Method and system for evaluating value of data assets based on chain aggregation, fusion efficiency and gain
By constructing a data asset chain and combining it with a dynamic weight model, the problem of dimensional fragmentation in the traditional method of data asset value assessment is solved, enabling refined quantification and forward-looking value-added prediction of data assets, and improving assessment efficiency.
Patent Information
- Application Number
- CN202511288003.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-10
- Publication Date
- 2025-12-12
AI Technical Summary
Existing data asset valuation methods are insufficient in handling the dynamism, collaboration, multi-chain integration, and spillover effects of data. Traditional methods are unable to accurately quantify the continuous value-added and cross-chain gains of data.
A data asset value assessment method based on clustering, integration effect, and gain is adopted. By constructing a data asset chain, the clustering value, integration effect value, and gain value are calculated. A comprehensive assessment is then conducted in conjunction with a dynamic weighting model to generate a visual report.
It enables refined quantification of the intrinsic quality and relationships of data assets, dynamically reflects value transformation, provides forward-looking value-added predictions, improves assessment efficiency by more than 80%, and solves the problem of assessment results being disconnected from business operations.
Smart Images
Figure CN121120256A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of information technology, and in particular to a method and system for evaluating the value of data assets based on aggregation, synergy and gain. BACKGROUND
[0002] With the rapid development of global digital economy, data has become the fifth production factor after land, capital, labor and technology. Countries are accelerating the construction of data factor markets. The United States and the European Union have developed systematic rules in data governance and cross-border data circulation, and promoted the value release of data in finance, manufacturing, medical care and energy industries.
[0003] The International Organization for Standardization (ISO) and the International Telecommunication Union (ITU) are also actively promoting the development of data asset-related standards, such as ISO / IEC 38505 (data governance), which provides a reference framework for data asset management and evaluation worldwide.
[0004] After the implementation of the Data Security Law and the Personal Information Protection Law, the rights, circulation and transaction of data assets have gradually been standardized. In the evaluation practice, the traditional methods of cost method, market method and income method are still mainly used, but these methods have shortcomings in dealing with the dynamic nature, synergy, multi-chain fusion and spillover effect of data. Among them, the cost method only considers the historical cost of data acquisition, storage and processing, ignoring the continuous value-added and multiple use value of data. The market method relies on comparable transaction cases, but the data transaction market is not mature and the comparable cases are limited. The income method is based on the discounting of future cash flows, but it is difficult to quantify the synergistic effect and cross-chain gain of data. SUMMARY
[0005] Therefore, the purpose of the present application is to provide a method and system for evaluating the value of data assets based on aggregation, synergy and gain to solve the problems mentioned in the background.
[0006] In order to achieve the above-mentioned purpose, the technical solution adopted by the present application is as follows: The method for evaluating the value of data assets based on aggregation, synergy and gain comprises the following steps: S1, data aggregation processing step: acquiring original data and external data sources, constructing a data asset chain, and assigning a quality weight to the data asset chain, and outputting the aggregation value; S2, data synergy analysis step: receiving the aggregation value, calculating the efficiency conversion value of data in a predetermined business process, and outputting the synergy value; S3, data gain evaluation step: receiving the aggregation value and the synergy value, predicting the potential value-added space of data assets, and outputting the gain value; S4. Multidimensional value comprehensive evaluation steps: Receive the cluster value, integration value and gain value, integrate the three through a dynamic weight model, calculate and output the comprehensive value score; S5. Report generation steps: Based on the comprehensive value score, generate a visual evaluation report that includes value composition analysis and optimization suggestions.
[0007] Furthermore, the data aggregation chain processing steps in step S1 include: constructing a data lineage map, mapping application scenarios, and assigning quality weights based on data integrity and link depth.
[0008] Furthermore, the data fusion analysis step S2 includes one or more of the following: quantifying decision support, cost savings rate, and efficiency improvement value.
[0009] Furthermore, the data gain assessment step S3 includes calculating one or more of the following: market potential index, technological scalability, and risk-adjusted return.
