Research on the Reform of Enterprise Financial Analysis in the Era
of Great Wisdom Propelling Clouds
Xinyue Peng
1
and Zhiwei Zhang
2
1
Southwest Petroleum University, Nanchong, Sichuan, China
2
Intelligent Financial Technology and System Key Laboratory of Nanchong City, Southwest Petroleum University,
Nanchong, Sichuan, China
Keywords: Great Wisdom Propelling Clouds, Change Analysis, Path Research.
Abstract: Along with the further development of our social economy and science and technology, "Big Data",
"Intelligence", "Mobile Internet" and "Cloud Computing" (hereinafter referred to as "Great Wisdom
Propelling Clouds ") has become the main theme of today's era. The birth of these technologies provides a
breeding ground for new development concepts, the cradle of incubating new reform strategies. This paper
first explains the definition of Great Wisdom Propelling Clouds, and then through the study of the
limitations of the current enterprise financial analysis, so as to find out the opportunities and challenges
brought to the enterprise financial analysis in the era of Great Wisdom Propelling Clouds. Based on this, the
direction of the reform path is put forward for the financial analysis of the enterprise. Finally, specific
results the enterprise reform are expounded. This paper makes an in-depth analysis of the content and form
of financial management of enterprises, which provide a theoretical reference for enterprises to implement
financial analysis of big data enterprises and have certain practical significance.
1 INTRODUCTION
"Great Wisdom Propelling Clouds ", as the name
suggests, refers to the era of "big data",
"intelligence", "mobile Internet" and "cloud
computing". With the progress of national science
and technology and the development of social
productivity, the concept of "Great Wisdom
Propelling Clouds" was first proposed at the China
Internet Conference in August 2013. (Cao 2017)
Then at the 2014 China Internet Conference, Wu
Hequan said that with the arrival of the era of "Great
Wisdom Propelling Clouds," social economy will
face new challenges and create a new trend of
integration. Under a series of documents issued by
the central government to promote China 's financial
informatization, enterprises around the country have
adopted ' big data ', ' intelligent ', ' cloud computing '
and other technologies to carry out financial
management of enterprises, which has accelerated
the pace of the era of ' Great Wisdom Propelling
Clouds ' and also produced new challenges to
traditional financial management.
Big datum refers to a collection of nouns that are
far more complex than conventional data in terms of
capture, storage, and analysis. It has five
characteristics: large scale, high-speed circulation,
diverse value types, low value density, and
authenticity. Intelligentization also includes the
combination of the Internet and the Internet of
Things to meet all kinds of human needs. Mobile
Internet integrates communication and Internet to
realize communication anytime and anywhere.
Cloud computing refers to splitting the huge data
into small parts and reprocessing through the system
to achieve the final purpose of calculation.
Through the research on the current situation of
enterprise financial analysis, it is found that
enterprises face many challenges in financial
analysis, such as difficult to use existing technology
to analyze and process massive data, insufficient
data sources, single analysis method, one-sided
analysis and so on. In the era of great intelligence
and cloud shift, traditional financial analysis is
bound to be innovated to conform to the
development of the time. The establishment of big
data financial analysis platform for enterprises can
not only realize data sharing in domestic and foreign
industries, timely access to macro and micro policy
information, but also realize real-time
decision-making and future trend analysis, improve
Peng, X. and Zhang, Z.
Research on the Reform of Enterprise Financial Analysis in the Era of Great Wisdom Propelling Clouds.
DOI: 10.5220/0011350500003440
In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 831-839
ISBN: 978-989-758-593-7
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
831
the efficiency and influence of financial analysis,
and then provide strong support for managers to
make scientific decisions.
2 IMPACT ON ENTERPRISE
FINANCIAL ANALYSIS IN THE
ERA OF GREAT WISDOM
PROPELLING CLOUDS
2.1 Limitations of Current Enterprise
Financial Analysis
2.1.1
Limitations of Data Sources
Enterprise financial analysis is based on enterprise
financial report information, internal accounting
report information and other related information.
