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Data Asset Value Assessment Literature Review and Prospect
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IWAACE 2020 IOP Publishing
Journal of Physics: Conference Series 1550 (2020) 032133 doi:10.1088/1742-6596/1550/3/032133
Data Asset Value Assessment Literature Review and Prospect
Wang Yanlin 1, Zhao Haijun 2*
1
Master graduate. School of Information, Guangdong University of Finance and
Economics, China
2
Researcher, master tutor. School of Information, Guangdong University of Finance
and Economics, China
*
Corresponding author’s e-mail: zhj88888@126.com
Abstract. With the development of the internet, Cloud Computing and other new generations
of information technology, the society has entered the era of Big Data from the Information
Age, the role of Big Data is becoming more and more powerful in human decision-making
support, and the exchange of big data property rights value has become a new hot spot for
social and economic development. This paper analyzes the connotation, extension and the
basic characteristics based on the value dimension of data assets, combs the research results of
scholars in the value evaluation of data assets at home and abroad, and puts forward the
development direction of the value evaluation of data assets in the future around the natural
value-added, externality and multi-dimensional value characteristics of data assets, in order to
pave the way for future scholars.
1. Introduction
The rapid development of the new generation of information technology has led the digital economy
to stride forward. Through radio frequency identification (RFID) and other information sensing
technologies, the Internet of things realizes the interconnection of everything with data, accelerating
the process of digitization of the world ; Through ultra-wideband, ultra-high speed and ultra-low
latency data transmission, 5G will turn the Internet of everything anytime and anywhere into reality,
supporting the rapid development of the Internet of things; Cloud computing provides powerful
computing power through distributed computing, parallel computing and other technologies, realizing
instantaneous processing of huge amounts of data while reducing IT costs. Artificial intelligence
mines data value through machine learning, deep learning and other technologies, and converts
unordered data into decision information. Data is developing towards the trend of more convenient
data collection, faster data processing and more perfect data application. The huge economic value
contained in data needs to be realized urgently.
On November 1,2019, the fourth Plenary session of the 19th CPC Central Committee put forward
the data as the fourth factor of production after land, labor and capital in official documents, fully
affirming the asset value of the data. How to evaluate the value of data assets has become one of the
hot topics among scholars. This paper will introduce the connotation and extension of data assets in
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IWAACE 2020 IOP Publishing
Journal of Physics: Conference Series 1550 (2020) 032133 doi:10.1088/1742-6596/1550/3/032133
detail, analyze and summarize the present situation of data assets value evaluation, and try to make a
good conclusion for scholars in this field.1
2. Related concepts and characteristics of data assets
2.1. Related concepts of data assets
The data capitalization can realize the direct delivery and transaction of data resources, which is
beneficial to fully release its economic value. But not all data can be converted into data assets. The
cloud computing and big data institute of the china institute of information and communications
defines data assets as "physically or electronically recorded data resources, such as documents,
electronic data, which are owned or controlled by an enterprise and can bring future economic benefits
to the enterprise "[1]. From the perspective of value generated by data, data does not produce benefits
directly. Only by developing the information contained in data can value be generated. The raw data
could release valuable information to the enterprise after the steps of data collation, processing,
cleaning, analysis and visualization. In addition, the clear ownership of property rights is the premise
of the circulation of data assets. Only the clear ownership and use right of data can reasonably
distribute the income generated by data. Therefore, data assets is data whose property ownership is
clear go through the data capitalization process including data processing, data cleaning, data mining.
The identification process of data assets is shown in figure 1 below:
Remove data whose
ownership is unclear, Remove missing,
unreadable and redundant data
unquantifiable
Input output
Basic data Data processing Data cleaning Data mining Data assets
Identify economic
benefits
Figure 1 Identification process of data assets
In the process of the development of digital economy, similar concepts such as network
information products, information assets, digital assets and data assets have emerged in turn. Some
scholars believe that information assets, digital assets, data assets are the same products of data value
in different stages of development, the essence of which is information, which should be merged into
data assets[2]. Some scholars believe that these concepts are similar but different. Han Haiting divides
the data economy into six stages, such as business informatization, data resource utilization, data
productization, data capitalization, asset digitalization and asset monetization, and is distinguished
between transaction value, trading place (medium), customer characteristics, deliverables and service
patterns[3]. The author believes that there are essential differences between data assets and network
information products, information assets and digital assets, the specific differences are as follows:
Table 1. Related Categories of Data Assets
Concept Definition Concept connotation or distinction
Web Everything that can be digitally or The network information product exchange happens
Information digitally simulated and that can be between the upstream and downstream enterprise, the
1
This paper is one of the achievements of the project of "Research On Data Assets Right Confirmation Under
Big Data Environment"(project number:16BTQ075) which approved by National Social Science Fund of China
in 2016.
