Problems and Solutions of Data Analytics in Business
Liwei Liu
a
School of Engineering and Applied Science, Gonzaga University, Spokane, U.S.A.
Keywords: Data Quality, Data Breaches, Business Data Analysis, Data Encryption, Interdepartmental Collaboration.
Abstract: Nowadays, business cannot thrive without data, and naturally, data analytics has become an indispensable
tool in business. Data analysis can not only intuitively provide decision-making support, but also help
enterprises to understand the market and customers more deeply and provide enterprises with more accurate
and efficient programs and development direction. But while data analytics in business provides results for
companies, there are also not a few problems. This study analyzes the problems of data analytics in business
by analyzing them and proposing countermeasures. This paper examines four problems of data analysis in
enterprises: data quality problem, data leakage problem, inaccurate data analysis problem, and data
application problem. In response to these problems, this paper establishes a data processing mechanism,
encrypts data, upgrades the tools and related talents for data analysis, and promotes good communication
among departments, so as to ensure the stability of data quality, prevent the leakage of information, improve
the accuracy rate, and enable better application in reality.
1 INTRODUCTION
In today's competitive and ever-changing business
environment, data analytics has become a critical
success factor for organizations. With the
advancement of digital transformation, companies
can use data analytics to get the results they want to
make strategic decisions, optimize operational
processes, and enhance customer experience,
ultimately determining the direction of the company.
Whether it's market trend forecasting, customer
information analysis, or supply chain optimization,
data analytics is driving companies to achieve more
efficient and competitive business models (Wang,
2024). These phenomena show that data analytics has
never been more important in business.
With the rapid advancement of data analytics,
organizations have undergone a dramatic
transformation in their ability to collect, store, process
and apply data. For starters, the rise of big data
technologies has enabled organizations to handle
unprecedented scales of data and extract valuable
information from it. For example, Amazon has used
its powerful data analytics to boost sales through
personalized recommendation systems (See Figure 1)
(Huang, Jiang, Wu, & Wang, 2020).
a
https://orcid.org/0009-0007-5621-2765
Second, the application of machine learning and
deep learning has enabled companies to make more
accurate predictions and decisions. These
technologies can automate the processing of large
amounts of complex data and identify potential
patterns and trends, thus helping enterprises gain an
advantage in the fierce market competition. For
example, an innovative decision support model
“AlphaVision” designed for stock price prediction by
seamlessly integrating real-time news updates and
Return on Investment (ROI) values, utilizing various
machine learning and deep learning approaches
(Divyashree et al., 2024).
However, while data analytics technologies
present tremendous opportunities, they are also
accompanied by a new set of challenges and
problems. In the face of these rapidly changing
technological and market environments, in-depth
analysis and optimization of data analytics
technology has become inevitable. Enterprises must
not only rely on existing technical means, but also
need to comprehensively improve the efficiency and
accuracy of data analysis from multiple dimensions,
such as data quality, data security, analytical tools and
talents, as well as cross-departmental cooperation.
Only in this way can enterprises maintain a leading
position in the future market competition and
Liu, L.
Problems and Solutions of Data Analytics in Business.
DOI: 10.5220/0013207700004568
In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence (ECAI 2024), pages 123-127
ISBN: 978-989-758-726-9
Copyright © 2025 by Paper published under CC license (CC BY-NC-ND 4.0)
123
Figure 1: Example of Amazon.
continue to realize business growth. Therefore, this
paper will deeply explore the key issues of data
analytics in business applications and put forward
corresponding countermeasures, with a view to
providing valuable reference and guidance for
enterprises in the data-driven era.
2 PROBLEMS
2.1 Data Quality
In this era of information explosion, data and
information from a variety of sources, data types, so
when this paper is collecting data there will be
incomplete data, data duplication, and even incorrect
data. Handle and clean the data is one of the perennial
challenges in data analytics (Chu, Ilyas, Krishnan, &
Wang, 2016). Therefore, the quality of data is a key
determinant of the quality of decisions made
(Choughri, et al., 2018). When the data is duplicated,
it will cause great errors in data analysis. In life it is
also very easy to have data entry errors that lead to
incorrect data. When this paper unknowingly enters
incorrect information, it can make it difficult to find
effective ways to refine and modify the model. In
daily life, data entry errors are also a major cause of
incorrect data. For example, users may double-enter
information, or machines may make mistakes during
testing and recognition, all of which can make the
quality of the data much worse. And when this paper
unknowingly enters this incorrect information into
the system, this paper may be stuck in a predicament
where it is difficult to figure out effective ways to
refine and modify the model. These are all factors that
contribute to data quality problems, when the results
of the analysis may be biased and lead to poor
decision-making.
