Data asset value evaluation analysis method and system

Abstract

The invention discloses a data asset value evaluation analysis method and system, particularly relates to the field of data asset value evaluation, and evaluates the scene adaptation degree of data assets by analyzing the scene adaptation of the data assets. Evaluating the scene availability degree of the data assets by analyzing the availability of the data assets; judging whether the data assets meet value evaluation conditions or not according to the scene adaptation degree and the availability degree of the data assets; dividing an evaluation analysis stage of the data assets into an innovation discovery stage and a strategic application stage according to value characteristics of the data assets, and analyzing innovation values and decision values of the data assets in the innovation discovery stage and the strategic application stage; the innovation value of the data assets in the innovation discovery stage and the decision value of the data assets in the strategic application stage are comprehensively analyzed, the value evaluation index of the data assets is obtained, the accuracy and adaptability of evaluation are enhanced, and the value evaluation of the data assets is more detailed and comprehensive through phased analysis.

Description

Technical Field

[0001] The present invention relates to the field of data asset value assessment, and more specifically, to a data asset value assessment and analysis method and system. Background Art

[0002] Data assets refer to data generated, stored, analyzed, and used by information technology in the management of an enterprise or organization, which can bring potential economic value to the enterprise or organization. With the development of big data technology, how to evaluate and quantify the value of data assets has become an important issue facing many enterprises or organizations. Traditional data asset evaluation methods often ignore the adaptability and availability of data assets in specific scenarios, resulting in one-sided evaluation results, unable to accurately reflect the comprehensive value of data assets, and lack of comprehensive consideration of data assets. Traditional data asset evaluation methods do not take into account the performance of data assets in different life cycle stages, such as the innovation discovery stage and the strategic application stage. They cannot effectively distinguish between the innovation potential of data and the actual decision-making support capabilities, and cannot accurately reflect the true value of data assets. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a data asset value assessment and analysis method to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The data asset value assessment and analysis method includes the following steps:

[0006] Step S1: Analyze the scenario adaptability of data assets, and evaluate the scenario adaptability of data assets according to the scenario adaptability of data assets;

[0007] Step S2: Analyze the availability of data assets and evaluate the availability of data assets based on the availability of data assets;

[0008] Step S3: Determine whether the data asset value assessment and analysis conditions are met based on the scenario adaptability of the data asset and the availability of the data asset;

[0009] Step S4: When the data asset value assessment and analysis conditions are met, the data asset assessment and analysis phase is divided into the innovation discovery phase and the strategic application phase according to the value characteristics of the data asset; the innovation value of the data asset in the innovation discovery phase is analyzed; and the decision-making value of the data asset in the strategic application phase is analyzed;

[0010] Step S5: Comprehensively analyze the innovation value of data assets in the innovation discovery stage and the decision-making value of data assets in the strategic application stage to evaluate and analyze the value of data assets.

[0011] In a preferred embodiment, in step S1: the scenario adaptability dimension is defined, and the scenario adaptability dimension includes scenario dependency and adaptation flexibility; scenario dependency refers to the dependency of data assets in a specific scenario; adaptation flexibility refers to the flexibility of data assets between different scenarios; the scenario adaptability of data assets is analyzed according to scenario dependency and adaptation flexibility:

[0012]

[0013] Among them, A s is the scene adaptability index; n is the number of scene adaptability dimensions; ω i is the weight coefficient of the i-th scenario adaptability dimension; f(i) is the evaluation function of the data asset in the i-th scenario adaptability dimension.

[0014] In a preferred embodiment, in step S2: the real-time dimension of data assets is defined, and the real-time dimension includes real-time response time, update frequency and data transmission delay; real-time response time refers to the time interval from the generation of data assets to their use; update frequency refers to the update speed of data assets; data transmission delay refers to the delay time of data in the process of network transmission, and a data asset real-time evaluation model is constructed based on the real-time response time, update frequency and data transmission delay:

[0015]

[0016] Among them, DTS is the timeliness score of the data asset, representing the real-time performance of the data asset; UF is the update frequency of the data asset; RT is the average response time of the data asset; and LT is the transmission delay of the data asset.