This kind of information has three specific forms.
The first is the most original paper file data. The
second is the data recorded by accountants in
Internet memory, and the third is the logical
structure that can be inferred between these data,
which are also a special type of data. (Hu 2018) The
above three types of data are all structured data.
Although they come from a high degree of
reliability within the enterprise, the number of data
is very small, and most of these data are historical
data, which is of little reference value to the present.
In this case, the first step of financial analysis cannot
effectively obtain the source of data, so it is difficult
to continue the effective evaluation and reasoning.
Moreover, based on the asymmetry of information,
enterprises will appear adverse selection and moral
hazard. It is difficult for enterprises to master the
data information of other competitors in the same
industry, and it is difficult to obtain valuable
resources for enterprises themselves in public
limited data. Therefore, the limitation of data
sources has become one of the obstacles to financial
analysis.
2.1.2 Limitations of Professional
Thinking
For the managers of most enterprises, it is their
vision to maximize shareholder wealth. Therefore,
most of them only pay attention to the indicators of
operating income and operating profit, while
ignoring the importance of financial analysis for
enterprise development. For small-scale or growing
enterprise financial workers, Financial analysis of
the enterprise is carried out by relying solely on a
few financial statements and limited internal data,
and the conclusions are reported to managers. Thus,
the analysis made when the enterprise financial
personnel do not fully grasp the specific financial
situation of the enterprise may have relatively large
errors with the actual situation. Such financial
analysis has no reference value for enterprises. With
the further development of enterprises and the
gradual maturity, the market has higher and higher
requirements for enterprises, and the requirements of
enterprises for internal financial analysts should also
be improved. For the traditional financial personnel,
it is difficult to continue to carry out effective
financial analysis and put forward constructive
suggestions or solutions for enterprises if they only
master the basic knowledge they have
learned before,
so they are self-contained and no longer further
study and improve. Therefore, breaking the
limitation of professional thinking is also an
important means for the survival and development
of enterprises in the era of Great Wisdom Propelling
Clouds.
2.2 Opportunities and Challenges
Brought to the Financial Analysis
of Enterprises in the Era of Great
Wisdom Propelling Clouds
2.2.1 The Arrival of the Era of Great
Wisdom Propelling Clouds Can
Bring New Opportunities for
Enterprise Financial Analysis
As mentioned above, the traditional financial
analysis of enterprises is mostly based on internal
data such as financial statements. These data are
mostly static structural historical data, which have
low reference value for financial analysis of
enterprises. Therefore, the limitations of data
sources are a major constraint for financial analysis
of enterprises. However, with the advent of the era
of Great Wisdom Propelling Clouds, the use of big
data, the Internet, the Internet of Things, cloud
computing and other means can accurately and
quickly mine and capture more effective dynamic
data, which greatly improves the accuracy and
availability of data, and has more reference
significance and use value for evaluating various
financial indicators of enterprises. Of course, for the
operation of intelligent cloud computing, the
requirements for financial workers are also
increasing. Therefore, the status of financial analysis
in the financial management of enterprises is also
increasing, and financial workers are increasingly
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
832
favored by managers. This has laid a good
foundation for enterprises to establish specific
analysis departments or analysts, and played a
certain role in improving the management
organizational structure and optimizing the
allocation of resources. (Wang 2017)
2.2.2 The Arrival of the Era of Great
Wisdom Propelling Clouds Can
Bring New Challenges to Corporate
Financial Analysis
In order to make full use of the Internet, big data,
cloud computing and other tools to contribute to the
financial analysis of enterprises, the corresponding
supporting hardware facilities of enterprises also
need to be further followed up. However, the R & D
expenditure and cost of hardware facilities are a
large amount of expenses that enterprises need to
consider. Moreover, the software for data processing
and processing that meet the needs of enterprises is
not yet mature, which cannot be adapted to local
conditions and used in accordance with the time.