2.2 Data Leakage
As the amount of data increases, the issue of data
privacy and security becomes more and more
important. All that is needed for data analytics is a lot
of data, and the more data is piled up then the greater
the risk of leakage. Chen et al. (2017) discussed
relationships revealed in this study. As the volume
of data is growing exponentially and data breaches
are happening more frequently than ever before,
detecting and preventing data loss has become one of
the most pressing security concerns for enterprises.
. So, the more organizations use and store data, the
greater the risk of data leakage and unauthorized
access. Cyber attackers will invade the enterprise
network through malware, phishing attacks, DDoS
attacks and other means to steal sensitive data. As
technology advances, attackers' tactics become more
sophisticated, and traditional firewalls and anti-virus
software can no longer fully defend against these
advanced threats. In addition, corporate insiders may
also cause data leakage through intentional or
unintentional behavior. Insiders often have higher
privileges and direct access to data, so when they are
the source of a breach, the damage is often more
severe. A data breach can then lead to data analysis
results that may become inaccurate for company
decisions or even affect the advancement of data
analysis. Not only can this lead to financial losses, but
it can also have a serious impact on a company's
reputation.
2.3 Inaccurate Data Analysis
The business market is evolving rapidly, so the cost
of trial and error for companies is remarkably high.
So, the accuracy of data analysis is crucial for
companies. Due to the differences in the selection of
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data analysis tools and the differences in the
professionals, so the problem of inaccuracy may
occur in the market and in the decision making of
companies using data analysis. When data analysts
are not technically literate enough, it may be difficult
to extract valid values from the data or perform more
complex and large data analysis. When the tools do
not match the actual needs, there is a high probability
of overfitting and inefficient analysis effects. The
impact of these data analysis inaccuracies on the
company and the market is huge. Once the error is
large it may lead to poor decision making and wrong
direction of development of the company, resulting in
irreversible consequences.
2.4 Application of Data Analytics
Even if the results and accuracy of the data analysis
are good, it is useless when the results of the analysis
are not well applied to the market and the company's
decisions and actions. If companies do not make good
use of data processing and utilization in the market,
then they will be eliminated by their competitors (Hu,
2018). Just like our human body, when our brain
receives the information and completes the
instructions, if our nerve center is not able to transfer
the instructions accurately and quickly to all the
organs of our body, then our body won't be able to
function properly. In reality, when a data analytics
scientist analyzes market and company data and gets
a result, and the business department is unable to
understand the result and distribute it to the executive
departments, then the result is not well applied in
decision making and action.
3 SOLUTIONS
3.1 Reasonable Processing of Data
For the problem of data quality, what this paper has
to do is to improve the quality of data, that is, to make
reasonable processing of data. This paper can carry
out data cleansing to clean up the missing values and
duplicate values in the data set. In the enterprise there
should be data analysis scientists according to the
different requirements of each project, to establish a
set of appropriate data cleaning mechanism, to help in
data analysis in the data analysis to improve and
optimize the data analysis, regular review of the data,
fill in the missing values, remove duplicate data, so as
to improve the quality of the data. For example, for
missing data, data preprocessing and missing value
verification are performed to ensure data integrity.
For duplicate data, data cleansing techniques are used
to remove duplicate data and ensure that each piece
of data appears only once in the data set. For incorrect
data, the end can be achieved by multiple data entry
and comparing the correctness. Moreover, the
company should also develop a unified standard and
process for data processing, so that whenever a new
data set is added to the analysis, it will be
standardized in this process, maintaining the integrity
and consistency of the data, so that the data can be
more accurately applied to data processing.
3.2 Encrypting the Data
To solve the data leakage problem, enterprises should
strengthen both external protection and internal
prevention. First of all, strict access policies and
management rights should be formulated so that only
core personnel or authorized personnel can access.
When accessing and processing important data, you
can let multiple top-ranking data analytics scientists
have some of the permissions, and only when the
majority of scientists are authorized can they legally
access it. In this way, even if there is a problem with
an insider, it will not affect the entire data leakage.
Second, the data can be strictly protected using
encryption so that even if the company's data is
hacked, it cannot be accessed illegally. This is
equivalent to provide a double insurance for the
protection of data, for example, this paper can use
irreversible encryption algorithm for data processing,
which can effectively prevent reverse decryption. Just
like the new encryption method of data shown in the
study by Eltayieb et al., (2024) the encryption method
of CLPRE-CRF is proposed to prevent data leakage
and enhance data security. Then this paper also needs
to strengthen the risk assessment of data reading to
prevent violent decryption to obtain data. Finally, an
inspection team can be established, specifically
responsible for data protection, to conduct regular
data security checks, identify and repair potential
security gaps, to ensure that data security measures
are always effective. Once a data leakage problem
occurs, it will be immediately investigated and solved
to ensure that the data leakage problem is solved at
the first time.
