customers.
In Figure 5, 61.25% of collaborators mention
that the level of knowledge storage is poor,
demonstrating that there are difficulties in retaining,
organizing, and distributing knowledge in the
institution, limiting the ability to consolidate
knowledge and increase the interaction of members
with valuable information held by the organization.
Coinciding with [29], [31] who indicate that
knowledge storage consists of organizing and
distributing the knowledge of the organization in
various databases, intranets, extranets, and
information systems that improve the provision of
information to innovate, lead, and direct strategies.
Likewise, [30] affirms that the storage of knowledge
allows to safeguard the new theories, patterns, ideas,
and information generated by the organization,
which facilitates the interaction of workers with the
information to increase productivity levels.
7 Conclusions
This research proposal maintains that data analytics
is essential to improving data collection, evaluation,
and analysis, as well as finding patterns,
correlations, and trends that improve the
organization's strategies. This increases the capacity
for innovation and decision-making based on the
new knowledge generated.
Likewise, machine learning allows simple tasks
related to data analytics to respond to the
organization's needs. However, complex programs
that improve the ability to react to unscheduled tasks
through efficient information management must be
implemented. Also, information can adapt data
patterns automatically in the responses provided by
technological equipment in unknown situations.
Similarly, creating knowledge actively promotes the
implementation of spaces for exchanging essential
data and information. However, the knowledge
distribution and analysis network must be improved
and automated to integrate them into organizational
strategies.
On the other hand, data extraction is deficient
since the process of collecting information from
databases faces incompatibility problems,
unstructured or semi-structured data, poor data
quality, and data security, which limits the ability of
computers to recognize, collect, analyze, process,
and organize information. In addition, predictive
analytics cannot make safe and accurate predictions
based on available data, so the information tends to
be distorted, inadequate, or difficult to understand.
Finally, knowledge storage is inadequate because no
secure methods exist to retain, organize, and
distribute the information generated. This limits the
organization's ability to consolidate the knowledge
produced, share the information, and include it in
developing strategies, decision-making, and
organizational plans.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1553
Volume 21, 2024
8 Limitations, Recommendations and
Future Work
While providing a valuable assessment of the
current state of data analytics in the retail sector, this
study has certain limitations. First, the research was
based on surveys only, which may introduce self-
reporting biases and limit the depth of insights
obtained. In addition, the sample is restricted to
employees of companies in the retail sector, which
may be different from other sectors or have a
broader view of the problem. Future studies should
incorporate mixed methodologies that include
qualitative and quantitative approaches and expand
the sample to include different sectors and
hierarchical levels within organizations. In addition,
it is suggested that longitudinal studies be
implemented to observe the evolution and impact of
data analytics over time. Finally, future research
could further explore the barriers organizations face
in implementing advanced data analytics
technologies and how these can be overcome
through training and organizational change
strategies.
Acknowledgement:
We are grateful to the Universidad San Ignacio de
Loyola for their support during the conduct of this
research. Also, the authors would like to
acknowledge the financial support of the National
Funds provided by FCT-Foundation for Science and
Technology to VALORIZA-Research Center for
Endogenous Resource Valorization (project
UIDB/05064/2020).
Declaration of Generative AI and AI-assisted
technologies in the writing process
During the preparation of this work the authors used
Chat GTP in order to briefly clarify joint concepts.
After using this tool/service, the authors reviewed
and edited the content as needed and take full
responsibility for the content of the publication.
References:
[1] M. Pohl, D. Staegemann, and K. Turowski,
“The Performance Benefit of Data Analytics
Applications,” Procedia Comput Sci, vol.
201, pp. 679–683, Jan. 2022, doi:
10.1016/J.PROCS.2022.03.090.
[2] N. Nyoman, B. Tekinerdogan, C. Catal, and
R. Tol, “Data analytics platforms for
agricultural systems: A systematic literature
review,” Comput Electron Agric, vol. 195, p.
106813, Apr. 2022, doi:
10.1016/J.COMPAG.2022.106813.
[3] A. Geistanger, K. Braese, and R. Laubender,
“Automated data analytics workflow for
stability experiments based on regression
analysis,” Journal of Mass Spectrometry and
Advances in the Clinical Lab, vol. 24, pp. 5
14, Apr. 2022, doi:
10.1016/J.JMSACL.2022.01.001.
[4] A. Perdana, H. Lee, D. Arisandi, and S. Koh,
“Accelerating data analytics adoption in
small and mid-size enterprises: A Singapore
context,” Technol Soc, vol. 69, p. 101966,
May 2022, doi:
10.1016/J.TECHSOC.2022.101966.
[5] A. Dacal, J. Areal, V. Alonso, and M. Lluch,
“Integrating a data analytics system in
automotive manufacturing: background,
methodology and learned lessons,” Procedia
Comput Sci, vol. 200, pp. 718–726, Jan.