Secondly, financial analysis in the era of Great
Wisdom Propelling Clouds has certain professional
requirements for operators. Firstly, the gap of
professional and technical talents cannot be
compensated. Financial analysis talents are still the
major colleges and universities should focus on
training. For the financial workers who have been
employed, they are faced with the dual test of
strengthening their professional knowledge and
constantly learning big data intelligent financial
technology. Therefore, it brings new opportunities in
the era of Great Wisdom Propelling Clouds, but also
brings new challenges to enterprises.
3 CONSTRUCTION OF
ENTERPRISE FINANCIAL
ANALYSIS PLATFORM
STRUCTURAL SYSTEM IN
THE ERA OF GREAT WISDOM
PROPELLING CLOUDS
3.1 Platform Function Settings
3.1.1 Financial Index Analysis
The purpose of building a financial analysis
platform is to analyze financial indicators more
effectively, which requires that the platform
structure system can comprehensively cover the four
basic financial indicators, and can focus on
reflecting different financial conditions, providing
information related to decision-making for internal
information users such as enterprise managers and
external information users such as investors and
creditors. In the traditional financial analysis,
enterprises often use the DuPont analysis system
method (Figure 1). In this method, enterprises
decompose the equity net interest rate into the
product of sales net interest rate, asset turnover rate
and equity multiplier, so that the complex indicators
can be decomposed into specific and feasible
indicators. It provides a valuable reference for
investment and financing decisions of enterprises
from two aspects of business leverage and financial
leverage. However, the DuPont analysis system
method also has some shortcomings. Firstly, this
method focuses on the analysis of financial
information and ignores the impact of non-financial
information on enterprises, such as consumers and
suppliers. Secondly, this method is applicable to
short-term business decisions, but not to long-term
strategic decisions. In addition, DuPont analysis
system is based on the analysis of historical data,
which cannot meet the needs of future
decision-making for growing enterprises.
net interest rate
net interest rate equity multiplier
n
et profit on net sale Asset turnover rate
profit Sales revenue Sales revenue Total assets
Sales revenue-all costs + other
profits-income tax
Long-term assets +
current assets
Figure 1: Diagram of DuPont Financial Analysis System.
In the era of Great Wisdom Propelling Clouds,
intelligent enterprises based on big data can break
through the blind spots and omissions in the past
methods one by one on the basis of the existing
financial analysis methods, increase the
management of non-financial information such as
consumers and suppliers, and adjust the financial
analysis system in time in combination with the
constantly updated data in the development of
enterprises, so as to continuously improve and
progress the financial analysis system. Based
on this,
enterprises can consider combining with the
balanced scorecard in corporate strategy (Figure 2)
to grasp the overall financial analysis of enterprises
from four aspects: finance, customers, internal
business processes, learning and growth. (Wang
2017)
Research on the Reform of Enterprise Financial Analysis in the Era of Great Wisdom Propelling Clouds
833
Learning and
growth
customer
finance
Internal
business process
Vision
and
strategy
Figure 2: Balanced Scorecard.
3.1.2 Financial Decision Analysis
Traditional financial decision-making analysis is
generally carried out in qualitative and quantitative
aspects by financial staff based on the above various
indicators and parameters, combined with their own
experience accumulated in many years of work.
Today, in the era of Great Wisdom Propelling
Clouds, enterprises can obtain various data from
mobile Internet, Internet of Things and other
channels, including structured data, semi-structured
data, unstructured data, and upload the collected
data to the data center of cloud computing platform
for big data processing, and then analyze data. It
provides feasible reference data for enterprises to
make a series of financial decisions such as
enterprise budget management, financing decision,
investment decision, production decision, pricing
decision and cost decision. (Figure 3)
Data
Sources
data
center
data
processing
Structured
data
Semi-structured
data
Unstructured
data
Big data
processing
Analysis
support
policy support
channel
Mobile Internet, Internet of Things,
Social Network
data
analysis
Financial
decision
Cloud
computing
platform
Text analysis and search, smart
business, etc.
