Dynamic evaluation method for data asset value

Abstract

The invention discloses a dynamic evaluation method for data asset value, and relates to the technical field of data asset value evaluation.The dynamic evaluation method for the data asset value comprises the following specific operation steps that S1, all data assets of a company are determined; s2, performing classified calculation on the data assets according to the types of the data assets; s3, according to the calculated values, inputting the values into a data warehouse in sequence; s4, inputting a calculation result into a data real-time integration system; and S5, inputting the calculation result into an evaluation data overall system, and generating a real-time broken line graph. According to the dynamic evaluation method for the data asset value, each data asset can be analyzed and sorted in real time and summarized to generate a real-time broken line graph, so that people can conveniently know the value of the data assets in real time, and a scientific basis is provided for value management and maintenance of the data assets.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset value evaluation, and particularly to a dynamic evaluation method for data asset value. Background Art

[0002] In the context of the big data era and digital economy, the intersection of information technology and economic society has led to the rapid growth of various types of data. Data rights confirmation, data valuation, and data trading are the three key points in the marketization of data elements. Among them, data asset valuation provides a value measurement basis for the circulation of data elements. Valuing and further pricing data assets will play a positive role in promoting the construction of the data element market.

[0003] Traditional value evaluation methods include the market method, cost method, and income method. They each have their own advantages and limitations. In actual value evaluation, traditional value evaluation methods are usually used as static value evaluation methods and cannot handle the real-time dynamic changes of data value. Dynamically and real-time evaluating the value of data assets requires comprehensive consideration of various aspects of data, including traditional valuation methods, data-specific attributes, data application scenarios, and complex market characteristics, etc., in order to more accurately determine the value of data, promote the effective circulation and application of data in the market. Precise data dynamic valuation can reflect the dynamic changes of data assets in real time, provide real-time dynamic value evaluation for data assets, and provide a scientific basis for the value management and maintenance of data assets. However, there is no effective method for dynamically and real-time evaluating the value of data assets in the existing technology. Summary of the Invention

[0004] The purpose of the present invention is to at least solve one of the technical problems existing in the prior art, and provide a dynamic evaluation method for data asset value, which can solve the problems in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A dynamic evaluation method for data asset value, including the following specific operation steps:

[0006] S1. Determine all data assets of the company;

[0007] S2. Classify and calculate data assets according to the types of data assets;

[0008] S3. Enter the values calculated above into the data warehouse in sequence;

[0009] S4. Enter the results calculated above into the data real-time integration system;

[0010] S5. Enter the above calculation results into the overall evaluation data system to generate a real-time line chart.

[0011] Preferably, all the data assets in S1 include data resources (customer information, market trends, business process records, etc.), digital securities, virtual items, digital copyrights, and other forms of digital assets (virtual currencies, digitized intellectual property content, accounts, etc.).

[0012] Preferably, the calculation method in S2 includes: The market approach is used to calculate data resources, and the formula is: The value of the big data asset to be evaluated = The value of the comparable instance big data asset × Technical correction coefficient × Value density correction coefficient × Date correction coefficient × Capacity correction coefficient × Other correction coefficients;

[0013] For digital securities, by grasping the real-time value, the formula is: Stock market value = Current stock price × Number of outstanding shares;

[0014] For digital copyrights, the value of the intangible asset is determined according to market transactions.

[0015] Preferably, in S3, a calculation module is set in the data warehouse, and the calculation formula is entered into the calculation module to evaluate the value of each data asset.

[0016] Preferably, in S4, the overall value of the data assets is calculated by means of mutual accumulation.

[0017] Preferably, in S5, the obtained data is entered into the evaluation data sorting system by using the input module to form a line chart.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] (1) This dynamic evaluation method for the value of data assets can perform real-time analysis and sorting on each data asset, and summarize them to generate a real-time line chart, thus facilitating people to understand the value of data assets in real time and providing a scientific basis for the value management and maintenance of data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below with reference to the drawings and embodiments:

[0021] Figure 1 is the method flow chart of the present invention;

[0022] Figure 2 is the schematic diagram of the dynamic change and real-time valuation of the data asset value of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0023] This section will describe in detail the specific embodiments of the present invention. The preferred embodiments of the present invention are shown in the accompanying drawings. The function of the accompanying drawings is to supplement the description in the text part of the specification, enabling people to intuitively and vividly understand each technical feature and the overall technical solution of the present invention. However, it should not be construed as a limitation on the protection scope of the present invention.