3.3 Elevation Tools, Talents and Models
To improve the accuracy of data analysis, it is
necessary to improve both tools and talents. For the
selection of multiple analysis tools, enterprises
should choose appropriate analysis tools and
technologies according to the project and market clear
Problems and Solutions of Data Analytics in Business
125
needs and goals. In addition, a variety of analysis
tools and techniques can be selected to get different
results. Multiple small-scale pilot projects can be
established based on the different results of the
analysis before applying them to the whole project, so
as to test out the most accurate and compliant analysis
tools. For professionals, companies should increase
the education and training of analysts to improve the
data analysis ability of employees. In addition, it can
also be improved by external factors, such as the
introduction of excellent data analysis talents with
high technical literacy, and the cooperation with
professional data analysis organizations to complete
the project. By further improving the tools and
talents, the accuracy of data analysis will be greatly
guaranteed. In addition, the accuracy can be ensured
by adjusting the model itself. Enterprises can also
continuously optimize the analysis model and assess
the accuracy and applicability of the model through
the verification of historical data (Vanwinckelen,
2012). For example, previous historical data can be
used as a validation set to observe the fitting effect of
the analytical model, so as to adjust and analyze the
accuracy of the model. The accuracy of the prediction
can also be improved by using several different
models for prediction and analyzing the results
comparatively. In conclusion. The prediction of the
accuracy of data analysis must not be too high, when
the enterprise is making decisions, it is necessary to
consider the uncertainty of the prediction results and
reduce the risk of decision-making by developing a
variety of response options and risk management
strategies.
3.4 Enhancing Collaboration Across
Sectors
Firstly, this paper must focus on strengthening the
synergy between all parts of the organization. This
means strengthening the communication and
collaboration between the data analysis team and the
business units to ensure that the analysis results are
highly aligned with the actual business needs. In order
to achieve this goal, this paper should establish a
double-check working concept, i.e., in every
project, the business department and the data analysis
department should communicate and report to each
other twice. This minimizes errors and ensures that
both parties have the same understanding and
expectations of the project. Through frequent
communication and feedback, this paper is able to
identify and resolve potential problems in a timely
manner, thereby improving the quality and efficiency
of the project. Second, to avoid errors due to poor
presentation and understanding, this paper should
present data and results in a more visual way. This
can be done through the use of charts, documents and
other forms of presenting complex data processing
processes and results so that other departments can
understand and apply the information in a more
intuitive way. The impact of visualization in big data
analysis is very important for business. It is a
technique of displaying information in a graphical
format that makes it easy for users to understand the
information (Mahajan, & Gokhale, 2017). For
example, this paper can create data dashboards,
reports, and visual models that present key metrics
and trends to business units in a clear and
understandable way. As a result, task assignment and
execution will become more efficient and precise
because everyone will be able to make decisions
based on the same understanding and data. Finally,
before each project acceptance, this paper should
convene a meeting of all project department heads to
provide joint feedback on the results and their
application in decision-making and action, and to
understand the specific progress so far. During the
meeting, departments can share their experiences and
findings, make suggestions for improvement, and
adjust data analysis and optimization strategies based
on feedback from other departments. This cross-
departmental cooperation and communication will
help ensure smooth progress and provide valuable
lessons learned for future projects. By working
together, this paper can continue to improve the
quality and value of data analysis and provide strong
support for business development. In conclusion,
through enhanced collaboration, visual data
presentation and cross-departmental feedback
mechanisms, this paper can improve the accuracy and
usefulness of data analysis to better support business
decisions and actions. This will help enterprises gain
advantages in the competitive market and realize
sustainable development.
4 CONCLUSIONS
In the current business environment, data analytics
has undoubtedly become a core tool for enterprises to
gain competitive advantage. However, data analytics
faces many challenges in its practical application.
Through in-depth analysis of four major problems:
data quality, data leakage, inaccurate data analysis,
and data analysis application, this study proposes
targeted solutions, including the establishment of a
sound data processing mechanism, the enhancement
of data encryption technology, the improvement of
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the match between analytical tools and talents, and
the promotion of cross-departmental collaboration.
These countermeasures can help enterprises deal with
various problems in data analysis more effectively
and ensure the accuracy and security of data, so that
they can develop better with the support of data
analysis.
Future research directions can focus on the
innovation and practical application of data analysis.
Firstly, with the continuous development of artificial
intelligence and machine learning technology, the
technology of data analysis is also improving, how to
better apply these technologies in data analysis to
improve the accuracy of prediction and rationality of
decision-making will be one of the important future
research directions. Secondly, better application of
data analytics to company development and decision
making, more accurate as well as rational application
in business is also a very important research direction
in the future.
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