Budget
management
Financing
decision
Investment
decision
Production
decision
Pricing
decision
Cost decision
Figure 3: Flow chart of enterprise financial decision analysis.
3.1.3 Financial Forecast Analysis
Financial forecast is the premise for enterprises to
prepare financial budgets. The traditional financial
forecast mostly combines certain methods and
principles, and uses cost behavior analysis,
cost-volume-profit analysis model and other
methods to predict the future of enterprises,
including sales forecast, cost forecast, profit forecast
and capital demand forecast. In the era of Great
Wisdom Propelling Clouds, enterprises can combine
financial indicators such as liquidity, profitability,
solvency and market value with non-financial
indicators such as ownership structure and the
composition of board of directors and board of
supervisors on the basis of mastering their own
financial status, operating ability, corporate
governance and other original information, so as to
realize the transformation of enterprise financial
analysis from static analysis to dynamic analysis.
(Figure 4)
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834
Early warning analysis of
enterprise financial dynamics
Liquidity status
Current ratio
Quick ratio
Inventory turnover
Accounts Receivable
Turnover Rate
Liquid assets turnover rate
Turnover rate of fixed assets
Profitability status
Roe
Net profit rate
Net assets growth rate
Operating income growth
rate
Operating profit growth rate
After-tax profit growth rate
Ownership structure
Types of actual shareholders of listed companies
The largest shareholder's shareholding ratio
The sum of the shareholding ratios of the top five
major shareholders of the company
The ratio of the company's largest shareholder to
the second largest shareholder
Solvency status
Assets and liabilities
Inventory current
debt ratio
Cash flow debt ratio
Cash debt ratio
Debt-to-capital ratio
Composition of the Board
of Directors and the Board
of Supervisors
Board size
Supervisory board size
Shareholding ratio of the
board of directors
Shareholding ratio of the
board of supervisors
Market value analysis
Earnings per share
Net assets per share
Operating income per share
P/E ratio P/B ratio P/S ratio
Capital adequacy ratio
Financial and non-financial indicators
Financial status
income
cost
Profit etc.
Operating conditions
Production and sales
Procurement,
research and
development, etc.
Corporate Governance
Ownership structure
Board composition
Governance
mechanism, etc.
environment
Macro (politics, law, economy, technology, culture, nature, etc.)
Micro (materials, energy, capital, labor, consumption preferences, etc.)
Original information
Figure 4: Flow chart of financial forecast dynamic analysis.
3.2 Platform Structure System Design
3.2.1 Data Collection Layer
Traditional financial analysis only takes financial
information into the scope of data collection, but
ignores non-financial information. On the basis of
the data in the data collection layer designed by the
structural system of the financial analysis platform,
the collection of non-financial information is added,
and the structured data and unstructured data are
collected together. The original vouchers, books,
sales contracts, shipping orders and other data
information are uploaded through the internal
network of the enterprise. At the same time, the data
information related to the enterprise is collected and
stored in the data warehouse in the external social
network, and the traditional data collection scope is
expanded to make the data collection more
comprehensive. (Figure 5)
database
Intranet
Corporate
external
network
Original
documents
Structured
data
Unstructured
data
storage
Obtain
Original
documents
Figure 5: Flow chart of business data collection.
