Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 20, No. 3, 2025 

 

145 

Data Asset Value Evaluation Model of H Pharmaceutical 
Enterprise 

Danyang Xu, Xiaolian Meng, Yongqi Yi *, Yongning Zhao 

School of Economics & Management, Nanjing University of Science & Technology, Nanjing, Jiangsu, 210094, China 

* Corresponding author: Yongqi Yi 

 

Abstract: This paper discusses how to construct the evaluation index system of data assets of listed companies (H 
Pharmaceutical Enterprises), and on this basis, constructs an evaluation model to evaluate the value of data assets by using the 

relevant data of the listed company, verify its scientific rationality and feasibility, and provide relevant theories and methods for 

the data assets of enterprises in the pharmaceutical industry. Firstly, based on the discussion of the sources and characteristics of 

H Pharmaceutical Enterprises' data assets, the analytic hierarchy process is used to construct the evaluation index system of its 

data asset value. Secondly, the replacement cost method and the multi-period excess income method are introduced; the value of 

data assets is calculated from two aspects: cost and income. Thirdly, the final value of data assets is adjusted by the fuzzy 

comprehensive evaluation method. This study offers a systematic and operational data asset value evaluation index system and 

evaluation methods for managing related enterprises. It provides a reference for enterprise management and offers theoretical 

support and practical guidance for evaluating data asset value in related industries. 

Keywords: Data asset valuation; Pharmaceutical enterprises; Analytic hierarchy process; Replacement cost method; Multi-
period excess return method. 

 

1. Introduction 

With the rise of big data technology, the economic value of 

data has been gradually mined, and data has become the fifth 

major factor of production after land, labor, capital, and 

technology [1]. In December 2022, China’s CPC Central 

Committee and the State Council first proposed the “Data 

assets” concept at the national level. Later, policies like the 

“Data Elements X” Three-Year Action Plan (2024-2026) and 
the “Guidance on Strengthening Data Asset Management” 

were released, explicitly calling for accelerating the 

development of the data element market and promoting the 

compliant circulation of data assets. 

This study focuses on the pharmaceutical industry for two 

key reasons. First, the industry’s data is standardized and 

traceable in real time. Stringent regulatory policies have laid 

a solid foundation for standardized data management in 

pharmaceutical enterprises, reflected in their generally high 

Data Management Maturity Model (DCMM) ratings. 

Additionally, the fast-moving nature of pharmaceuticals 

makes enterprise data value easy to track and quantify. 
Second, as pharmaceutical enterprises increase investment 

in data to meet the growing demand for proper data asset 

accounting, accurate value assessment becomes essential. 

Global technology market consultancy ABI Research predicts 

that the industry’s data analytics spending will grow at a 27% 

annual compound rate, reaching $1.2 billion by 2030. This 

highlights the industry’s focus on digital transformation and 

signals the continuous generation of high-value data assets. 

However, existing research mainly covers industries with 

active data transactions (e.g., internet, e-commerce), while the 

pharmaceutical industry lacks mature data asset valuation 
methods [2]. This gap makes it hard to quantify data asset 

value accurately, hindering their recognition in financial 

statements. Thus, building a scientific data asset value 

evaluation index system and model, and fairly reflecting 

values in financial statements, is an urgent task. 

To ensure rational and scientific evaluation results for 

pharmaceutical enterprises’ data assets, this study constructs 

an evaluation system from two dimensions: evaluation 

method selection and index weight allocation. Given strict 

regulations making market-based pricing of pharmaceutical 

data difficult, the replacement cost method works as data 

collection/processing costs are quantifiable [3-4]. The multi-

period excess income method, combined with discount 
models, helps quantify future value by linking to free cash 

flows. 

2. Related Research Progress 

2.1. Definition of Data Assets 

Scholars have defined data assets from multiple 

perspectives in recent years: Peng et al. (2024, resource 

perspective) view them as capitalized data resources meeting 

accounting asset standards [5]; Li Chunqiu et al. (2020, asset 

attributes) classify them as intangible assets [6], while Liu 

Jiang et al. (2023) call them non-monetary assets; Yuan Xiu'e 

et al. (2024, accounting) define them as enterprise-

owned/controlled, measurable data bringing future economic 
inflows [7-8]; Tan Mingjun (2021, informatics) refers to 

legally collected, value-creating information [9]. 