[0017] In a preferred embodiment, in step S3: a scene adaptability index threshold and a time efficiency score threshold are preset, and the scene adaptability index is compared with the scene adaptability index threshold:

[0018] When the scenario adaptability index is greater than the scenario adaptability index threshold, it means that the adaptability of the data asset in the target scenario meets the expected requirements;

[0019] Compare the timeliness score with the timeliness score threshold: When the timeliness score is greater than the timeliness score threshold, it means that the real-time performance of the data asset meets the expected requirements;

[0020] When the scenario adaptability index is greater than the scenario adaptability index threshold, and the timeliness score is greater than the timeliness score threshold, it means that the data asset meets the value assessment analysis conditions and enters the data asset value assessment analysis stage.

[0021] In a preferred embodiment, in step S4: defining the evaluation dimensions of the innovative value of data assets, including breakthrough potential and technological integration; constructing an innovative value evaluation model, and quantifying the performance of data assets in the breakthrough potential and technological integration dimensions into an innovative value score:

[0022]

[0023] Among them, I v is the innovation value score, which represents the potential innovation capability of data assets in the innovation discovery stage; E c is the breakthrough potential score, which reflects the potential of data assets in promoting technological breakthroughs and market changes; F c is the technology integration score, which evaluates the performance of data assets in the technology ecosystem by analyzing the compatibility and integration capabilities of data assets with existing technologies, platforms or systems; m is the number of application scenarios and technology fields covered by the data assets;

[0024] The decision value index is introduced to comprehensively evaluate the decision value of data assets in the strategic application stage:

[0025] Among them, DVI is the decision value index, which represents the decision contribution of data assets in the entire evaluation cycle; R b (t) is the business relevance function, which indicates the relevance of data assets to business objectives at time point t; G l (t) is the information gain function, which represents the information increment provided by the data asset for the decision-making process at time point t; T is the total time length of the decision evaluation cycle, which represents the time period covered from the beginning to the end of the decision evaluation.

[0026] In a preferred embodiment, in step S5: the innovation value score corresponding to the innovation value of the data asset in the innovation discovery stage and the decision value index corresponding to the decision value of the data asset in the strategic application stage are normalized, and the value evaluation index of the data asset is calculated by respectively presetting proportional coefficients for the normalized innovation value score and decision value index:

[0027] DVE=α*I v +β*DVI;

[0028] Among them, DVE is the value evaluation index of data assets, which indicates the comprehensive value of data assets in the overall value evaluation; v It is the innovation value score corresponding to the innovation value of data assets in the innovation discovery stage; DVI is the decision value index corresponding to the decision value of data assets in the strategic application stage; α and β are the preset proportional coefficients of the innovation value score and the decision value index respectively;

[0029] A first value assessment threshold and a second value assessment threshold are preset, and the second value assessment threshold is greater than the first value assessment threshold, and the value assessment index is compared with the first value assessment threshold and the second value assessment threshold respectively:

[0030] When the value assessment index is greater than or equal to the second value assessment threshold, it is called a high-value data asset;

[0031] When the value assessment index is less than the second value assessment threshold, and the value assessment index is greater than or equal to the first value assessment threshold, it is called a medium-value data asset;

[0032] When the value assessment index is less than the first value assessment threshold, it is called a low-value data asset.

[0033] Technical effects and advantages of the method of the present invention:

[0034] 1. By analyzing the scenario adaptability and availability of data assets, we can more accurately capture the performance of data assets in different application scenarios and ensure the multi-dimensional adaptability of data assets. We not only consider the flexibility and adaptability of data in specific scenarios, but also measure its effectiveness in actual applications. Through the combined analysis of the scenario adaptability and availability of data assets, we can ensure that data assets will enter the further value assessment stage only when both adaptability and availability meet the conditions. The combined analysis process of the scenario adaptability and availability of data assets improves the overall assessment accuracy of data assets, reduces resource waste, and facilitates subsequent innovation value and decision-making value analysis.