Research on the Reform of Enterprise Financial Analysis in the Era of Great Wisdom Propelling Clouds
835
3.2.2 Data Storage Processing Layer
After storing the collected data in the data
warehouse, large amounts of data need to be
processed effectively. The data processing
technologies adopted by different data types are also
different. Firstly, for structured data, using Shared
Nothing architecture, the data are divided into high
value density data and low value density data,
combined with the Massively Parallel Processor
(MPP) system for large-scale parallel processing,
which has the characteristics of non-sharing
resources. Secondly, for unstructured data,
HADOOP technology is used to store massive data
with HDFS, and calculates on MapReduce. In this
way, we can combine HADOOP technology (Ma
2019) with the new database to process different
types of data. (Figure 6)
Classification
application
Structured
data
Unstructured
data
New
database
HADOOP
Structured
data
Unstructured
data
High-value
density data
Low-value density
data
Figure 6: Core technology of big data processing
3.2.3 Security Architecture
In the era of Great Wisdom Propelling Clouds, it is
more convenient to obtain information, but also
brings a series of problems in data security. When
constructing the system of enterprise financial
analysis platform, the security architecture of
information should be included, which can be
constructed from six aspects: physical security,
system security, network security, application
security, data security and management security. For
example, in the design of data security, the access to
data can be controlled by means of fingerprint or
facial unlocking, and graphic password input. In
addition, verification code or password input can be
carried out in data transmission to prevent data from
being stolen and lost, which makes the data control
the data security to a certain extent in the links of
storage, access and transmission.
4 THE TRANSFORMATION
PATH OF ENTERPRISE
FINANCIAL ANALYSIS IN THE
ERA OF GREAT WISDOM
PROPELLING CLOUDS
4.1 Ideological Level
In the era of Great Wisdom Propelling Clouds, both
managers and financial workers of enterprises
should change their way of thinking and cultivate
new ideas. Porter has proposed three competitive
strategies, including cost leadership, centralization
and differentiation. In the era of big intelligence and
cloud shifting, big data strategy has become the
fourth means of enterprise competition, and has an
impact on the other three competitive strategies to a
certain extent (Figure 7). Both business managers
and financial staff should be keen to detect market
dynamics, and make timely adjustments in their
respective areas for further strategic decisions.
Competitive
strategy
Centralized
Differentiation
Cost
leadership
Big Data
strategy
Centralized
Differentiation
Cost
leadership
Figure 7: Four new types of competitive strategies.
First of all, for business managers, they are the
helmsman of the enterprise, is the enterprise strategic
decision makers, business managers must first
establish the concept of enterprise big data intelligent
thinking, in the face of the emergence of new things,
timely reflect, quickly adjust the layout, analysis of
existing opportunities and threats, formulate the next
step of the development of the enterprise, take
advantage of the trend to lead the enterprise to a
higher level. If managers are satisfied with the status
qua and stagnate, they will lag behind the
development of society and eventually be eliminated
in the increasingly fierce market competition.
Secondly, for financial analysts, with the advent
of the era of Great Wisdom Propelling Clouds, the
further development of society has brought new
development space for enterprises and their own
development. At this time, we should be keen to
detect the changes in the wind direction of the
market, adjust and change the way of thinking, pay
attention to the use of big data intelligence in data
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
836
processing, and consciously cultivate their ability to
use the Internet for operation. This not only
improves their own business ability, but also
provides further protection for the accuracy of
corporate financial analysis.
4.2 Data Plane
4.2.1 Build a Data Warehouse
Traditional accounting records are in the process of
manually copying the current data to the original
vouchers and other paper documents and registering
books in accordance with the time sequence of the
brokerage business. If you want to view the original
data of a certain data, you must look up and look up
from all the accounting books in the warehouse in
turn. The difficulty and complexity of the process
can be imagined, the workload is large, the time is
long, and the search is difficult. Today, in the era of
Great Wisdom Propelling Clouds, this problem has
been solved optimally. Financial personnel
electronically all paper documents through financial
software such as Kingdee and Yongyou, which not
only makes the records of documents simple and
rapid, but also facilitates the collation, induction and
viewing of original data. Enterprises can also share
the data of electronic documents in real time,
reprocess the data horizontally and vertically, and
maximize the utilization of data according to the
needs of enterprises.