2
IWAACE 2020 IOP Publishing
Journal of Physics: Conference Series 1550 (2020) 032133 doi:10.1088/1742-6596/1550/3/032133
Products spread and traded on the Internet product does not involve all enterprises. Does not
depends entirely on the web for have the exchange matchmaking transaction.
production and consumption [4].
Information Data that have or should be It is preliminarily determined that the information has
assets recorded of value or potential value, but it does not provide a solution to how the
value [5]. value of the information is realized.
Digital assets Ownership of binary formal data, The core of digital assets is digitization, and
generated and stored in devices digitization is analog data, converted to binaries
such as computers, smartphones, represented by 0 and 1, so that the computer can
digital media, or the cloud [6]. process the data. The core of data assets is data,
which is the process of transforming a phenomenon
into a quantifiable form of tabulation analysis [7].
Data assets Have data rights (exploration The requirement of data capitalization is to ensure the
rights, right of use, ownership), sovereignty of data by creating a series of rules, rights
data sets in valuable, measurable, rationing, transaction mechanism and pricing
readable cyberspace [2]. mechanism, the security of data is protected, the
privacy of data subject is fully protected, the data
assets can flow freely and the value of data is
mined[3].
2.2. Characteristics of data assets
As a new factor of production, data is traded in the market, which makes the data assets have asset
attributes. From the composition of data assets, Only through data analysis and mining of massive,
valuable and diversified data can information with economic benefits be produced. The above-
mentioned characteristics are the basic characteristics of big data. Therefore, the raw data before the
data assets are capitalized is a subset of big data. In addition, The value of the transaction of data
assets lies in the transfer of intelligence information, so the essence of data assets is an information
commodity. In summary, data assets have the characteristics of information commodity, assets and big
data. Among them, the characteristics of quasi-public goods of information commodities and the basic
characteristics of big data bring externality and natural increment of data assets. Details are shown in
table 2:
Table2 Characteristics of data assets
Characteristics Concept connotation Impact on value assessment
Asset Resources formed by past transactions or Data can be traded as assets in the market
characteristics events, owned or controlled by the enterprise,
that can bring potential benefits to the
enterprise in the future.
Basic "4V Characteristic",that is, volume, varity, Information value of data assets
characteristics of Velocity, and value
big data
Characteristics of Limited non-exclusive and non-competitive The cost of reproduction is extremely low,
quasi-public goods easily causes the property right ownership
unclear, causes the data asset value
increment part to be difficult to determine.
Externality The use of data assets can bring the effect of The more consumers consuming data
positive market feedback, and consumers assets, the greater the value of data assets.
consume data assets, not only to bring their
own utility, but also to others.
Natural value Raw data and derivative data are generated in The value of data assets changes
added large quantities every moment, which can dynamically every moment.
creating new value.
The information contained in the index assets The information contained in the data
Multidimensional can have the objective attribute of satisfying assets has different value quantities for
human existence and development in many different people.
aspects.
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IWAACE 2020 IOP Publishing
Journal of Physics: Conference Series 1550 (2020) 032133 doi:10.1088/1742-6596/1550/3/032133
3. Current status of the data asset valuation study
The author divides the current data asset value evaluation into four types, namely, the traditional
intangible asset evaluation method faction, the intangible asset evaluation method improvement
faction, the quantitative impact factor evaluation faction and the algorithm evaluation faction. Among
them, the traditional intangible assets appraisal scholars refer to the use of one or more traditional
intangible assets valuation methods to evaluate the value of data assets, such as market method, cost
method and income method; The improvement of intangible assets evaluation method refers to the
adaptive improvement based on the traditional intangible assets evaluation method by adding various
adjustment coefficients according to the characteristics of data assets. Quantitative impact factor
evaluation method refers to finding out the value influencing factors of data assets at first, then
determining the weight of each value influencing factor according to the analytic hierarchy process,
and then determining the value evaluation model of data assets. Algorithm evaluation refers to the use
of classification, clustering, regression and other machine learning methods to calculate the value of
data assets.