[0010] Furthermore, the dynamic weighting model in step S4 dynamically adjusts the weight allocation of chain value, integration value, and gain value based on industry benchmarks and / or corporate strategy.
[0011] A system for assessing the value of data assets based on aggregation, fusion efficiency, and gains includes a processor and a memory. The memory stores programs or instructions, which, when executed by the processor, perform the following steps: S1. Data aggregation chain processing steps: Obtain raw data and external data sources, construct a data asset chain, assign quality weights to the data asset chain, and output the aggregation chain value; S2. Data integration and efficiency analysis steps: Receive the cluster value, calculate the efficiency conversion value of the data in the preset business process, and output the integration value; S3. Data gain assessment step: Receive the cluster value and the fusion value, predict the potential value-added space of data assets, and output the gain value; S4. Multidimensional value comprehensive evaluation steps: Receive the cluster value, integration value and gain value, integrate the three through a dynamic weight model, calculate and output the comprehensive value score; S5. Report generation steps: Based on the comprehensive value score, generate a visual evaluation report that includes value composition analysis and optimization suggestions.
[0012] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The data aggregation chain processing steps in step S1 include: constructing a data lineage map, mapping application scenarios, and assigning quality weights based on data integrity and link depth.
[0013] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The data fusion analysis step S2 includes one or more of the following: quantifying decision support, cost saving rate, and efficiency improvement value.
[0014] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The data gain assessment step S3 includes calculating one or more of the following: market potential index, technology scalability, and risk-adjusted return.
[0015] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The dynamic weighting model in step S4 dynamically adjusts the weighting of chain value, integration value, and gain value based on industry benchmarks and / or corporate strategy.
[0016] The beneficial effects of this invention are: The present invention provides a method and system for evaluating the value of data assets based on aggregation chains, fusion efficiency, and gain. Specifically, it includes: acquiring raw data and external data sources, constructing a data asset chain, assigning quality weights to the data asset chain, and outputting the aggregation chain value; receiving the aggregation chain value, calculating the efficiency conversion value of the data in a preset business process, and outputting the fusion efficiency value; receiving the aggregation chain value and the fusion efficiency value, predicting the potential value-added space of the data asset, and outputting the gain value; receiving the aggregation chain value, fusion efficiency value, and gain value, integrating the three through a dynamic weighting model, calculating and outputting a comprehensive value score; and generating a visualized evaluation report containing value composition analysis and optimization suggestions based on the comprehensive value score. By incorporating data chain aggregation, business efficiency conversion, and potential value-added prediction into a unified framework, it effectively solves the problem of dimensional fragmentation in traditional methods; dynamic value quantification: by embedding fusion efficiency analysis into business processes, it reflects the dynamic value conversion of data assets in real time; gain prediction mechanism: combining market and technology trends, it accurately predicts the value-added space, providing forward-looking guidance for enterprise data asset operation; improved evaluation efficiency: adopting automated processing, it improves efficiency by more than 80% compared to manual evaluation. Attached Figure Description
[0017] Figure 1 The diagram shows the steps of the data asset value assessment method based on chain aggregation, fusion efficiency, and gain according to the present invention. Figure 2 The diagram shown is a structural block diagram of the data asset value assessment system based on clustering, fusion efficiency, and gain according to the present invention. Explanation of icon numbers: 1-Processor; 2-Memory. Detailed Implementation
[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments: like Figure 1 As shown, the method for evaluating the value of data assets based on chain aggregation, fusion efficiency, and gains provided by this invention includes the following steps: S1. Data aggregation chain processing steps: Obtain raw data and external data sources, construct a data asset chain, assign quality weights to the data asset chain, and output the aggregation chain value; S2. Data integration and efficiency analysis steps: Receive the cluster value, calculate the efficiency conversion value of the data in the preset business process, and output the integration value; S3. Data gain assessment step: Receive the cluster value and the fusion value, predict the potential value-added space of data assets, and output the gain value; S4. Multidimensional value comprehensive evaluation steps: Receive the cluster value, integration value and gain value, integrate the three through a dynamic weight model, calculate and output the comprehensive value score; S5. Report generation steps: Based on the comprehensive value score, generate a visual evaluation report that includes value composition analysis and optimization suggestions.