4.2.2 Revolutionizing Data Processing
Technology
Data processing is one of the most important links in
the financial analysis of enterprises. Data processing
provides a new direction of change and development
platform for data processing from the initial
handwritten books to the medium-term accounting
computerization, to the arrival of the era of Great
Wisdom Propelling Clouds. In the past, in the
traditional data processing, the enterprise financial
personnel needed to export the historical data
obtained from the financial software to Excel for
secondary processing. This method takes a long time
and a heavy workload, and the storage space is
limited, and the probability of error is also high.
With the help of big data, intelligent cloud
computing can help financial workers to quickly
process data, greatly accelerating the work
efficiency, and the accuracy and reliability of the
results are improved. It also saves time for financial
workers to carry out the next work and improves
work efficiency.
4.3 Knowledge Level
4.3.1 Train High-Quality Financial Staff
In the era of Great Wisdom Propelling Clouds, the
demand for enterprise financial workers is gradually
increasing, and the ability of financial workers is
gradually improving. Enterprises not only needs a
certain number of financial personnel, but also needs
to have solid professional skills, active thinking
mode, keen market insight, strong big data operation
level and specific analysis and problem solving
ability. At present, most of the enterprises in our
country have a huge gap in financial personnel, for
big data, intelligent financial analytical ability has
not yet been. This requires colleges and universities
to carry out certain reforms in the talent training
mode, set up as many accounting courses as
possible,
cultivate excellent financial analysts, and form an
intelligent financial analysis talented team in
enterprises.
4.3.2 Pay Attention to Dynamic Analysis
The traditional financial analysis of enterprises
mainly focuses on the static analysis of financial
indicators and structural data, while ignoring the
impact of non-financial indicators and non-structural
data on corporate financial management. In the era
of Great Wisdom Propelling Clouds, enterprises can
use big data to obtain non-structural data that cannot
be obtained before. The effective analysis of these
real-time updated data can realize the transformation
to dynamic analysis on the basis of static analysis,
so as to improve and supplement the shortcomings
and deficiencies in traditional financial analysis, lay
a good foundation for the intelligentization of
financial analysis, and realize the organic integration
of financial indicators and non-financial indicators,
structural data and non-structural data.
5 THE EFFECT OF CORPORATE
FINANCIAL ANALYSIS
REFORMS IN THE ERA OF
GREAT WISDOM
PROPELLING CLOUDS
5.1 Make It Possible to Analyze the
Impact of Macro and Micro
Factors
The analysis of enterprises from the perspective of
Research on the Reform of Enterprise Financial Analysis in the Era of Great Wisdom Propelling Clouds
837
corporate strategy includes the macro environment
and the micro environment. The macro environment
includes four factors: political and legal factors,
economic factors, social and cultural factors and
technical factors, also known as the “PEST analysis”
(Wang 2014) model. Porter believes that there are
five kinds of competitiveness in the industry from
the basic structure of the industry, namely: potential
entrants, buyers, substitutes, suppliers and
competitors in the existing industry, which is the
famous ' Porter ' s five forces analysis model. We
know that the traditional financial analysis is
difficult to effectively sort out and process the
factors of the macro environment and the micro
environment. In the era of Great Wisdom Propelling
Clouds, the collected factors are reprocessed through
the mobile Internet big data technology to further
analyze the financial situation of enterprises, so that
managers can more comprehensively understand the
internal and external environment of enterprises, so
as to make important strategic decisions conducive
to the development of enterprises. (Figure 8)
Enterprise
competitor
supplier
Potential
entrant
alternatives
Target
customers
Politics and
law factor
Economic
factors
Social and
cultural
factors
Technical
factors
Macro
environment
Micro-
environment
Figure 8: Macro and micro environments that affect the
development of enterprises.