[0035] 2. By introducing the division of the innovation discovery stage and the strategic application stage, a dual-track parallel analysis of data asset value assessment is achieved; the division of the innovation discovery stage and the strategic application stage not only ensures the in-depth exploration of the data innovation potential, but also comprehensively considers the actual application effect of data in decision support; through comprehensive analysis of innovation value and decision value, enterprises or organizations can comprehensively and accurately evaluate the overall value of data assets to ensure the best choice in resource allocation and business decision-making; the flexibility of the dual-track parallel analysis of data asset value assessment allows dynamic adjustment of assessment weights according to the strategic focus of the enterprise or organization, further improving the practicality and operability of the assessment results, and improving the efficiency and accuracy of data asset assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a schematic diagram of the data asset value assessment and analysis method of the present invention;

[0037] Figure 2 This is a schematic diagram of the structure of the data asset value assessment and analysis system of the present invention; DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Reference Figure 1 .

[0040] The embodiment provides a data asset value assessment and analysis method of the present invention, which includes the following steps:

[0041] Step S1: Analyze the scenario adaptability of data assets, and evaluate the scenario adaptability of data assets according to the scenario adaptability of data assets;

[0042] Step S2: Analyze the availability of data assets and evaluate the availability of data assets based on the availability of data assets;

[0043] Step S3: Determine whether the data asset value assessment and analysis conditions are met based on the scenario adaptability of the data asset and the availability of the data asset;

[0044] Step S4: When the data asset value assessment and analysis conditions are met, the data asset assessment and analysis phase is divided into the innovation discovery phase and the strategic application phase according to the value characteristics of the data asset; the innovation value of the data asset in the innovation discovery phase is analyzed; and the decision-making value of the data asset in the strategic application phase is analyzed;

[0045] Step S5: Comprehensively analyze the innovation value of data assets in the innovation discovery stage and the decision-making value of data assets in the strategic application stage to evaluate and analyze the value of data assets.

[0046] In step S1: analyzing the scenario adaptability of data assets and evaluating the degree of adaptability based on the adaptability of data assets can help understand the flexibility and effectiveness of data in current and future usage scenarios; scenario adaptability evaluation can help determine whether data assets can adapt to different application requirements, whether they have sufficient scalability, and whether data assets can evolve and continue to play their value in a dynamically changing environment; analysis of the scenario adaptability of data assets can better determine the value of data assets in practical applications and avoid waste of resources or decision-making errors due to limitations of data assets; adaptability analysis can also reveal whether data has cross-domain usage potential in a variety of application scenarios, thereby improving data reusability; adaptability analysis can also help identify potential adaptability bottlenecks in advance and improve adaptability through data supplementation or data architecture optimization.

[0047] Analyze the scenario adaptability of data assets and evaluate the scenario adaptability of data assets based on the scenario adaptability of data assets, specifically:

[0048] Define the scenario adaptability dimension, which includes scenario dependency and adaptation flexibility. Scenario dependency refers to the dependency of data assets in a specific scenario, for example, whether the data depends on a specific hardware, software or actual environment. Adaptation flexibility refers to the flexibility of data assets between different scenarios, for example, whether the data can be used directly in different hardware, software or actual environments. Analyze the scenario adaptability of data assets based on scenario dependency and adaptation flexibility:

[0049]

[0050] Among them, A s is the scene adaptability index; n is the number of scene adaptability dimensions; ω i is the weight coefficient of the adaptability dimension of the i-th scenario. The weight coefficient can be determined through expert opinions, historical data analysis, market research, etc. Different dimensions are usually scored according to the importance of specific application scenarios, and the weight coefficient is determined by the scoring results; f(i) is the evaluation function of the data asset in the i-th scenario adaptability dimension.