In this paper, data assets are defined as "Data resources that 

are legally owned or controlled by an enterprise, can be 

reliably measured, and can bring economic benefits or 

potential economic benefits to the enterprise". 

2.2. Characteristics of Data Assets 

On the characteristics of data assets, domestic and foreign 

scholars mainly study the characteristics of data assets from 

the perspective of the attributes of data assets, the expression 

form and use characteristics of data, and the embodiment of 

value. Pa Jingping et al. (2024) discuss the characteristics of 
data assets from the perspective of data asset ownership, and 

argue that public data assets are shareable [10]. From the 



 

146 

perspective of the attributes of data assets, Li et al. (2024) 

proposed that data assets have five characteristics of big data, 

namely, Volume, Velocity, Veracity, Variety and Value. Ma et 

al. (2024) argue that data assets have the characteristics of 

intangible assets, such as non-physical form, non-monetary 

and identifiability, from the perspective of the existence form 

and Operation Mechanism of data assets [11]. Yang Xiaoqing 

(2024) found the dependence of data value on scenarios [12]. 
In addition, some scholars have analyzed the confirmation, 

traceability and valuation of data assets: Li Qingyi et al. (2022) 

proposed that the value of data assets is uncertain, and Zhang 

(2024) emphasized the exclusivity of data assets. 

3. Construction of Data Asset Value 
Evaluation Index System and Model 
of H Pharmaceutical Company 

3.1. Company Profile and Its Data Assets 

To ensure the typicality of the research object and the 

feasibility of data acquisition, H Company is selected as the 
analysis sample. H Company is a pharmaceutical R&D, 

production, and sales enterprise, operating on a moderate 

scale. Its data management maturity assessment (DCMM) has 

reached the second level, placing it at a medium maturity 

stage within the pharmaceutical industry, and it has certain 

representativeness. The company attaches great importance to 

the driving effect of data elements on enterprise value, 

continues to invest resources in the construction of a data 

governance system, and cooperates with professional data 

governance institutions to promote the systematic 

management of enterprise data assets. 

In view of the confidentiality and compliance requirements 
of the data, H Company has authorized this study to use the 

relevant data within the scope of the provisions. Code names 

represent the company name and platform information. 

3.2. The Classification and Authorization 
Management of Data Assets of H 

Pharmaceutical Enterprises 

To ensure clear scope, reliable sources, and evaluation 

accuracy, this paper first clarifies H's data asset 

classification/confirmation (considering asset characteristics 

for weight assignment) and guarantees legal compliance. 

H classifies confirmable data assets (by scenario and value) 

into internal operational, market, and R&D types. It entrusts 

Z Company to govern data via Z's D platform (quality checks, 

compliance reviews), which screens source data (traceability, 
stability) into structured sets managed in three modules. 

 

Table 1. Classification of data assets of pharmaceutical enterprises 

Classification of data assets Data Interpretation 

Internal operational 
data assets 

Production data 
Production line data Equipment running status, time series data, etc. 

Data of product quality Quality inspection data, GMP, etc. 

Supply chain data 
Raw material data Raw material purchase data, raw material loss data, etc. 

Data warehousing Raw Material Inventory, product inventory, etc. 

Market data assets 

Market research 
data 

Customer data Customer demand, customer loyalty and so on 

Industry data Market size, growth trends, etc. 

Marketing data Ad conversion rate 

Sales data 
Sales figures Sales data of products in hospitals and pharmacies 

Pricing data Pricing impact, promotional impact 

Research and 
development data 

assets 

Research data 

Compound Library data Structures and properties of compounds 

Experimental data Experimental data of pharmacology, toxicology, etc. 

Clinical data Feedback data from clinical trials 

Formula data Formula data Patents on pharmaceutical formulations and processes 

Table 1 shows the results of the classification of H Pharma's 

data assets, covering the core areas of business operations, 

marketing and R & D, providing a reliable basis for 

subsequent valuation. 