3.1. Traditional intangible asset evaluation faction
Liu Lianghui (2002) thinks that data assets are different from intangible assets. The cost composition
and profit size of the data assets are uncertain because of the virtual product production process, and
the way of bringing excess income to the enterprise is different from the intangible assets. Therefore,
we should adopt the historical cost method in the development stage, and use the market method to
measure the excess benefits of the data assets in the application stage [8]. Tong Yonggang (2008)
believes that the measurement of data assets should be continuous, and the additional expenditure on
the new value of data assets should be included in the book value of data assets [9]. Zhang Yongmei
(2015) thinks that financial data assets are a kind of self-made intangible assets that can be profited but
not easily quantified, so the replacement cost method is used to evaluate the value of financial data
assets [10]. Li Ru (2017) believes that the cost of data assets should be recognized in different stages by
means of external purchase, active acquisition, passive acquisition, etc [11]. Xu Yi (2017) recognized
that the cost of data assets is a dynamic index, and the cost of data assets mainly comes from the
construction cost and operation and maintenance cost of data collection, storage and processing system
[12]
. Li Yonghong (2017) refined the traditional intangible asset value evaluation method according to
the characteristics of the Internet, and added the operating cost, comparable asset market value, future
income and other influencing factors to the Internet enterprise data asset value evaluation [13].
It can be seen from the research of the above scholars that the scholars of this faction measured the
value of data assets by using the traditional intangible assets valuation methods such as cost method,
income method and market method alone or in combination. Some scholars directly to data assets shall
be regarded as intangible assets, and some scholars can realize data assets differented from intangible
assets because of the value-added features of data assets, but there is no explanation for how to
estimate the value-added content of the data assets.
3.2. Intangible asset evaluation method improvement faction
Liu Qi (2016) proposed modifying the market method to evaluate the value of data assets. On the basis
of the value of the similar big data assets, quantitative adjustment of different factors such as
technology level, value density and data capacity [14]. Zhu Dan (2017) realized that data assets have
spillover value. Based on the traditional cost method of intangible assets, he comprehensively
considered the cost and expected use premium of data assets, and modified the asset value by adding
influencing factors on the value of government data assets, so as to put forward the value evaluation
model of government data assets [15]. Le Huang (2018) adds production cost, operating cost, data
realization factor, premium rate coefficient, number of users of platform, network node distance, user
activity coefficient, market adjustment coefficient and other value adjustment coefficients on the basis
of comprehensive consideration of data assets cost and income, and pioneering the introduction of
platform activity coefficient and other parameters into platform-type data asset value evaluation model
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IWAACE 2020 IOP Publishing
Journal of Physics: Conference Series 1550 (2020) 032133 doi:10.1088/1742-6596/1550/3/032133
[16]
. Chen Tingting (2013) modified the income method to introduce the spillover value generated by
the brand into the value evaluation of the trademark [17].
It can be seen that above scholars approve that the data assets are obviously different from the
intangible assets, and realize the necessity of evaluating the value increment of the data assets, and the
various value adjustment coefficients will be added when the traditional intangible assets evaluation
method is used to evaluate the data assets.
3.3. Quantitative impact factor evaluation faction
The quantitative influence factor assessment group puts forward the value influence factors of the data
assets from the different classification angles of data assets cost, application, maintenance and so on,
and carries on the weight analysis to the various value influence factors according to the analytic
hierarchy process, and then establishes the data asset value evaluation model. As shown in table 3:
Table 3 Research status of quantitative impact factor assessment scholars
The author Evaluation Methods Classification of Angle Influence factors
object
Zhang Data assets Analytic hierarchy Data asset cost, data asset Construction cost, operation and
Zhigang process(AHP) application maintenance cost, asset
（2015） classification, frequency of
[18] utilization, objects, use effect
evaluation
Hak J. Kim Data assets - Mapping to business value Corresponding granularity of data
（2015） based on the volume characteristics; Data
[19] characteristics of big data timeliness characteristic
correspondence analysis speed;
Data diversity corresponds to data
type; Data variability corresponds to
analysis reliability.
