Data asset assessment method based on revenue method

Abstract

The invention provides a data asset assessment method based on a revenue method, and the method comprises the following steps: S1, carrying out the design of a data scene through referring to the related cases of national data exchange, so as to obtain the related data content, and carrying out the multi-dimensional analysis of the data content; s2, determining a target customer group of the application scene, wherein the target customer group comprises customers directly associated with the data content and customers for realizing data required by other data through the data content; s3, determining a data source and determining a data product form; s4, carrying out value measurement and calculation, including unit price and charging mode determination, scene income analysis, total cost analysis, income deadline determination, income measurement and calculation, expected income determination, discount rate determination and evaluation value determination; s5, determining the discount rate, and evaluating the value to measure and calculate the convertible economic benefits; and S6, determining an evaluation value.

Description

Technical Field

[0001] The present invention relates to an evaluation method, and in particular to a data asset evaluation method based on an income approach. Background Art

[0003] The data asset evaluation process is indispensable in the process of building the data factor market, and the development of the income approach was born from the practice of asset evaluation. With the continuous development of the market economy, people have gradually realized that the value of an asset depends not only on its acquisition cost, but also on the economic benefits it can generate in the future. Therefore, the income approach has gradually become an important asset evaluation method and has been widely used in the field of data asset evaluation. Summary of the invention

[0004] The present invention provides a data asset evaluation method based on the income approach, which solves the problem of data asset evaluation. The technical solution is as follows:

[0005] A data asset evaluation method based on the income approach includes the following steps:

[0006] S1: Design data scenarios with reference to relevant cases of the National Digital Exchange to obtain relevant data content and conduct multi-dimensional analysis of the data content;

[0007] S2: Determine the target customer group of the application scenario, wherein the target customer group includes customers who are directly related to the data content and customers who need to obtain other data through the data content;

[0008] S3: Clarify the data source and determine the form of data products;

[0009] S4: Carry out value calculation, including clarifying the unit price and charging method, scenario income analysis, total cost analysis, determining the income period, income calculation, determining the expected income, determining the discount rate, and determining the assessment value;

[0010] S5: Determine the discount rate and evaluate the economic benefits that the value measurement can convert;

[0011] S6: Determine the appraisal value.

[0012] Furthermore, in step S1, the data scenario needs to obtain the node that generates the data, and the node has a constraint and control effect on the data content.

[0013] Furthermore, in step S2, the multi-dimensional analysis includes analyzing the generation time, node type, data type and data size of the data content.

[0014] Furthermore, in step S4, the value of data assets is calculated in the following ways:

[0015] (1) Annual fee is charged, with no limit on the number of inquiries;

[0016] (2) Comprehensively analyze the number of potential customers based on market conditions;

[0017] (3) Analyze the development costs of similar data products;

[0018] (4) When the application scenario is a sustainable scenario, the stable income period and cycle factors should be comprehensively considered.

[0019] Furthermore, when the application scenario is a sustainable scenario, the total income is calculated first, taking into account the stable income period and cycle factors, and then the total income is divided between the evaluated data assets and other assets that contribute to the generation of total income, and the profit sharing rate is used to calculate the shared income:

[0020] Ft=Rt*Kt2;

[0021] In the formula, Ft represents the predicted income of the data assets in the tth period; Rt represents the predicted total revenue or profit before interest and taxes in the tth period; Kt2 represents the predicted net profit sharing rate of the data assets in the tth period.

[0022] Furthermore, the net profit share rate of the data assets is derived using the hierarchical analysis method, which analyzes the contribution of various factors to the profit acquisition of data assets.

[0023] Furthermore, in step S5, the formula of the discount rate is: discount rate = risk-free rate of return + risk rate of return + individual risk adjustment.

[0024] Furthermore, in step S6, the assessed value is determined by summing up the present value of the data asset income in each year to determine the data asset assessed value.

[0025] The data asset valuation method based on the income approach has the following advantages: (1) It directly reflects the profit potential and future value of data assets. (2) It is easy to understand and accept. The valuation principles and methods of the income approach are relatively simple and clear, and are easy for evaluators and stakeholders to understand and accept, making it easier to realize the value of data assets. (3) Combined with real cases, it organically combines theory and practice. The adjustment of parameters in the practice process makes the income approach calculation method more valuable. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flowchart of the data asset valuation method based on the income approach. DETAILED DESCRIPTION

[0027] In the practice of data asset valuation, it is found that the future income of most data assets can be reasonably expected and measured in monetary terms, and the future income and risks can be predicted and quantified. Therefore, the income method after practical adjustment is often used. The comparison table of commonly used data asset evaluation methods is shown in Table 1:

[0028] Table 1 Comparison of commonly used data asset evaluation methods

[0029]

[0030] like Figure 1 As shown, the data asset evaluation method based on the income approach includes the following steps:

[0031] S1: Design data scenarios with reference to relevant cases of the National Digital Exchange to obtain relevant data content and conduct multi-dimensional analysis of the data content;

[0032] Taking the valorization of highway traffic data as an example, toll stations are important traffic nodes on the road, and the data they generate is extremely valuable. These data mainly include vehicle traffic data, toll data, traffic data, traffic status data, etc., which are of great significance to road management, traffic planning, toll collection strategies, etc. Through in-depth analysis of these data, we can better understand traffic flow, vehicle traffic conditions, road usage, etc.