3.3. The Construction of the Evaluation 
System 

3.3.1. Construction Ideas of Data Asset Value Evaluation 
System 

In this paper, a data asset value evaluation system based on 

the replacement cost method and the multi-period excess 

income method is constructed. 

The final value of data assets is 

𝑉𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑑𝑎𝑡𝑎 𝑎𝑠𝑠𝑒𝑡𝑠 = 𝜇 ∗ 𝑄 ∗ 𝑉Preliminary valuation results  

(1) 

3.3.2. Overview of the Methodology for the Valuation of 
Data Assets 

To scientifically and systematically evaluate the value of 

data assets, it is necessary to clarify the dimensions of 

economic value and the corresponding evaluation methods to 

ensure that the value of both costs and benefits is reasonably 
quantified. 

In this paper, the economic value of data assets is divided 

into cost dimension and income dimension, and the 

replacement cost method and multi-period excess return 

method are used to evaluate the economic value of data assets 

in each dimension. AHP calculates the weight of the two 

dimensions. Finally, the value of data assets is calculated by 

summation. Fig. 1 shows the evaluation system of economic 

value of data assets. 

 



 

147 

Cost Indicators

Revenue Indicators

Acquisition Cost

Construction Cost

Operation and 

Maintenance Cost

Expected Revenue 

Amount

Residual Revenue 

Amount

Discount Rate

Economic Value of 

Data Assets

 
Figure 1. Evaluation system of economic value of data assets 

3.3.3. Data Quality Indicators Selection and System 
Construction 

The analytic hierarchy process is used to calculate the 

weight of data quality, which is used to adjust the value of 

data assets under different quality conditions. 

Data quality evaluation index and its selection basis include 

the following aspects. Firstly, this paper systematically 

examines authoritative standards, including the International 
Monetary Fund Data Quality Assessment Framework, the 

National Statistical Quality Assurance Framework, the 

statistical quality assurance framework of Eurostat, and the 

national standard of the People's Republic of China. 

Based on the analysis of keywords and research topics in 

domestic and foreign literature on data quality, this paper 

integrates literature on data quality dimensions to 

preliminarily establish a data quality evaluation index pool. 

3.3.4. The Result of Weight Distribution of Data Asset 
Evaluation Index of H Company 

In the research process, a total of 10 experts from the 

pharmaceutical industry, manufacturing industry, and data 

governance industry were invited to score the weights of 

indicators at all levels of data asset value. 

To improve the adaptability and operability of the model in 

the pharmaceutical industry, the weights of the cost and 

income methods are scored. Combined with the previous 
analysis, the cost method measures the difficulty of resource 

input and replacement, while the income method focuses on 

future economic returns. The weighting process can balance 

the deviation caused by a single method, integrate the 

advantages of the two methods, and combine the expert's 

judgment on the applicability of the method. The weighting 

results are shown in Tables 2. 

 

Table 2. Weight of cost method and income method 

Methods Weight 

Replacement cost method 0.41 

Multi-period excess return method 0.59 

 
To reflect the relative importance of each data quality index 

in the overall value assessment, the weight score is calculated. 

3.4. Quantification of Data Asset Valuation 

Metrics 

3.4.1. Formulation of Quantitative Standards for Data 
Asset Value Assessment Indicators 

It is the premise of applying the evaluation index of data 

asset value to practice to clarify the measurement method and 

scoring criteria of each index. On the basis of determining the 

weight of each index, the quantitative standards are 

formulated around the specific indicators in the evaluation 

model, so as to ensure that the model can be applied in H 

pharmaceutical enterprises, and then extended to the entire 

pharmaceutical industry. 

The evaluation results under the cost dimension can be 

calculated according to the following formula: 

𝑉1 = 𝑅𝐶 ∙ (1 + 𝑟)                (2) 

𝑅𝐶 = (𝐶1 + 𝐶2 + 𝐶3) ∙ 𝛽            (3) 

In the formula, C1 represents the acquisition cost, covering 

the direct costs incurred in data acquisition, cleaning and 

compliance. C2 represents the construction cost, including the 

input of data infrastructure construction and processing flow 

development. C3 represents the operation and maintenance 

cost, involving continuous update, maintenance, etc.  

represents the daily price index of asset evaluation,  

represents the replacement cost of data assets, and  

represents the profit margin of data assets. 