[0051] The higher the scenario adaptability index is, the stronger the adaptability of the digital asset is in a specific application scenario, and the higher the degree of scenario adaptability of the digital asset is. The higher the scenario adaptability index is, the more stably the digital asset can meet the needs in a specific application scenario and the better its compatibility is. Digital assets with higher scenario adaptability index are usually able to play a role in multiple application environments, have wide adaptability, and can even be adjusted or expanded as the scenario changes.

[0052] In the scenario adaptability analysis of data assets in the intelligent transportation system, it is assumed that the scenarios include real-time analysis of traffic flow, accident prediction and route optimization; the scenario adaptability index is calculated for each scenario through the scenario adaptability dimensions (scenario dependency, adaptation flexibility); according to the scenario adaptability index of each scenario, it is judged in which scenario the data asset is more suitable for value assessment analysis. For example, the scenario adaptability index of data assets in the real-time analysis of traffic flow is the highest, that is, the data asset is more suitable for value assessment analysis in the real-time analysis of traffic flow scenario.

[0053] In step S2: the availability of data assets is analyzed, and the availability of data assets is evaluated according to the availability of data assets, specifically:

[0054] The availability of data assets is mainly reflected by the real-time performance of data assets. Analyzing the real-time performance of data assets and evaluating their real-time degree based on their real-time performance are important in determining whether the conditions for data asset value assessment and analysis are met. The real-time performance of data assets reflects the delays, response time, and update frequency in data collection, transmission, processing, and application. Assessing the real-time degree of data assets can help determine whether the data is timely in current or future scenarios, thereby ensuring that the value assessment of the data is more comprehensive and accurate. Real-time analysis of data assets helps identify potential timeliness issues, provides directions for improvement, and ensures that data assets can respond to dynamic needs in a timely manner. It helps determine whether data can enter the data asset assessment and analysis stage.

[0055] Define the real-time dimension of data assets, which includes real-time response time, update frequency, and data transmission delay. Real-time response time refers to the time interval from the generation of data assets to their use. Update frequency refers to the update speed of data assets. Data transmission delay refers to the delay time of data during network transmission. A data asset real-time evaluation model is constructed based on real-time response time, update frequency, and data transmission delay:

[0056]

[0057] Among them, DTS is the timeliness score of the data asset, representing the real-time performance of the data asset; UF is the update frequency of the data asset; RT is the average response time of the data asset; and LT is the transmission delay of the data asset.

[0058] In the process of processing and analyzing data assets, programming languages ​​such as Python and related data analysis libraries can be used to implement the calculation and analysis of the above-mentioned evaluation model. Combining neural network algorithms and EDAAM models can further optimize the scenario-adaptive evaluation model; neural network algorithms can help automatically identify hidden patterns in data asset characteristics, further improving the accuracy and robustness of the evaluation model; by introducing the EDAAM model, the multi-dimensional trade-off problem in the evaluation process can be effectively solved to ensure a reasonable balance between the evaluation results in different dimensions.

[0059] The higher the timeliness score of a data asset, the more real-time the data asset is, that is, the data can be generated, processed and transmitted in a shorter time. The high real-time nature of a data asset means that the data can be applied to various real-time decision-making and operation scenarios more promptly. Therefore, data assets with higher timeliness scores have higher availability in these scenarios, that is, the data assets have a higher degree of availability. Data assets with high timeliness scores also usually have lower response times and transmission delays, enabling data assets to better support business processes that require quick feedback, further improving the availability of data assets.

[0060] In step S3: according to the adaptability and availability of the data assets, it is determined whether the data asset value assessment and analysis conditions are met, specifically:

[0061] The scenario adaptability index threshold and timeliness score threshold are preset. The scenario adaptability index threshold and timeliness score threshold are set based on the specific needs of the application scenario, historical data analysis and industry standards.

[0062] Compare the scene suitability index to the scene suitability index threshold:

[0063] When the scenario adaptability index is greater than the scenario adaptability index threshold, it means that the adaptability of the data asset in the target scenario meets the expected requirements, has low scenario dependence and high adaptation flexibility, and can enter the next stage of data asset value assessment and analysis.