[0033] Based on the highway toll station traffic data product, insurance companies and logistics companies can query the traffic flow data of the toll station during the specified period by entering the name of the highway toll station and the specified time period, including the toll station number, toll station name, entrance and exit type, traffic medium type, and traffic flow of each type of vehicle under the payment method. This can help regional insurance companies assess risks, adjust insurance premiums and underwriting policies based on vehicle type and traffic flow information, and help regional logistics companies determine the best route and time to reduce transportation costs and avoid congestion.

[0034] S2: Determine the target customer group of the application scenario, wherein the target customer group includes customers who are directly related to the data content and customers who need to obtain other data through the data content;

[0035] Based on the traffic data products of highway toll stations, we select target customers with demand, such as Ping An of China, Taiping Property, Sunshine Property and other companies.

[0036] S3: Clarify the data source and determine the form of data products;

[0037] The data for this scenario mainly comes from the "One Table System for Operation Management" built by a highway group. Currently, the highway group has a total of 274 toll stations. The current daily traffic volume is between 1.1 million and 1.2 million vehicles, and the peak traffic volume during holidays can reach 2.5 million vehicles. See Table 2 for detailed system and data field example information.

[0038] Table 2 Entrance traffic flow data table

[0039]

[0040] S4: Carry out value calculation, including clarifying the unit price and charging method, scenario income analysis, total cost analysis, determining the income period, income calculation, determining the expected income, determining the discount rate, and determining the assessment value;

[0041] For data assets, it is necessary to measure their value, which can be done in the following ways:

[0042] First, this scenario adopts an annual fee, i.e. 100,000 yuan / year, with no limit on the number of queries;

[0043] Second, based on a comprehensive analysis of market conditions, there are at least 394 potential logistics and insurance customers for data products;

[0044] Third, combined with the development costs of similar data products, the initial construction cost of this scenario is estimated to be 300,000 yuan, and the annual product operation and promotion expenses, enterprise-related fixed operating costs and other fixed costs are approximately 45% of the annual revenue.

[0045] Fourth, as highway toll collection is a sustainable scenario, the revenue period is determined to be 10 years after comprehensive consideration of factors such as stable revenue period and cycle. The revenue and total cost of the scenario in ten years based on the above factors are calculated (see Table 3):

[0046] Table 3 Scenario income estimation for the next ten years

[0047]

[0048] This assessment first calculates the total revenue and then divides it between the data assets being assessed and other assets that contribute to the generation of the total revenue.

[0049] This time, the profit sharing rate is used to calculate the profit sharing income:

[0050] Ft=Rt*Kt2;

[0051] Where:

[0052] Ft——the predicted income of the data asset in period t;

[0053] Rt——forecast total revenue or EBIT for period t;

[0054] Kt2——Predict the net profit sharing rate of data assets in period t.

[0055] The net profit share rate of data assets is obtained by using the analytic hierarchy process (AHP), and the contribution of each factor to the profit of data assets is analyzed. According to its own characteristics, the classification of overall assets is defined as data assets, intangible assets such as intellectual property rights, and other assets. These three factors form the "schema layer (bottom layer)" of the entire analytic hierarchy process (see Table 4).

[0056] Table 4. Net profit sharing rate of data assets confirmed by AHP

[0057]

[0058] After analysis, the average contribution of data assets to profits is 70.74% of the total assets. Calculate the expected returns for the next ten years (see Table 5).

[0059] Table 5 Expected income table for the next ten years

[0060]

[0061] S5: Determine the discount rate and evaluate the economic benefits that the value measurement can convert;

[0062] The formula for the discount rate is: discount rate = risk-free rate of return + risk-return rate + individual risk adjustment. The one-year deposit rate announced by the People's Bank of China in August 2024 is selected as the risk-free rate of return of 1.55%; the risk-reward rate is calculated through comprehensive analysis to be 7.50%; the comprehensive individual risk adjustment coefficient is 3%; in summary, the discount rate used in this assessment is 12.05%.