[0019] As can be seen from the above description, the present invention has the following beneficial effects: The present invention provides a method for evaluating the value of data assets based on aggregation chain, fusion efficiency, and gain, specifically including: acquiring raw data and external data sources, constructing a data asset chain, assigning quality weights to the data asset chain, and outputting the aggregation chain value; receiving the aggregation chain value, calculating the efficiency conversion value of the data in a preset business process, and outputting the fusion efficiency value; receiving the aggregation chain value and the fusion efficiency value, predicting the potential value-added space of the data asset, and outputting the gain value; receiving the aggregation chain value, fusion efficiency value, and gain value, integrating the three through a dynamic weight model, calculating and outputting a comprehensive value score; and generating a visualized evaluation report containing value composition analysis and optimization suggestions based on the comprehensive value score. By incorporating data chain aggregation, business efficiency conversion, and potential value-added prediction into a unified framework, the method effectively solves the problem of dimensional fragmentation in traditional methods; dynamic value quantification: by embedding fusion efficiency analysis into business processes, it reflects the dynamic value conversion of data assets in real time; gain prediction mechanism: by combining market and technology trends, it accurately predicts the value-added space, providing forward-looking guidance for enterprise data asset operation; and improved evaluation efficiency: by adopting automated processing, the efficiency is improved by more than 80% compared to manual evaluation.
[0020] Furthermore, the data aggregation chain processing steps in step S1 include: constructing a data lineage map, mapping application scenarios, and assigning quality weights based on data integrity and link depth.
[0021] As described above, this approach achieves refined and structured measurement of the intrinsic quality and relationships of data assets. By constructing lineage maps and application scenario mappings, the source, flow, and business context of data are clarified, ensuring that the evaluation results are no longer isolated numbers but rather interpretable and business-relevant value judgments. This solves the problem of the evaluation results being disconnected from the actual business context of the data.
[0022] Furthermore, the data fusion analysis step S2 includes one or more of the following: quantifying decision support, cost savings rate, and efficiency improvement value.
[0023] As described above, directly linking data value to specific business process performance improvements quantifies and "utilizes" data value. By calculating indicators such as decision support and cost savings, abstract data value is transformed into concrete and perceptible business benefits (such as increased efficiency and reduced costs), powerfully demonstrating the actual contribution of data in driving business growth and optimizing operations, thus solving the problem of data value being "invisible and intangible."
[0024] Furthermore, the data gain assessment step S3 includes calculating one or more of the following: market potential index, technological scalability, and risk-adjusted return.
[0025] As described above, the introduction of a future value prediction mechanism breaks through the limitations of traditional evaluation methods that only focus on historical costs and current benefits. By assessing market potential and technological scalability, it is possible to capture the potential value-added space and long-tail effect brought about by data assets through reuse, sharing, and externalities, providing a forward-looking decision-making basis for enterprise data strategy investment and innovative applications.
[0026] Furthermore, the dynamic weighting model in step S4 dynamically adjusts the weight allocation of chain value, integration value, and gain value based on industry benchmarks and / or corporate strategy.
[0027] As described above, this gives the evaluation system a high degree of flexibility and adaptability. By dynamically adjusting weights according to industry characteristics and corporate strategies, the same evaluation method can be universally applied to different industries such as finance, manufacturing, and e-commerce. It can also be tailored to the individual strategic goals of different companies (such as whether to pursue efficiency or innovation), outputting more instructive and customized evaluation results, thus solving the problem of poor applicability of fixed-weight models.