The valuation results  under the revenue dimension can 

be calculated according to the following formula: 

𝑉2 = ∑
𝐸𝐶𝐹−𝐸𝑐−𝐸𝑓−𝐸𝑖

(1+𝑖)𝑡
𝑛
𝑖=1            (4) 

𝑊𝐴𝐶𝐶 = 𝑅𝑒 ×
𝐸

𝐷+𝐸
+ 𝑅𝑑 ×

𝐷

𝐷+𝐸
× (1 − 𝑇)      (5) 

𝑅𝑒 = 𝑅𝑓 + 𝛽 ∙ (𝑅𝑚 − 𝑅𝑓)          (6) 

The resulting economic value of the enterprise's data assets 

is as follows: 

 



 

148 

𝑉𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑣𝑎𝑙𝑢𝑒 = 𝑉1 ∙ 𝜔𝑉1 + 𝑉2 ∙ 𝜔𝑉2         (7) 

Where and represents the weight determined by AHP for 

each dimension, respectively. 

3.4.2. Replacement Cost Results 
In this paper, the replacement cost of data assets is 

calculated by the product of the historical cost of data assets 

and the replacement coefficient of data assets. Without data 

asset market reference prices, the replacement cost method 

(replacement cost = data assets' historical cost × replacement 

coefficient) is practical and reflects reconstruction costs well. 

First, H Company's 2020-2024 historical cost data (from 

procurement, infrastructure, cloud services, etc.) is 

591,368,736.52 yuan. 
Second, the replacement coefficient has two types: price 

(using 5-year CPI for non-labor costs) and manpower (using 

5-year average pharmaceutical wage growth for labor costs). 

Based on the replacement cost method, the replacement 

cost is estimated at $611,333,285.80. 

In view of the current policy that data assets are generally 

included in the category of intangible assets, the return on 

assets is selected as the reasonable profit rate of data assets, 

which is 14.01% according to the overall return on assets of 

the company. 

After formula (1) calculation: 

= 696,981,079.14                

3.4.3. Excess Return Method 
In the case that data assets continue to create economic 

benefits, their value is reflected in the future excess return. 

Therefore, by extracting and discounting the excess returns 

attributed to data assets, the actual contribution of data assets 

to corporate earnings can be measured more directly and 

comprehensively. 

Considering the external environmental factors in recent 

years lead to fluctuations in operating income, to increase the 

reliability of the results, through the statistics of the operating 
income of H Enterprise in 10 years, the fitting function is 

obtained, according to this formula, the operating income of 

the company in the next four years is predicted, and the 

operating income of the company in the next four years is 

shown in table 3. 

 

Table 3. Company's forecast value from 2025 to 2028(in Yuan) 

 2025 2026 2027 2028 

Operating 

income Forecast 

14,716,7

37, 

15,483,3

07,907 

16,249,8

78,595 

17,016,4

49,283 

 

According to the proportion of each index and the predicted 

value of operating income, the specific amount of each index 
in the next 4 years is calculated. 

Taking the forecast of the contribution value of current 

assets in 2025 as an example, the specific calculation formula 

is: current assets contribution = Average balance of current 

assets × return on investment of current assets, the average 

balance of current assets in the year is 16,274,627,296.81 

yuan, the return rate of current assets investment is 3.1% of 

one-year bank loan rate (December 2024) , and the 

contribution value of current assets is 536,448,765.97 yuan, 

which can further predict the contribution value of current 

assets in the next few years. Similarly, the contribution of 
fixed assets and other intangible assets can be calculated and 

predicted, both using a five-year bank loan rate of 3.6% return 

on investment. 

= 769,549,714.25 yuan              

Economic value of available data assets =  00.41 

00.59=739,796,613.65 yuan 

3.5. Application of Fuzzy Comprehensive 
Evaluation Method 

3.5.1. Specific Analysis 
The fuzzy comprehensive evaluation method can 

effectively correct the subjective deviation in the evaluation 

results of the analytic hierarchy process and improve the 

objectivity of the evaluation. The steps are as follows: 

First, determine the factor set U and evaluation set k. 