[0063] S6: Determine the assessed value by summing up the present value of the data asset income in each year to determine the assessed value of the data assets.

[0064] For example, after calculation, the specific amount is 382.7493549 million yuan.

[0065] The present invention has the following beneficial effects:

[0066] 1. Truly reflect the value of data assets

[0067] The income approach can truly reflect the future earnings capacity of data assets by predicting the future earnings of data assets and discounting them to the current point in time. This method not only takes into account the current use value of data assets, but also their potential future earnings, thereby being able to more comprehensively evaluate the value of data assets.

[0068] 2. Easy to understand and accept

[0069] The evaluation principle and method of the income approach are relatively simple and clear, and are easy to be understood and accepted by the evaluators and stakeholders. It avoids complex mathematical models and calculation processes, making the evaluation results more intuitive and easy to understand. At the same time, because the income approach can be directly linked to the operating income of data assets, it is easier to be accepted and recognized by all parties in transactions.

[0070] 3. Closely linked to the market environment

[0071] The income approach fully considers the impact of the market environment on the value of data assets during the evaluation process. By predicting the changes and trends in the future market environment and the impact of these changes on the returns of data assets, the income approach can more accurately evaluate the value of data assets in the current market environment. This makes the evaluation results more in line with market reality and helps guide the trading and pricing of data assets.

[0072] 4. High flexibility

[0073] The income approach can be flexibly adjusted according to different data asset types and evaluation purposes. For different types of data assets, such as databases, data platforms, data analysis reports, etc., the income approach can conduct personalized evaluations based on their characteristics and uses. At the same time, for different evaluation purposes, such as investment decisions, mergers and acquisitions, asset disposal, etc., the income approach can also provide corresponding evaluation methods and parameter settings.

[0074] 5. Promote the effective use of data assets

[0075] Through the evaluation of the income approach, we can clearly understand the future profitability and potential value of data assets. This helps enterprises or individuals better understand and manage their data assets, thereby promoting the effective use of data assets and maximizing their value. At the same time, the evaluation results of the income approach can also provide important references for the transaction and pricing of data assets, which will help promote the healthy development of the data asset market.

Claims

1. A data asset evaluation method based on the income approach, comprising the following steps: S1: Design data scenarios with reference to relevant cases of the National Digital Exchange to obtain relevant data content and conduct multi-dimensional analysis of the data content; S2: Determine the target customer group of the application scenario, wherein the target customer group includes customers who are directly related to the data content and customers who need to obtain other data through the data content; S3: Clarify the data source and determine the form of data products; S4: Carry out value calculation, including clarifying the unit price and charging method, scenario income analysis, total cost analysis, determining the income period, income calculation, determining the expected income, determining the discount rate, and determining the assessment value; S5: Determine the discount rate and evaluate the economic benefits that the value measurement can convert; S6: Determine the appraisal value.

2. The data asset evaluation method based on the income approach according to claim 1 is characterized by: In step S1, the data scenario needs to obtain the node that generates the data, and the node has a constraint and control effect on the data content.

3. The data asset evaluation method based on the income approach according to claim 2 is characterized by: In step S2, the multi-dimensional analysis includes analyzing the generation time, node type, data type and data size of the data content.

4. The data asset evaluation method based on the income approach according to claim 1 is characterized by: In step S4, the value of data assets is calculated in the following ways: (1) Annual fee is charged, with no limit on the number of inquiries; (2) Comprehensively analyze the number of potential customers based on market conditions; (3) Analyze the development costs of similar data products; (4) When the application scenario is a sustainable scenario, the stable income period and cycle factors should be comprehensively considered.

5. The data asset evaluation method based on the income approach according to claim 4 is characterized by: When the application scenario is a sustainable scenario, the total income is calculated first, taking into account the stable income period and cycle factors, and then the total income is divided between the evaluated data assets and other assets that contribute to the total income. The profit sharing rate is used to calculate the shared income: Ft=Rt*Kt2; In the formula, Ft represents the predicted income of the data assets in the tth period; Rt represents the predicted total revenue or profit before interest and taxes in the tth period; Kt2 represents the predicted net profit sharing rate of the data assets in the tth period.

6. The data asset evaluation method based on the income approach according to claim 5 is characterized by: The net profit share rate of the data assets is derived using the analytic hierarchy process, which analyzes the contribution of various factors to the profit acquisition of data assets.

7. The data asset evaluation method based on the income approach according to claim 1 is characterized by: In step S5, the formula of the discount rate is: discount rate = risk-free rate of return + risk rate of return + individual risk adjustment.

8. The data asset evaluation method based on the income approach according to claim 1 is characterized by: In step S6, the evaluation value is determined by summing up the present value of the data asset income in each year to determine the data asset evaluation value.

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