[0028] See Figure 2 The system for evaluating the value of data assets based on clustering, fusion efficiency, and gain provided by the present invention includes a processor 1 and a memory 2. The memory 2 stores programs or instructions, and when the programs or instructions are executed by the processor 1, they perform the following steps: S1. Data aggregation chain processing steps: Obtain raw data and external data sources, construct a data asset chain, assign quality weights to the data asset chain, and output the aggregation chain value; S2. Data integration and efficiency analysis steps: Receive the cluster value, calculate the efficiency conversion value of the data in the preset business process, and output the integration value; S3. Data gain assessment step: Receive the cluster value and the fusion value, predict the potential value-added space of data assets, and output the gain value; S4. Multidimensional value comprehensive evaluation steps: Receive the cluster value, integration value and gain value, integrate the three through a dynamic weight model, calculate and output the comprehensive value score; S5. Report generation steps: Based on the comprehensive value score, generate a visual evaluation report that includes value composition analysis and optimization suggestions.
[0029] As can be seen from the above description, the present invention has the following beneficial effects: This invention provides a system for evaluating the value of data assets based on aggregation chains, fusion efficiency, and gain. Specifically, it includes: acquiring raw data and external data sources, constructing a data asset chain, assigning quality weights to the data asset chain, and outputting the aggregation chain value; receiving the aggregation chain value, calculating the efficiency conversion value of the data in a preset business process, and outputting the fusion efficiency value; receiving the aggregation chain value and the fusion efficiency value, predicting the potential value-added space of the data asset, and outputting the gain value; receiving the aggregation chain value, fusion efficiency value, and gain value, integrating the three through a dynamic weighting model, calculating and outputting a comprehensive value score; and generating a visualized evaluation report containing value composition analysis and optimization suggestions based on the comprehensive value score. By incorporating data chain aggregation, business efficiency conversion, and potential value-added prediction into a unified framework, it effectively solves the problem of dimensional fragmentation in traditional methods; dynamic value quantification: by embedding fusion efficiency analysis into business processes, it reflects the dynamic value conversion of data assets in real time; gain prediction mechanism: combining market and technology trends, it accurately predicts the value-added space, providing forward-looking guidance for enterprise data asset operation; improved evaluation efficiency: adopting automated processing, it improves efficiency by more than 80% compared to manual evaluation.
[0030] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The data aggregation chain processing steps in step S1 include: constructing a data lineage map, mapping application scenarios, and assigning quality weights based on data integrity and link depth.
[0031] As described above, this approach achieves refined and structured measurement of the intrinsic quality and relationships of data assets. By constructing lineage maps and application scenario mappings, the source, flow, and business context of data are clarified, ensuring that the evaluation results are no longer isolated numbers but rather interpretable and business-relevant value judgments. This solves the problem of the evaluation results being disconnected from the actual business context of the data.
[0032] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The data fusion analysis step S2 includes one or more of the following: quantifying decision support, cost saving rate, and efficiency improvement value.
[0033] As described above, directly linking data value to specific business process performance improvements quantifies and "utilizes" data value. By calculating indicators such as decision support and cost savings, abstract data value is transformed into concrete and perceptible business benefits (such as increased efficiency and reduced costs), powerfully demonstrating the actual contribution of data in driving business growth and optimizing operations, thus solving the problem of data value being "invisible and intangible."
[0034] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The data gain assessment step S3 includes calculating one or more of the following: market potential index, technology scalability, and risk-adjusted return.
[0035] As described above, the introduction of a future value prediction mechanism breaks through the limitations of traditional evaluation methods that only focus on historical costs and current benefits. By assessing market potential and technological scalability, it is possible to capture the potential value-added space and long-tail effect brought about by data assets through reuse, sharing, and externalities, providing a forward-looking decision-making basis for enterprise data strategy investment and innovative applications.
[0036] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The dynamic weighting model in step S4 dynamically adjusts the weighting of chain value, integration value, and gain value based on industry benchmarks and / or corporate strategy.
[0037] As described above, this gives the evaluation system a high degree of flexibility and adaptability. By dynamically adjusting weights according to industry characteristics and corporate strategies, the same evaluation method can be universally applied to different industries such as finance, manufacturing, and e-commerce. It can also be tailored to the individual strategic goals of different companies (such as whether to pursue efficiency or innovation), outputting more instructive and customized evaluation results, thus solving the problem of poor applicability of fixed-weight models.