2022, doi: 10.1016/J.PROCS.2022.01.270.
[6] W. Sardjono, Harisno, and W. G. Perdana,
“Improve Understanding and Dissemination
of Disaster Management and Climate Change
by Using Knowledge Management Systems,”
IOP Conf Ser Earth Environ Sci, vol. 426,
no. 1, p. 012158, Feb. 2020, doi:
10.1088/1755-1315/426/1/012158.
[7] O. Barbón and J. Fernández, Role of
strategic educational management in the
management of knowledge, science,
technology and innovation in higher
education (“Rol de la gestión educativa
estratégica en la gestión del conocimiento, la
ciencia, la tecnología y la innovación en la
educación superior”), Educación Médica,
vol. 19, no. 1, pp. 51–55, Jan. 2018, doi:
10.1016/J.EDUMED.2016.12.001.
[8] M. Mohammad, R. Abdullah, A. Jabar, W.
Sardjono, M. Mukhlis, and E. Selviyanti,
“Factors to increasing the employee
performance through knowledge
management systems implementation at PT.
XYZ,” J Phys Conf Ser, vol. 1563, no. 1, p.
012023, Jun. 2020, doi: 10.1088/1742-
6596/1563/1/012023.
[9] R. Abdul, D. Maria, S. Laila, and M. Azima,
“Development of Knowledge Management
System for Determining Organizational
Performances, Total Quality Management,
And Culture,” J Phys Conf Ser, vol. 1529,
no. 2, p. 022063, Apr. 2020, doi:
10.1088/1742-6596/1529/2/022063.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1554
Volume 21, 2024
[10] G. Li and L. Jiajun, “Automatic Analysis
And Intelligent Information Extraction Of
Remote Sensing Big Data,” J Phys Conf Ser,
vol. 1616, no. 1, p. 012003, Aug. 2020, doi:
10.1088/1742-6596/1616/1/012003.
[11] S. Yusoff, N. Noh, and N. Isa, “University
Students’ Readiness for Job Opportunities in
Big Data Analytics,” J Phys Conf Ser, vol.
2084, no. 1, p. 012026, Nov. 2021, doi:
10.1088/1742-6596/2084/1/012026.
[12] R. Rawat and R. Yadav, “Big Data: Big Data
Analysis, Issues and Challenges and
Technologies,” IOP Conf Ser Mater Sci Eng,
vol. 1022, no. 1, p. 012014, Jan. 2021, doi:
10.1088/1757-899X/1022/1/012014.
[13] A. Rojas, J. Londoño, N. Pérez, and M.
Gómez, “Analysis of the big data generated
in the company’s social networks ‘Sistemas
Expertos SAS’ using NVivo,” J Phys Conf
Ser, vol. 1418, no. 1, p. 012004, Dec. 2019,
doi: 10.1088/1742-6596/1418/1/012004.
[14] S. Hu and H. Yin, “Research on the optimum
synchronous network search data extraction
based on swarm intelligence algorithm,”
Future Generation Computer Systems, vol.
125, pp. 151–155, Dec. 2021, doi:
10.1016/J.FUTURE.2021.05.001.
[15] A. Razak, S. Asmah, L. Wang, B. Yu, and C.
Chen, “Parking Area Data Collection and
Scenario Extraction for the Purpose of
Automatic Parking ADAS Function,” IOP
Conf Ser Mater Sci Eng, vol. 780, no. 3, p.
032026, Mar. 2020, doi: 10.1088/1757-
899X/780/3/032026.
[16] J. Nolde, A. Mian, L. Schlaich,, J. Chan, L.
Lugo-Gavidia, N. Barrie, V. Gopal, G. Hillis,
C. Chow and M. Schlaich. “Automatic data
extraction from 24 hour blood pressure
measurement reports of a large multicenter
clinical trial,” Comput Methods Programs
Biomed, vol. 214, p. 106588, Feb. 2022, doi:
10.1016/J.CMPB.2021.106588.
[17] A. Tolba and Z. Al, “Predictive data analysis
approach for securing medical data in smart
grid healthcare systems,” Future Generation
Computer Systems, vol. 117, pp. 87–96, Apr.
2021, doi: 10.1016/J.FUTURE.2020.11.008.
[18] A. Mbakop, F. Biyeme, J. Voufo, and J.
Lucien, “Predictive analysis of the value of
information flow on the shop floor of
developing countries using artificial neural
network based deep learning,” Heliyon, vol.
7, no. 11, p. e08315, Nov. 2021, doi:
10.1016/J.HELIYON.2021.E08315.
[19] T. Drozdova and A. Vereshchagina,
“Predictive assessment of man-made risks
during oil-handling operations at tank
farms,” IOP Conf Ser Earth Environ Sci, vol.
408, no. 1, p. 012017, Jan. 2020, doi:
10.1088/1755-1315/408/1/012017.