5.2 Make In-depth Analysis Possible
The traditional financial analysis often occurs after
the event, while ignoring the beforehand and in the
event, which makes the financial analysis more use
of historical data as a reference, greatly reducing the
timeliness of financial analysis, making the analysis
only floating on the surface but not deep-seated
research. Based on big data is conducive to the
enterprise will be in-depth analysis, thorough, will
fully implement the financial analysis of business
management in advance, in and after the event, the
long-term analysis of the stage, the stage analysis of
real-time, and the long-term objectives of the
enterprise segmentation, including business
management activities, budget system, business
forecasting, strategic planning, these decomposition
goals are phased targeted one by one to complete,
add up to the company set the ultimate goal. (Figure
9)
long-term
3-5 years
Per year
Every season
every day
Company
goals
Strategic Planning
Business forecast
Budget system
Management activities
Figure 9: The relationship between corporate plans,
budgets, strategies, and goals.
5.3 Make It Possible to Analyze Future
Development Trends
In today ' s era of Great Wisdom Propelling Clouds,
the level of science and technology is rising rapidly.
The data acquisition, information collection,
processing and method strategy of financial analysis
should be effectively reformed under the big data
technology and keep pace with the time. Taking
consumer analysis as an example, nowadays
Taobao, Jingdong, Vipshop, Pinduoduo and other
shopping software are widely used, so that consumer
information can be easily obtained on the Internet.
According to the browsing records of consumers,
enterprises can join the product characteristics of
shopping carts, collect browsing data such as
consumer preferences, price acceptance, and pay
attention to the substitutability of products. Through
targeted analysis, it is found that the law behind the
characteristics, and can obtain information
beneficial to the development of enterprises, which
provide a reference for the next business strategy of
enterprises.
The traditional financial analysis is based on the
research of enterprise financial personnel using
historical data and the work experience of managers
themselves. The accuracy and reliability of the
results are not high. Nowadays, enterprises can use
the structural system of the financial analysis
platform to understand consumers’ psychology more
comprehensively and achieve their expected goals,
so that enterprises can stand out in the competition
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838
for the same industry and increase the market share
to a certain extent.
6 CONCLUSIONS
With the rapid development of social productivity in
China, the innovation of science and technology has
become the core symbol of the new era. The
emergence of ' big data ', ' intelligent ', ' mobile
Internet ' and ' cloud computing ' has laid a solid
foundation for enterprise development and laid a
good start for social progress. Everything has two
sides, which bring opportunities and challenges to
enterprises. Under this premise, enterprises need to
keep up with the pace of the times. From managers
to financial workers, they should innovate and
change from the aspects of thought, data and
knowledge, make full use of the obvious advantages
brought by the era of big intelligence and cloud
shifting. On the basis of analyzing the development
ability of enterprises, new ideas, new technologies
and new strategies are constantly established to seek
greater development space for enterprises.
ACKNOWLEDGEMENTS
This paper is the research results of the 2019
municipal science and technology strategic
cooperation project “Research on intelligent
financial system based on big data background”
(project number: 19SXHZ0003). The research
results of the 2021 municipal science and
technology strategic cooperation project ' Research
on the expenditure control and early warning of
Nanchong administrative institutions based on
machine learning ' ( project number: SXQHJH017);
research results of the 2021 Municipal Science and
Technology Strategic Cooperation Project'
Application Research on Resource and Environment
Audit Based on Big Data Visualization Technology '
(Project Number: SXQHJH018 ); the research
results of the project funded by the Innovation and
Entrepreneurship Research Fund of Southwest
Petroleum University “Research on the construction
of practical teaching system of financial
management major based on innovation and
entrepreneurship education” (project number:
2021RW025); the research results of the new
engineering research and practice project of the
Ministry of Education’s “exploration and practice of
talent innovation and entrepreneurship training in
industry (oil) universities under the background of
new engineering” (project number:
E-CXCYYR20200943); research results of the
first-class undergraduate course cultivation project
of Southwest Petroleum University in 2020 (course
name: basic accounting); research on the
Construction of Group Financial Management and
Control Curriculum System Based on Financial
Sharing (No. 202101197005), a collaborative
education project between industry and education of
the Ministry of Education in 2021; the research
results of the collaborative education project '
Research on the construction of financial big data
practice teaching center' (project number:
201902162056) of the Ministry of Education in
2019.
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