Shi Aixin Internet AHP and principal Data collection, Channel source, time span and
（2017） enterprise data component processing (construction geographical scope, business
[20] assets analysis(PCA) and application) and operation and technical operation
maintenance costs, labor costs, material costs and
overhead costs, R&D costs and
promotion costs
Li Data assets The combination of Data quantity, data Enterprise size, data coverage, data
Yonghong AHP, grey relation quality, data analysis integrity, data externality, data
（2018） analysis and market ability timeliness, data relevance and
[21] comparison information systems, talent skills,
consumer needs
Tao Yiran Platform data AHP Platform Value Granularity, Diversity, Activity,
（2019） assets Influencing Factors Scale and Relevance of Platform
[22] Data
Wang Jing Internet AHP and practical Data control layer, user Internal and external users, data
（2019） Financial option research access layer, data value-added products, data areas,
[23] Enterprise application layer, data data exchange components and
Data Assets computing layer, data platforms, data control platforms,
exchange layer, data data standards, data quality, light
generation layer data, data security, data types, data
sources
Guo Yiban Internet AHP and practical Platform operating data, Platform operating data assets,
(2019)[24] Financial option research investor data assets and investor data assets, borrower data
Enterprise borrower data assets assets, executive data asset value,
Data Assets data asset volatility, data asset life
cycle, data asset risk-free rate
In addition, Gartner, the world's most authoritative information research and consultancy company,
jointly released the world's first data asset evaluation model in 2016, which is based on the quantity,
scope, quality, granularity, relevance, limitation, source, scarcity, industry nature, equity, nature of
transactions, expected benefits and so on.
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IWAACE 2020 IOP Publishing
Journal of Physics: Conference Series 1550 (2020) 032133 doi:10.1088/1742-6596/1550/3/032133
3.4. Algorithm evaluation faction
Wang Jianbo (2006) believes that the artificial intelligence method can be used to evaluate the value of
data assets. Artificial neural network has high self-organizing, adaptive and self-learning ability,
which can make objective evaluation and prediction of the application value of data itself. Not only
can it overcomes the influence of artificial factors and fuzzy randomness brought by artificial
evaluation, but also ensures the objectivity and accuracy of evaluation results. Besides, it has strong
dynamic, which can provide important basis for the determination of data price [25]. Zhang Chi (2018)
proposed using deep learning method based on data characteristics to establish a data asset value
evaluation model based on five dimensions: data granularity, activity, correlation, multi-dimensional
and scale [26].
From the research of the above scholars, it can be seen that this scholar fully realizes it’s data
characteristics such as data dynamic, data scale, data quality that play a important role in data assets
valuation and tries to use machine learning to evaluate the value of data assets.
Through the above literature research, we can get the following conclusion: Firstly, Data assets are
initially regarded as intangible assets, and the value is evaluated according to the traditional intangible
assets evaluation method. Secondly, since the cost method cannot accurately measure the value-added
effect after the data capitalization, the operability of the income method and the market method are not
applicable, scholars began to modify the traditional intangible assets evaluation method by adding
various correction factors data assets different from intangible assets. Thirdly, as the boundaries
between data assets and intangible assets are becoming clearer, data assets are beginning to be
evaluated as a separate category. At present, the evaluation methods are mainly analytic hierarchy
process, fuzzy comprehensive evaluation method, artificial intelligence method and so on. The
characteristics of this kind of method are classified according to the influencing factors of data asset
value and weight analysis and then valuation modelling.
4. The difficulty of data asset valuation
4.1. Difficulty of redistributing the value of property rights
It is difficult to redistribute the value of property rights due to the continuous transfer of data
ownership along with the increment of data circulation. In the process of Data assets circulation, data
property rights of personal data rights and data property rights will also be transferred. Specifically,
data flow can realize value mining of scenario application in this circulation. While data decision,
confidentiality, query, correction, blockade, deletion and compensation request are transferred among
different owners along with the flow of data assets. Different property right owners can record,
retrieve, sort out, label, compare, analyse and mine the data through these data rights to realize data
increment. To determine the value of the increment of data assets, it is necessary to consider the
change of property value, that is, the contribution of each transferor to the increment.
Shi Xianwang pointed out that the value of property rights is the redistribution of rights,
responsibilities and benefits [27]. To evaluate the value of data assets, first of all, it is necessary to
clarify the rights of property owners to jointly own, use and dispose of the data assets and the right to
obtain the income generated therefrom .The responsibility is that the subject of property rights should
assume (such as the true validity of data, etc.), which means the right to the data assets must be
confirmed. However, the liquidity of data makes the right of data confirmation very complicated. At
present, China has not formed a reasonable and mature right mechanism of data assets.