[20] K. Jayareka, P. Sobiyaa, Kaladevi,
Vinodhini.V, and B. Suman, “An effective
automatic detection of tooth cavity using
machine cum deep learning concepts and
ICDAS measurement,” Mater Today Proc,
May 2022, doi:
10.1016/J.MATPR.2022.05.109.
[21] M. Su, B. Liang, S. Ma, C. Xiang, C. Zhang,
and J. Wang, “Automatic Machine Learning
Method for Hyper-parameter Search,” J Phys
Conf Ser, vol. 1802, no. 3, p. 032082, Mar.
2021, doi: 10.1088/1742-
6596/1802/3/032082.
[22] R. Castilla, A. Pacheco, I. Robles, A. Reyes,
and R. Inquilla, “Digital channel for
interaction with citizens in public sector
entities,” World Journal of Engineering, vol.
18, no. 4, pp. 547–552, 2020, doi:
10.1108/WJE-08-2020-0377. .
[23] J. Qu and X. Cui, “Automatic machine
learning Framework for Forest fire
forecasting,” J Phys Conf Ser, vol. 1651, no.
1, p. 012116, Nov. 2020, doi: 10.1088/1742-
6596/1651/1/012116.
[24] A. Nisula, K. Blomqvist, J. Bergman, and S.
Yrjölä, “Organizing for knowledge creation
in a strategic interorganizational innovation
project,” International Journal of Project
Management, vol. 40, no. 4, pp. 398–410,
May 2022, doi:
10.1016/J.IJPROMAN.2022.03.011.
[25] J. Ore, A. Pacheco, E. Roque, A. Reyes, and
L. Pacheco, “Augmented reality for the
treatment of arachnophobia: exposure
therapy,” World Journal of Engineering, vol.
18, no. 4, pp. 566–572, 2020, doi:
10.1108/WJE-09-2020-0410. .
[26] K. Al, D. Palacios, and K. Ulrich, “The
impact of intellectual capital on supply chain
agility and collaborative knowledge creation
in responding to unprecedented pandemic
crises,” Technol Forecast Soc Change, vol.
178, p. 121603, May 2022, doi:
10.1016/J.TECHFORE.2022.121603.
[27] R. Castilla, A. Pacheco, and J. Franco,
“Digital government: Mobile applications
and their impact on access to public
information,” SoftwareX, vol. 22, p. 101382,
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1555
Volume 21, 2024
May 2023, doi:
10.1016/J.SOFTX.2023.101382.
[28] S. Juvonen, J. Koivisto, and H. Toiviainen,
“Knowledge creation for the future of
integrated health and social services: Vague
visions or an expansion of activity?,” Learn
Cult Soc Interact, p. 100613, Feb. 2022, doi:
10.1016/J.LCSI.2022.100613.
[29] C. Mubin and Y. Latief, “Organizational
culture influence on implementation of
knowledge management and quality
management system for improving
Indonesian construction companies’
performances,” IOP Conf Ser Mater Sci Eng,
vol. 508, no. 1, p. 012037, Apr. 2019, doi:
10.1088/1757-899X/508/1/012037.
[30] M. Alim, A. Nugroho, S. Arif,
Murtiningrum, and L. Sutiarso,
“Development of knowledge management
system for assisting the Agrotechno Edu-
park establishments in Sriharjo village,
Imogiri district, Bantul regency,” IOP Conf
Ser Earth Environ Sci, vol. 542, no. 1, p.
012064, Jul. 2020, doi: 10.1088/1755-
1315/542/1/012064.
[31] P. Chaithanapat, P. Punnakitikashem, N. C.
Khin Khin Oo, and S. Rakthin,
“Relationships among knowledge-oriented
leadership, customer knowledge
management, innovation quality and firm
performance in SMEs,” Journal of
Innovation & Knowledge, vol. 7, no. 1, p.
100162, Jan. 2022, doi:
10.1016/J.JIK.2022.100162.
[32] R. Hernández and C. Mendoza, Research
methodology: the three quantitative,
qualitative and mixed routes (Metodología
de la investigación: las tres rutas
cuantitativa, cualitativa y mixta). México:
Mc Graw Hill- educación, 2018.
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Rosario Pariona-Luque and Alex Pacheco were in
charge of writing and revising the article.
- Edwin Vegas-Gallo, Edwin Felix-Poicon and Rui
Alexandre Castanho, Ana Loures, and Liz
Pacheco-Pumaleque carried out the
conceptualisation and methodology of the
research.
- Fabian Lema collected and curated the data.
- Marco Añaños-Bedriñana and Wilson Marin
conducted the research.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.126
Rosario Pariona-Luque, Alex Pacheco,
Edwin Vegas-Gallo et al.
E-ISSN: 2224-2899
1556
Volume 21, 2024