Combined with the hierarchical structure model, the 
evaluation results of data quality indicators are taken as the 

key correction objects in the fuzzy comprehensive evaluation 

method, and the factor set U is set as: 

U = {ε1, ε2, … , εm}              (8) 

Where m is the number of secondary influencing factors of 

data quality under the data asset value evaluation system of 

pharmaceutical enterprises. 

The evaluation of the factors affecting the value of data 

assets is divided into N grades. After the discussion of the 

field experts, the grade number N is set to 5, and the 

evaluation set K is {excellent, good, medium, poor, bad}, the 
corresponding assignment of each grade is J = 

{1.0,0.8,0.6,0.4,0.2}. 

Secondly, the fuzzy evaluation matrix is constructed. Many 

experts are invited to score according to the actual situation 

of the index, and the evaluation proportion of each grade is 

counted, and the fuzzy evaluation matrix M is obtained by 

calculation. 

𝑀 = [

𝑗11 𝑗12 ⋯ 𝑗15
𝑗21 𝑗22 ⋯ 𝑗25
⋮ ⋮ ⋱ ⋮

𝑗61 𝑗62 ⋯ 𝑗65

]            (9) 

Third, determine the weight vector. Using the analytic 

hierarchy process, the weight set W = (W1, W2, ..., W6) of U 

is calculated based on expert opinions 

∑  6𝑖=1 𝑤𝑖 = 1               (10) 

Fourth, calculate the correction coefficient. The weighted 

average method is used to calculate the fuzzy matrix and 

weight vector: 

I=W0M=(i1, i2, ..., i5)              

𝜇 =
∑  𝑛𝑖=1 𝐽𝑖×𝐼𝑖

∑  𝑛𝑖=1 𝐼𝑖
               (11) 

Finally, the revised value of the data asset is derived: 

𝑉 = 𝜇 ∗ 𝑄 ∗ 𝑉economic value           (12) 

3.5.2. Fuzzy Comprehensive Evaluation Method to 
Adjust the Results 

According to the principle of the fuzzy comprehensive 

evaluation method, the factor set u = {normative, integrity, 

accuracy, consistency, timeliness, accessibility} is selected. 

According to the scores of many experts, the fuzzy 

evaluation matrix M is as follows: 



 

149 

𝑀 =

[
 
 
 
 
 
0.4 0.4 0.2 0 0
0.2 0.8 0 0 0
0.8 0.2 0 0 0
0.2 0.4 0.4 0 0
0.2 0.4 0.4 0 0
0.4 0.4 0.2 0 0]

 
 
 
 
 

 

It is calculated as: Weight set W = (0.17, 0.17, 0.19, 0.15, 

0.15, 0.17)  

Fuzzy comprehensive evaluation vector I= (0.38, 0.43, 

0.19, 0.00.0.00)  

The correction coefficient μ = 0.84 
The data asset value of Company H was V = 554, 117, 

583.70 yuan. 

4. Conclusion and Prospect 

Data assets play an increasingly prominent driving role for 

enterprises, so analyzing and evaluating their value is of great 

practical significance. Through an in-depth study of data asset 

evaluation methods and applications, this paper develops a 

value evaluation system for pharmaceutical enterprise H 

Company. It uses the analytic hierarchy process, replacement 

cost method, multi-period excess income method, and fuzzy 

comprehensive evaluation method to evaluate and quantify 
data asset value. 

This paper expands the data asset valuation research system 

through theoretical innovation and method improvement. It 

enriches relevant research results by building a valuation 

model suitable for pharmaceutical enterprises to provide 

reference for subsequent application of related theoretical 

models. It also proposes index selection criteria and 

quantitative methods. To address gaps in current studies, it 

conducts in-depth research at H Company with industry 

experts to solve problems of unclear index criteria and lack of 

quantification. 
Finally, the paper proposes building a classification 

framework by referencing national standards to enhance data 

asset tradeability, and establishing a dynamic data asset 

monitoring and evaluation model to facilitate timely 

responses to value changes and support enterprise resource 

allocation. 

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