[0064] When the scenario adaptability index is less than or equal to the scenario adaptability index threshold, it means that the data asset is insufficiently adaptable in the target scenario, has high scenario dependence and low adaptation flexibility, and cannot enter the next stage of data asset value assessment and analysis.

[0065] Compare the timeliness score to the timeliness score threshold:

[0066] When the timeliness score is greater than the timeliness score threshold, it means that the real-time performance of the data asset meets the expected requirements, that is, the data asset has a high degree of availability, which means that the real-time requirements of the data asset value assessment and analysis are met.

[0067] When the timeliness score is less than or equal to the timeliness score threshold, it means that the real-time performance of the data asset is insufficient, that is, the availability of the data asset is low and it is impossible to enter the next stage of data asset value assessment and analysis.

[0068] When the scenario adaptability index is greater than the scenario adaptability index threshold and the timeliness score is greater than the timeliness score threshold, it means that the data asset meets the value assessment analysis conditions and enters the data asset value assessment analysis stage.

[0069] In step S4: the evaluation and analysis phase of data assets is divided into the innovation discovery phase and the strategic application phase according to the value characteristics of data assets, specifically:

[0070] The evaluation and analysis of data assets will be divided into two stages according to the value characteristics of data assets: the innovation discovery stage and the strategic application stage. The value characteristics of data assets are reflected through innovation potential and decision support potential. Multi-dimensional evaluation and analysis will be conducted on the innovation potential and decision support potential respectively, and based on the analysis results, data assets will be divided into the innovation discovery stage or the strategic application stage for value evaluation and analysis.

[0071] Define the value characteristics of data assets, which include innovation potential and decision-making support potential. Innovation potential is a key factor in determining whether a data asset enters the innovation discovery stage, including the novelty of the data asset. Use similarity analysis tools (such as cluster analysis or anomaly detection) to evaluate the differences between data assets and existing data sets and calculate the novelty score of the data asset. The larger the novelty score, the greater the novelty of the data asset. Decision-making support potential is a key factor in determining whether a data asset enters the strategic application stage, including the reliability of the data asset. Use data quality detection tools to evaluate the reliability score of the data asset. The larger the reliability score of the data asset, the higher the reliability of the data asset.

[0072] When the novelty score of a data asset is greater than or equal to the set novelty score threshold, the data asset enters the innovation discovery stage. The evaluation focus of the innovation discovery stage is to analyze the innovation value of the data asset. When the reliability score of a data asset is greater than or equal to the set reliability score threshold, the data asset will enter the strategic application stage. The evaluation focus of the strategic application stage is to analyze the decision-making value of the data asset.

[0073] Analyze the innovative value of data assets in the innovation discovery phase, specifically:

[0074] In the innovation discovery stage, the core value of data assets lies in their potential to promote new technology, new product development and market innovation. A comprehensive analysis of the innovation value of data assets will help determine the specific application direction of data assets in the innovation discovery stage, as well as their contribution to future technology and market development. Innovation value refers to whether data assets are innovative and can bring breakthroughs to new technologies, new products or new markets, such as whether data can reveal new trends, support new research directions, or provide new business opportunities.

[0075] Define the evaluation dimensions of the innovative value of data assets, including breakthrough potential and technological integration; breakthrough potential refers to the potential of data assets in promoting technological breakthroughs, product innovation or market changes, and evaluates whether data assets can bring significant innovation effects and promote changes in industries or fields; technological integration refers to the ability of data assets to integrate with existing technologies, platforms or systems, and evaluates the performance of the innovative value of digital assets in the technology ecosystem; construct an innovation value evaluation model to quantify the performance of data assets in the breakthrough potential and technological integration dimensions into an innovation value score:

[0076]

[0077] Among them, I v is the innovation value score, which represents the potential innovation capability of data assets in the innovation discovery stage; E cThe breakthrough potential score reflects the potential of data assets in promoting technological breakthroughs and market changes. Through market analysis, technology trend research and expert review, the potential innovation breakthrough potential of data assets is scored. The larger the breakthrough potential score, the stronger the breakthrough potential. c is the technology integration score, which evaluates the performance of data assets in the technology ecosystem by analyzing the compatibility and integration capabilities of data assets with existing technologies, platforms or systems. The larger the technology integration score, the stronger the technology integration of the data asset. m is the number of application scenarios and technology fields covered by the data asset.