[0038] The following are several preferred embodiments or application embodiments to help those skilled in the art better understand the technical content of the present invention and the technical contributions made by the present invention compared with the prior art: Preferred embodiment 1: The "New Three Methods" provided by this invention—the Chain Convergence Method, the Integration Effect Method, and the Gain Method—are an innovative evaluation system proposed based on the traditional three methods and combined with the development trends and industry realities of China's digital economy. The "New Three Methods" are used to evaluate the comprehensive performance of data assets in terms of multi-chain collaboration, integration effectiveness, and dynamic reuse spillover value. The three methods can be used individually or combined to form a composite evaluation model to adapt to different scenarios and needs. The core value of this system lies in: (1) Comprehensively measure the collaborative value of multiple chains (chain aggregation method); (2) Quantitative analysis of the operational improvement results after integration (integration effect method); (3) Capture the multiplier value of dynamic data reuse and spillover effects (gain method).
[0039] Against the backdrop of the current national push for market-based allocation of data elements and the construction of a national integrated big data center and data exchange, the "New Three Laws" have significant policy opportunities and promotional value.
[0040] like Figure 1 As shown, the method for evaluating the value of data assets based on chain aggregation, fusion efficiency, and gains provided by this invention includes the following steps: S1. Data aggregation chain processing steps: Obtain raw data and external data sources, construct a data asset chain, assign quality weights to the data asset chain, and output the aggregation chain value; In this embodiment, the data aggregation chain processing step S1 includes: constructing a data lineage map, mapping application scenarios, and allocating quality weights based on data integrity and link depth.
[0041] The steps described above enable a refined and structured measurement of the intrinsic quality and relationships of data assets. By constructing lineage maps and application scenario mappings, the source, flow, and business context of the data are clarified, ensuring that the evaluation results are no longer isolated numbers but rather value judgments with interpretability and business relevance, thus resolving the problem of the evaluation results being disconnected from the actual business context of the data.
[0042] S2. Data integration and efficiency analysis steps: Receive the cluster value, calculate the efficiency conversion value of the data in the preset business process, and output the integration value; In this embodiment, the data fusion analysis step S2 includes one or more of the following: quantifying decision support, cost saving rate, and efficiency improvement value.
[0043] The above steps directly link data value to specific business process performance improvements, realizing the quantification of data value through "utility". By calculating indicators such as decision support and cost savings, the abstract data value is transformed into concrete and perceptible business benefits (such as efficiency improvement and cost reduction), powerfully demonstrating the actual contribution of data in driving business growth and optimizing operations, and solving the problem of data value being "invisible and intangible".
[0044] S3. Data gain assessment step: Receive the cluster value and the fusion value, predict the potential value-added space of data assets, and output the gain value; In this embodiment, the data gain assessment step S3 includes: calculating one or more of the following: market potential index, technological scalability, and risk-adjusted return.
[0045] The steps described above introduce a mechanism for predicting future value, breaking through the limitations of traditional evaluation methods that only focus on historical costs and current benefits. By assessing market potential and technological scalability, it is possible to capture the potential added value and long-tail effects of data assets due to reuse, sharing, and externalities, providing a forward-looking decision-making basis for enterprise data strategy investment and innovative applications.
[0046] S4. Multidimensional value comprehensive evaluation steps: Receive the cluster value, integration value and gain value, integrate the three through a dynamic weight model, calculate and output the comprehensive value score; In this embodiment, the dynamic weight model in step S4 dynamically adjusts the weight allocation of chain value, integration value and gain value based on industry benchmarks and / or corporate strategies.
[0047] The above steps give the evaluation system a high degree of flexibility and adaptability. By dynamically adjusting the weights according to industry characteristics and corporate strategies, the same evaluation method can be universally applied to different industries such as finance, manufacturing, and e-commerce, and can be tailored to the individual strategic goals of different companies (such as whether to pursue efficiency or innovation), outputting more instructive and customized evaluation results, thus solving the problem of poor applicability of fixed-weight models.