[0024] As Figure 1 shown, a dynamic evaluation method for the value of data assets includes:

[0025] S1. Determine all the data assets of the company, such as data resources (customer information, market trends, business process records, etc.), digital securities, virtual items, digital copyrights, and other forms of digital assets (virtual currencies, digitalized intellectual property content, accounts, etc.).

[0026] S2. Classify and calculate the data assets according to the types of data assets. For example, the market approach is used for data resources, and the formula is: the value of the big data asset to be evaluated = the value of the comparable example big data asset × technical correction coefficient × value density correction coefficient × date correction coefficient × capacity correction coefficient × other correction coefficients

[0027] Among them: The technical correction coefficient mainly considers the value differences of data assets brought about by technical factors, usually including factors such as data acquisition, data storage, data processing, data mining, data protection, and data sharing;

[0028] The date correction coefficient mainly considers the value differences of data assets brought about by the difference between the evaluation benchmark date and the transaction date of the comparable case. Generally speaking, the closer to the evaluation benchmark date, the more it can reflect the transaction price in a similar business environment, and the smaller the value difference; Date correction coefficient = price index of the evaluation benchmark date / price index of the comparable case transaction date;

[0029] The capacity correction coefficient mainly considers the value differences of data assets brought about by different data capacities. Generally, when the value density is close, the larger the capacity, the higher the total value of the data assets;

[0030] The value density correction coefficient mainly considers the value differences of data assets brought about by the different proportions of effective data in the total data. The larger the proportion of effective data (the part of the data in the total data that contributes to the overall value) in the total data volume, the higher the total value of the data assets;

[0031] The other correction coefficients mainly consider other factors that affect the value differences of data assets according to the specific situation of the data assets in the physical evaluation of assets. The comparable case differences can be considered according to the actual situation to select the correction coefficients;

[0032] Among them, all factors are multiplied to obtain the adjustment factor.

[0033] For digital securities, by grasping the real-time value and using the formula: stock market value = current stock price × number of outstanding shares, it can directly reflect the overall value of the company in the stock market.

[0034] For virtual items and other forms of digital assets (such as virtual currency), according to the formula: current value × holding quantity, it can directly reflect the overall value of virtual items and other forms of digital assets.

[0035] For digital copyrights, by determining the value of intangible assets based on market transactions, which is applicable to patents, trademarks, copyrights, etc. Generally, the license fee of the above intangible assets is calculated as a percentage of the income according to the agreement reached by both parties to the transaction.

[0036] S3. Enter the data into the data warehouse in sequence according to the calculated value above, set a calculation module in the data warehouse, and enter the calculation formula into the calculation module to evaluate the value of each data asset.

[0037] S4. Enter the above calculated results into the data real-time integration system, and calculate the overall value of the data assets by the way of mutual accumulation.

[0038] S5. Enter the above calculated results into the overall evaluation data system to generate a real-time line chart, so that personnel can understand the evaluation value of the data assets in real time.

[0039] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art in the said technical field, various changes can be made without departing from the gist of the present invention.

Claims

1. A dynamic evaluation method for the value of data assets, characterized in that: It includes the following specific operation steps: S1. Determine all the data assets of the company; S2. Classify and calculate the data assets according to the types of data assets; S3. Enter them into the data warehouse in sequence according to the value calculated above; S4. Enter the result calculated above into the data real-time integration system; S5. Enter the above calculation result into the overall evaluation data system to generate a real-time line chart.

2. The dynamic evaluation method for the value of data assets according to claim 1, wherein: All the data assets in S1 include data resources (customer information, market trends, business process records, etc.), digital securities, virtual items, digital copyrights, and other forms of digital assets (virtual currency, digitalized intellectual property content, accounts, etc.).

3. The dynamic evaluation method for the value of data assets according to claim 1, wherein: The calculation methods in S2 include: For data resources, the market approach is used for calculation, and the formula is: The value of the big data asset to be evaluated = The value of the comparable instance big data asset × Technical correction coefficient × Value density correction coefficient × Date correction coefficient × Capacity correction coefficient × Other correction coefficients; For digital securities, by mastering the real-time value, the formula is: Stock market value = Current stock price × Number of outstanding shares; For digital copyrights, the value of the intangible asset is determined according to market transactions.

4. The dynamic evaluation method for the value of data assets according to claim 1, wherein: In S3, a calculation module is set in the data warehouse, and the calculation formula is entered into the calculation module to evaluate the value of each type of data asset.

5. The dynamic evaluation method for the value of data assets according to claim 1, wherein: In S4, the overall value of the data assets is calculated by the way of mutual accumulation.

6. The dynamic evaluation method for the value of data assets according to claim 1, wherein: In S5, the obtained data is entered into the evaluation data sorting system by using the input module to form a line chart.

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