[0078] The higher the innovation value score of a data asset, the higher the innovation value of the data asset in the innovation discovery stage, which means that the data asset has stronger innovation potential in the innovation discovery stage and it is worth it for the company to invest more resources in development and exploration; such data assets may bring significant technological advantages or market opportunities to the company in the future and will be given priority for innovation projects or new technology development.

[0079] Analyze the decision-making value of data assets in the strategic application stage, specifically:

[0080] Decision value refers to whether data assets can provide support for the strategic decision-making of an enterprise or organization, help optimize resource allocation, improve operational efficiency or enhance market competitiveness. For example, whether data can help decision makers make more accurate market forecasts, risk assessments or strategic plans. In the strategic application stage, the decision value of data assets is mainly reflected in information gain and business relevance.

[0081] The decision value index is introduced to comprehensively evaluate the decision value of data assets in the strategic application stage:

[0082]

[0083] Among them, DVI is the decision value index, which represents the decision contribution of data assets in the entire evaluation cycle; R b (t) is the business relevance function, which indicates the relevance of data assets to business objectives at time point t; G l (t) is the information gain function, which represents the information increment provided by the data asset for the decision-making process at time point t; T is the total time length of the decision evaluation cycle, which represents the time period covered from the beginning to the end of the decision evaluation.

[0084] The larger the decision value index of a data asset, the higher the business relevance, which means that the data asset is highly relevant to business goals and can effectively support decision-making; the larger the decision value index of a data asset, the greater the information gain, which means that the data asset provides important incremental information for the decision-making process and improves the quality of decision-making; the larger the decision value index of a data asset, the stronger the data asset's ability to support the strategic decision-making of an enterprise or organization, and the higher the decision value of the data asset in the strategic application stage.

[0085] In step S5: the innovation value of the data assets in the innovation discovery stage and the decision-making value of the data assets in the strategic application stage are comprehensively analyzed to achieve value assessment analysis of the data assets, specifically:

[0086] The innovation value score corresponding to the innovation value of data assets in the innovation discovery stage and the decision value index corresponding to the decision value of data assets in the strategic application stage are normalized, and the value assessment index of data assets is calculated by respectively presetting the proportional coefficients of the normalized innovation value score and decision value index:

[0087] DVE=α*I v +β*DVI;

[0088] Among them, DVE is the value evaluation index of data assets, which indicates the comprehensive value of data assets in the overall value evaluation; v It is the innovation value score corresponding to the innovation value of data assets in the innovation discovery stage; DVI is the decision value index corresponding to the decision value of data assets in the strategic application stage; α and β are the preset proportional coefficients of the innovation value score and the decision value index respectively.

[0089] A first value assessment threshold and a second value assessment threshold are preset, and the second value assessment threshold is greater than the first value assessment threshold, and the value assessment index is compared with the first value assessment threshold and the second value assessment threshold respectively:

[0090] When the value assessment index is greater than or equal to the second value assessment threshold, it indicates that the data asset has a higher comprehensive value and is called a high-value data asset. High-value data assets perform well in the innovation discovery stage and strategic application stage, have higher innovation value and decision-making value, and are given priority in allocating resources for development and application in key business decisions and innovation projects, maximizing the value potential of data assets.