4.2. Difficulty of evaluating the value increment of data assets
Changes in data rights structure make it difficult to evaluate the value increment of data assets.
Ownership of data assets determines the distribution of data value benefits and the division of data
quality and security responsibilities [28]. Data property right, like commodity, has two attributes of
value and use value, so it has two forms of rights, the utilization and proprietary rights, which would
be seperated during the process of data circulation. According to "who produces, who enjoys; who
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Journal of Physics: Conference Series 1550 (2020) 032133 doi:10.1088/1742-6596/1550/3/032133
records, who owns" principle, data assets is owned by the data provider during the data generation
phase. While in the data increment phase, the increment ownership belongs to the data processor.
Different ownership of property rights will produce different range of data to be used, and the change
of data right structure reflected in the circulation is difficult to grasp, which makes the value
appreciation and value evaluation of data assets extremely complicated.
5. The prospects of data assets valuation
5.1. Dynamic valuation
The natural appreciation of data assets requires that the dynamic value of data assets be evaluated.On
the one hand, the volume of data is growing exponentially every second ,making the value of data
changing dynamicly. In addition to the original data generated, derived data is generated in the
application process of data exchange and sharing during the process of data circulation. In the process
of scenario application, data assets can still obtain new data assets at different levels, and the
accumulation of such assets is theoretically infinite [3]. However, since the derived data is generated in
the iteration of data sharing, its attribution problem is more complicated than the original data, and it
needs to be differentiated according to the actual situation. The huge amount of original data and
derived data appearing every second will increase the value of data assets, making the value of data
assets increase with time. At this point, a single static value evaluation model is no longer applicable.
The specific data asset valuation framework should look like figure 2:
Data Asset production cost
Data asset
valuation static evaluation
framework
Data asset overflow value
Dynamic evaluation
Figure 2 Data asset valuation framework
5.2. Focus on spillover value
The externality of data assets requires that the spillover value generated by the data assets in
circulation be assessed. Data assets have the externality of network information products and can
produce the effect of positive market feedback. Unlike ordinary commodities, the externalities of
network information products must be positive because consumers bring utility not only to themselves
but also to others through consuming data assets. The more consumers consume the data assets, the
more valuable the data assets become. This can be explained by Metcalfe 's law, which says that as the
number of users increases, the value of the network increases at the rate of the square of the number of
users. Therefore, the value composition of data assets should include not only the capitalization
expenditure of data, but also the overflow value generated in the circulation process of data assets.
5.3. Applicable to different scenarios
The multidimensionality of data assets requires that the value of data assets be determined according
to different application scenarios. The multidimensional objective attribute of data assets and the
different needs of people will produce different values. In other word, the multidimensional nature of
data assets will result in the same information acting on different objects to produce different values.
The core value of data assets is the information contained in them. Since the information receiving
ability of the object and the conditions for using the information are different, the effect of data assets
containing the same information content and amount of information is different. This is not only
because of the difference in the ability of different objects to receive and understand information, but
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IWAACE 2020 IOP Publishing
Journal of Physics: Conference Series 1550 (2020) 032133 doi:10.1088/1742-6596/1550/3/032133
also because of the different purposes of the objects to use the data assets will lead to the difference in
the utility of the data assets. Therefore, according to the different purposes of data assets, data assets
should be divided into application scenarios for value evaluation.
6. Conclusion
With the digital economy developing rapidly, the huge economic value contained in data has realized
gradually, and it can be seen that a large number of data assets are traded and circulated in the market.
Therefore, how to evaluate the value of data assets is widely concerned by scholars. Scholars'
valuation of data assets has just started in recent years. The author summarizes the current valuation
methods of data assets as traditional intangible assets valuation method, traditional improved
intangible assets valuation method, quantitative impact factor method and algorithm method. Data
assets have experienced the progress from being identified as intangible assets at the beginning to
being listed as a new category of data assets. The value increment characteristics and big data
characteristics of data assets have also been converted into value impact factors, adding into the value
evaluation of data assets. In order to value the data assets more accurately, it is the essential attributes
of data assets that should be taken into consideration into the valuation which contains the natural
increment, externality and multi-dimensionality characteristics, pointing the way to evaluate the
dynamic value of data assets, focus on the spillover value generated by data assets in the circulation
process, and make value evaluation according to different application scenarios.
