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