[0091] When the value assessment index is less than the second value assessment threshold and greater than or equal to the first value assessment threshold, it indicates that the data asset has a certain value and is called a medium-value data asset. Medium-value data assets need to be further optimized or combined with other resources to maximize their value. Enterprises or organizations need to selectively develop and apply them based on specific business scenarios and needs.

[0092] When the value assessment index is less than the first value assessment threshold, it means that the comprehensive value of the data asset is low, which is called a low-value data asset.

[0093] The data asset value evaluation and analysis system disclosed in the present invention comprises: a scenario adaptability evaluation module, an availability evaluation module, a condition judgment module, a value analysis module, and a value evaluation module;

[0094] Scenario adaptability assessment module: Analyzes the scenario adaptability of data assets and assesses the scenario adaptability of data assets based on the scenario adaptability of data assets;

[0095] Availability assessment module: Analyzes the availability of data assets and assesses the availability of data assets based on the availability of data assets;

[0096] Condition judgment module: judges whether the data asset value assessment and analysis conditions are met based on the scenario adaptability and availability of the data asset;

[0097] Value analysis module: When the data asset value assessment and analysis conditions are met, the data asset assessment and analysis phase is divided into the innovation discovery phase and the strategic application phase according to the value characteristics of the data asset; the innovation value of the data asset in the innovation discovery phase is analyzed; and the decision-making value of the data asset in the strategic application phase is analyzed;

[0098] Value assessment module: Comprehensively analyze the innovation value of data assets in the innovation discovery stage and the decision-making value of data assets in the strategic application stage to evaluate and analyze the value of data assets.

[0099] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0100] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0102] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0103] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0105] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0106] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0107] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data asset value assessment and analysis method, characterized in that: The following steps are involved: Step S1: Analyze the scenario adaptability of data assets, and evaluate the scenario adaptability of data assets according to the scenario adaptability of data assets; Step S2: Analyze the availability of data assets and evaluate the availability of data assets based on the availability of data assets; Step S3: Determine whether the data asset value assessment and analysis conditions are met based on the scenario adaptability of the data asset and the availability of the data asset; Step S4: When the data asset value assessment and analysis conditions are met, the data asset assessment and analysis phase is divided into the innovation discovery phase and the strategic application phase according to the value characteristics of the data asset; the innovation value of the data asset in the innovation discovery phase is analyzed; and the decision-making value of the data asset in the strategic application phase is analyzed; Step S5: Comprehensively analyze the innovation value of data assets in the innovation discovery stage and the decision-making value of data assets in the strategic application stage to evaluate and analyze the value of data assets.

2. A data asset value assessment and analysis method according to claim 1, characterized in that: In step S1: Define the scenario adaptability dimension, which includes scenario dependency and adaptation flexibility. Scenario dependency refers to the dependency of data assets in a specific scenario. Adaptation flexibility refers to the flexibility of data assets between different scenarios. Analyze the scenario adaptability of data assets based on scenario dependency and adaptation flexibility: Among them, A s is the scene adaptability index; n is the number of scene adaptability dimensions; ω i is the weight coefficient of the i-th scenario adaptability dimension; f(i) is the evaluation function of the data asset in the i-th scenario adaptability dimension.

3. A data asset value assessment and analysis method according to claim 2, characterized in that: In step S2: Define the real-time dimension of data assets, which includes real-time response time, update frequency, and data transmission delay. Real-time response time refers to the time interval from the generation of data assets to their use. Update frequency refers to the update speed of data assets. Data transmission delay refers to the delay time of data during network transmission. A data asset real-time evaluation model is constructed based on real-time response time, update frequency, and data transmission delay: Among them, DTS is the timeliness score of data assets, representing the real-time performance of data assets; UF is the update frequency of data assets; RT is the average response time of data assets; LT is the transmission delay of data assets.