References
[1] Institute of cloud computing and big data, China academy of information and communications
technology. (2018) White paper on data asset management practices (version 2.0). China
academy of information and communications technology. Beijing.
[2] Zhu Yangyong, Ye Yazhen.(2008) Data assets from the perspective of data attributes .J. Big
data,4(06):65-76.
[3] Han Haiting, Yuan Linlin, Li Xiangrui. (2019) Research on data assets in digital economy.J.
Credit investigation,37(04):72-78.
[4] Geng Miao. (2010) Research on pricing strategy of network information products.D. Dongbei
university of finance and economics.
[5] IMPACT k. (1994) Information as an asset: The board agenda. KPMG/IMPACT Group,
London.
[6] TOYGAR A. (2013) A new asset type: digital assets. J. Journal of International Technology &
Information Management ,22(4):113-119.
[7] Zhang Yi. (2017) Data is king, subversive marketing. People's posts and telecommunications
Publishing. Beijing.
[8] Liu Lianghui. (2002) Accounting recognition, measurement and accounting of digital assets in
e-commerce environment. J. Finance and accounting research, 10:23-24.
[9] Tong Yonggang. (2008) On the accounting of digital assets under the environment of digital
economy.J. Friends of accounting (mid-ten), 07:40-41.
[10] Zhang Yongmei, Mu Wenjuan. (2015) Analysis on the characteristics and value of financial
data assets in the era of big data. J. Finance and accounting research, 08:78-80.
[11] Ru Li. (2017) Research on recognition and measurement of big data assets. D. Xi 'an
university of technology.
[12] Xu Yi. (2017) Property attribute and value evaluation of big data. J. Industry and technology
forum,16(02):97-99.
[13] Li Yonghong, Li Jinhao. (2017) Research on the valuation method of data assets of Internet
enterprises .J. Economic research guide, 14:104-107.
[14] Liu Qi, Tong Yang, Wei Yongchang. (2016) Application of market method to evaluate big
data assets. J. China asset appraisal, 11:33-37.
8
IWAACE 2020 IOP Publishing
Journal of Physics: Conference Series 1550 (2020) 032133 doi:10.1088/1742-6596/1550/3/032133
[15] Zhu Dan. (2017) Research on valuation and value realization of government data assets. D.
South China university of technology.
[16] Huang Le, Liu Jiajin, Huang Zhigang. (2008) Research on the value of platform data assets
in the era of big data. J. Journal of fuzhou university (philosophy and social sciences
edition) ,32(04):50-54.
[17] Chen Tingting. (2013) Brand evaluation method innovation. D. China university of political
science and law.
[18] Zhang Zhigang, Yang Dongshu, Wu Hongxia. (2015) Research and application of data asset
valuation model. J. Modern electronic technology,38(20):44-47.
[19] Kim H j. (2015) Big Data: The Structure & Value of Big Data Analytics. J. Structure &
Value of Big Data Analytics,02:1-5.
[20] Shi Aixin, Gao Ding, Xie Jing. (2017) Construction of data asset valuation system for
Internet enterprises. J. Times finance, 14:109-112.
[21] Li Yonghong, Zhang Shuwen. (2018) Construction of data asset valuation model. J. Journal
of finance and accounting, 09:30-35.
[22] Tao Yiran. (2019) Evaluation of platform data asset value based on AHP method. D. China
university of mining and technology.
[23] Wang Jing, Wang Juan. (2019) Valuation of data assets of Internet financial enterprises --
research based on b-s theoretical model. J. Research on technology economy and
management, 07:73-78.
[24] Guo Yitong. (2019) Research on valuation method of data assets of Internet financial
enterprises .D. Xi 'an university of science and technology.
[25] Wang Jianbo. (2016) Research on data asset value evaluation method. J. Times finance,
12:292-293.
[26] Zhang Chi. (2018) Research on data asset value analysis model and trading system. D.
Beijing jiaotong university.
[27] Shi Xianwang. (2006) Property right value movement: analysis from the perspective of
accounting objects .J. Accounting research, 06:35-40.
[28] Ding Daoqin. (2017) Binary division of basic data and value-added data. J. Financial and
economic law, 02:5-10.