[0048] S5. Report generation steps: Based on the comprehensive value score, generate a visual evaluation report that includes value composition analysis and optimization suggestions.
[0049] In this embodiment, the core idea of the clustering method is to measure the degree of synergy between the industrial chain, supply chain, blockchain, and data chain, and the comprehensive value they bring. The formula is as follows: Φ_chain =Σ(W_i × C_i); Where: W_i is the weight of the i-th chain (determined based on industry importance and strategic positioning); C_i is the synergy coefficient of the i-th chain (which can be determined through expert scoring, data analysis, etc.).
[0050] The clustering method indicator system (example) is shown in Table 1 below: Table 1 Example calculation: Φ_chain = 0.2×0.85 + 0.25×0.80 + 0.2×0.88 + 0.2×0.82 + 0.15×0.75 = 0.826 The integration method is used to measure the degree of improvement in efficiency, quality, cost, and risk control after the integration of different systems (industrial chain, supply chain, data chain, blockchain, etc.). The formula is as follows: Φ_fuse = Σ (W_j × ΔP_j); Where: W_j is the weight of the j-th improvement indicator; ΔP_j is the improvement (percentage) of the indicator before and after fusion; The indicator system for the integration effect method (example) is shown in Table 2 below: Table 2 Example calculation: Φ_fuse = 0.3×0.12 + 0.25×0.08 + 0.2×0.10 + 0.25×0.15 = 0.1125 The gain method builds upon the revenue method by adding an assessment of dynamic data reuse and spillover effects. The formula is as follows: Φ_gain = Base_Income × Reuse_Frequency × Spillover_Coefficient Where: Base_Income is the base income (unit: RMB 10,000 / year); Reuse_Frequency is the number of times data is reused (average per year); Spillover_Coefficient is the spillover coefficient (0~1).
[0051] Example calculation: If Base_Income = 300,000 yuan / year, Reuse_Frequency = 3, Spillover_Coefficient = 0.9, then Φ_gain = 30 × 3 × 0.9 = 810,000 yuan / year.
[0052] Preferred embodiment two: See Figure 2 The system for evaluating the value of data assets based on clustering, fusion efficiency, and gain provided by the present invention includes a processor 1 and a memory 2. The memory 2 stores programs or instructions, and when the programs or instructions are executed by the processor 1, they perform the following steps: S1. Data aggregation chain processing steps: Obtain raw data and external data sources, construct a data asset chain, assign quality weights to the data asset chain, and output the aggregation chain value; S2. Data integration and efficiency analysis steps: Receive the cluster value, calculate the efficiency conversion value of the data in the preset business process, and output the integration value; S3. Data gain assessment step: Receive the cluster value and the fusion value, predict the potential value-added space of data assets, and output the gain value; S4. Multidimensional value comprehensive evaluation steps: Receive the cluster value, integration value and gain value, integrate the three through a dynamic weight model, calculate and output the comprehensive value score; S5. Report generation steps: Based on the comprehensive value score, generate a visual evaluation report that includes value composition analysis and optimization suggestions.
[0053] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The data aggregation chain processing steps in step S1 include: constructing a data lineage map, mapping application scenarios, and assigning quality weights based on data integrity and link depth.
[0054] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The data fusion analysis step S2 includes one or more of the following: quantifying decision support, cost saving rate, and efficiency improvement value.
[0055] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The data gain assessment step S3 includes calculating one or more of the following: market potential index, technology scalability, and risk-adjusted return.
[0056] Furthermore, when the program or instructions are executed by the processor, the following steps are performed: The dynamic weighting model in step S4 dynamically adjusts the weighting of chain value, integration value, and gain value based on industry benchmarks and / or corporate strategy.
[0057] Preferred Embodiment Three In this embodiment, four industries—manufacturing, finance, cross-border e-commerce, and supply chain services—are selected as typical cases. Through detailed data collection, indicator calculation, method comparison, and result analysis, the practical application process and value of the "New Three Methods" in different industries are demonstrated.