4. A data asset value assessment and analysis method according to claim 3, characterized in that: In step S3: The scene adaptability index threshold and timeliness score threshold are preset, and the scene adaptability index is compared with the scene adaptability index threshold: When the scenario adaptability index is greater than the scenario adaptability index threshold, it means that the adaptability of the data asset in the target scenario meets the expected requirements; Compare the timeliness score with the timeliness score threshold: When the timeliness score is greater than the timeliness score threshold, it means that the real-time performance of the data asset meets the expected requirements; When the scenario adaptability index is greater than the scenario adaptability index threshold, and the timeliness score is greater than the timeliness score threshold, it means that the data asset meets the value assessment analysis conditions and enters the data asset value assessment analysis stage.

5. A data asset value assessment and analysis method according to claim 4, characterized in that: In step S4: Define the evaluation dimensions of the innovative value of data assets, including breakthrough potential and technological integration; build an innovative value evaluation model to quantify the performance of data assets in the breakthrough potential and technological integration dimensions into an innovative value score: Among them, Iv is the innovation value score, which represents the potential innovation ability of data assets in the innovation discovery stage; Ec is the breakthrough potential score, which reflects the potential of data assets in promoting technological breakthroughs and market changes; Fc is the technology integration score, which evaluates the performance of data assets in the technology ecosystem by analyzing the compatibility and integration capabilities of data assets with existing technologies, platforms or systems; m is the number of application scenarios and technology fields covered by data assets; The decision value index is introduced to comprehensively evaluate the decision value of data assets in the strategic application stage: Among them, DVI is the decision value index, which represents the decision contribution of data assets in the entire evaluation cycle; R b (t) is the business relevance function, which indicates the relevance of data assets to business objectives at time point t; Gl(t) is the information gain function, which indicates the information increment provided by data assets for the decision-making process at time point t; T is the total time length of the decision evaluation cycle, which indicates the time period covered from the beginning to the end of the decision evaluation.

6. A data asset value assessment and analysis method according to claim 5, characterized in that: In step S5: The innovation value score corresponding to the innovation value of data assets in the innovation discovery stage and the decision value index corresponding to the decision value of data assets in the strategic application stage are normalized, and the value assessment index of data assets is calculated by respectively presetting the proportional coefficients of the normalized innovation value score and decision value index: DVE=α*I v +β*DVI; Among them, DVE is the value evaluation index of data assets, which indicates the comprehensive value of data assets in the overall value evaluation; Iv is the innovation value score corresponding to the innovation value of data assets in the innovation discovery stage; DVI is the decision value index corresponding to the decision value of data assets in the strategic application stage; α and β are the preset proportional coefficients of the innovation value score and the decision value index respectively; A first value assessment threshold and a second value assessment threshold are preset, and the second value assessment threshold is greater than the first value assessment threshold, and the value assessment index is compared with the first value assessment threshold and the second value assessment threshold respectively: When the value assessment index is greater than or equal to the second value assessment threshold, it is called a high-value data asset; When the value assessment index is less than the second value assessment threshold, and the value assessment index is greater than or equal to the first value assessment threshold, it is called a medium-value data asset; When the value assessment index is less than the first value assessment threshold, it is called a low-value data asset.

7. A data asset value assessment and analysis system, applied to a data asset value assessment and analysis method according to any one of claims 1 to 6, characterized in that: include: Scenario adaptability assessment module: Analyzes the scenario adaptability of data assets and assesses the scenario adaptability of data assets based on the scenario adaptability of data assets; Availability assessment module: Analyzes the availability of data assets and assesses the availability of data assets based on the availability of data assets; Condition judgment module: judges whether the data asset value assessment and analysis conditions are met based on the scenario adaptability and availability of the data asset; Value analysis module: When the data asset value assessment and analysis conditions are met, the data asset assessment and analysis phase is divided into the innovation discovery phase and the strategic application phase according to the value characteristics of the data asset; the innovation value of the data asset in the innovation discovery phase is analyzed; and the decision-making value of the data asset in the strategic application phase is analyzed; Value assessment module: Comprehensively analyze the innovation value of data assets in the innovation discovery stage and the decision-making value of data assets in the strategic application stage to achieve evaluation and analysis of the value of data assets.

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