[0058] 1. Manufacturing Cases Background: A smart manufacturing company plans to value its production and supply chain data as an asset for on-balance-sheet assessment and financing.
[0059] The manufacturing data collection list (example) is shown in Table 3 below: Table 3 Example of calculation steps (chain-linking method) 1. Calculate the synergy coefficient C_i of the industrial chain, supply chain, data chain, and blockchain based on the collected data.
[0060] 2. Determine the weights W_i (total = 1) based on the importance of the business.
[0061] 3. Apply the formula Φ_chain = Σ(W_i × C_i) to obtain the comprehensive synergy index.
[0062] Example calculation: Φ_chain = 0.25×0.85 + 0.25×0.80 + 0.25×0.82 + 0.25×0.78 = 0.8125 The results of the "new three methods" and the traditional three methods in manufacturing cases are compared in Table 4 below: Table 4 The results show that the gain method has the highest valuation, reflecting the significant value of data reuse and spillover effects in the manufacturing industry; the clustering method and the integration method are also significantly higher than the traditional three methods, indicating that multi-chain collaboration and integration improvement are important sources of value for manufacturing data assets.
[0063] 2. Cases in the financial industry Background: A bank is assessing the asset value of its customer data platform for internal management and external auditing purposes.
[0064] The collected data includes transaction records, customer risk rating model outputs, credit scores, etc.
[0065] The list of data collection for the financial industry (example) is shown in Table 5 below: Table 5 The following table 6 compares the results of the "new three laws" with the traditional three laws in financial industry cases: Table 6 3. Cross-border e-commerce cases Background: A cross-border e-commerce platform wants to assess the value of its transaction data, user behavior data, and logistics data in order to support financing in overseas markets.
[0066] The cross-border e-commerce data collection list (example) is shown in Table 7 below: Table 7 Example of calculation steps (gain method) 1. Calculate the base income: Base_Income = Average monthly order volume × Average order value × 12 (months).
[0067] 2. Calculate the average annual reuse frequency (e.g., reuse of advertising data, multi-channel utilization of promotional activities).
[0068] 3. Estimate the spillover coefficient (e.g., the proportion of sales driven by related brands).
[0069] 4. Apply the formula Φ_gain = Base_Income × Reuse_Frequency × Spillover_Coefficient.
[0070] Example calculation: Φ_gain = 12 million yuan × 1.5 × 0.8 = 14.4 million yuan The following table (Table 8) compares the results of the "new three laws" with the traditional three laws in cross-border e-commerce cases: Table 8 The results show that the clustering method, the fusion method, and the gain method all have higher valuations in cross-border e-commerce than the traditional three methods, especially in reflecting data reuse and cross-system collaboration.
[0071] 4. Supply Chain Service Cases Background: A supply chain service company needs to assess the value of its logistics node data, warehouse management data, and transaction matching data for financing and business optimization.
[0072] The supply chain service data collection list (example) is shown in Table 9 below: Table 9 Example of calculation steps (fusion method) 1. Identify improvement indicators, such as on-time delivery rate, inventory turnover days, and abnormal event rate.
[0073] 2. The improvement magnitude ΔP_j before and after statistical fusion.
[0074] 3. Assign weights W_j (total = 1) based on the importance of the indicators.
[0075] 4. Calculate the comprehensive improvement index using the formula Φ_fuse = Σ(W_j × ΔP_j).
[0076] Example calculation: Φ_fuse = 0.4×15% + 0.3×10% + 0.3×5% = 0.106, which is an improvement of 10.6%.
[0077] The results of the "new three methods" and the traditional three methods in supply chain service case studies are compared in Table 10 below: Table 10 The results show that the integration and efficiency method has the highest valuation in the supply chain service scenario, indicating that integration and improvement have a significant effect on enhancing the value of data assets in this industry.
[0078] The present invention has been described with reference to the above-described embodiments and accompanying drawings; however, the above embodiments are merely examples for implementing the present invention. It must be noted that the disclosed embodiments do not limit the scope of the present invention. Conversely, modifications and equivalents included within the spirit and scope of the claims are all included within the scope of the present invention.
Claims
1. A method for evaluating the value of data assets based on clustering, fusion efficiency, and gains, characterized in that: Includes the following steps: S1. Data aggregation chain processing steps: Obtain raw data and external data sources, construct a data asset chain, assign quality weights to the data asset chain, and output the aggregation chain value; S2. Data integration and efficiency analysis steps: Receive the cluster value, calculate the efficiency conversion value of the data in the preset business process, and output the integration value; S3. Data gain assessment step: Receive the cluster value and the fusion value, predict the potential value-added space of data assets, and output the gain value; S4. Multidimensional value comprehensive evaluation steps: Receive the cluster value, integration value and gain value, integrate the three through a dynamic weight model, calculate and output the comprehensive value score; S5. Report generation steps: Based on the comprehensive value score, generate a visual evaluation report that includes value composition analysis and optimization suggestions.
2. The method for assessing the value of data assets based on clustering, fusion efficiency, and gain according to claim 1, characterized in that, The data aggregation chain processing steps in step S1 include: constructing a data lineage map, mapping application scenarios, and assigning quality weights based on data integrity and link depth.
3. The method for assessing the value of data assets based on clustering, fusion efficiency, and gains according to claim 1, characterized in that, The data fusion analysis step S2 includes one or more of the following: quantifying decision support, cost saving rate, and efficiency improvement value.
4. The method for assessing the value of data assets based on clustering, fusion efficiency, and gains according to claim 1, characterized in that, The data gain assessment step S3 includes calculating one or more of the following: market potential index, technology scalability, and risk-adjusted return.
5. The method for assessing the value of data assets based on clustering, fusion efficiency, and gain according to claim 1, characterized in that, The dynamic weighting model in step S4 dynamically adjusts the weighting of chain value, integration value, and gain value based on industry benchmarks and / or corporate strategy.
6. A system for assessing the value of data assets based on clustering, fusion efficiency, and gains, characterized in that: Includes a processor and a memory, wherein the memory stores a program or instructions, and when the program or instructions are executed by the processor, they perform the following steps: S1. Data aggregation chain processing steps: Obtain raw data and external data sources, construct a data asset chain, assign quality weights to the data asset chain, and output the aggregation chain value; S2. Data integration and efficiency analysis steps: Receive the cluster value, calculate the efficiency conversion value of the data in the preset business process, and output the integration value; S3. Data gain assessment step: Receive the cluster value and the fusion value, predict the potential value-added space of data assets, and output the gain value; S4. Multidimensional value comprehensive evaluation steps: Receive the cluster value, integration value and gain value, integrate the three through a dynamic weight model, calculate and output the comprehensive value score; S5. Report generation steps: Based on the comprehensive value score, generate a visual evaluation report that includes value composition analysis and optimization suggestions.
7. The system for assessing the value of data assets based on clustering, fusion efficiency, and gains according to claim 6, characterized in that, When the program or instructions are executed by the processor, the following steps are performed: The data aggregation chain processing steps in step S1 include: constructing a data lineage map, mapping application scenarios, and assigning quality weights based on data integrity and link depth.
8. The system for assessing the value of data assets based on clustering, fusion efficiency, and gains according to claim 6, characterized in that, When the program or instructions are executed by the processor, the following steps are performed: The data fusion analysis step S2 includes one or more of the following: quantifying decision support, cost saving rate, and efficiency improvement value.
9. The system for assessing the value of data assets based on clustering, fusion efficiency, and gains according to claim 6, characterized in that, When the program or instructions are executed by the processor, the following steps are performed: The data gain assessment step S3 includes calculating one or more of the following: market potential index, technology scalability, and risk-adjusted return.
10. The system for assessing the value of data assets based on clustering, fusion efficiency, and gains according to claim 6, characterized in that, When the program or instructions are executed by the processor, the following steps are performed: The dynamic weighting model in step S4 dynamically adjusts the weighting of chain value, integration value, and gain value based on industry benchmarks and / or corporate strategy.