A comprehensive data asset value assessment device

Abstract

The present invention provides a comprehensive data asset value assessment device, which belongs to the field of data assessment technology and includes: a data asset inventory module for automatically scanning the enterprise's data system, identifying and classifying various types of data assets; a data asset compliance review module for reviewing the compliance of various types of data assets under the indicator dimension assessment system; a data asset measurement module for performing multi-dimensional quantitative analysis on various types of data assets and determining the main value attributes of various types of data assets; an assessment module for assessing data assets separately according to existing assessment methods; an assessment report module for determining the data interaction status of various types of data assets in the corresponding enterprise based on compliance and main value attributes, and generating a data asset assessment report based on the assessment results. Providing comprehensive and accurate data asset assessment services for enterprises is conducive to the efficient management and value maximization of data assets.

Description

Technical Field

[0001] The present invention relates to the field of data evaluation technology, and in particular to a comprehensive data asset value evaluation device. Background Art

[0002] With the rapid development of information technology, data has become one of a company's most important intangible assets. By collecting, storing, processing, and analyzing data, companies can gain valuable information and insights, thereby optimizing decision-making, improving operational efficiency, and creating new business models and revenue streams. Therefore, effective management and valuation of data assets have become crucial for companies to enhance their competitiveness and achieve sustainable development.

[0003] Despite the growing importance of data assets, assessing their value presents numerous challenges. First, data types are diverse, including structured and unstructured data, each with its own distinct value attributes and valuation methods. Second, data value is difficult to quantify. The value of data assets is often reflected in the potential benefits and competitive advantages they can bring to an enterprise, which are often difficult to directly quantify and measure. Furthermore, data asset valuation is subject to constraints and restrictions regarding laws and regulations, privacy protection, and data security, further complicating and challenging the process.

[0004] Traditional asset valuation methods, such as the cost approach, income approach, and market approach, can assess the value of data assets to a certain extent, but they often suffer from cumbersome processes and inaccurate results. The cost approach focuses primarily on the investment costs of data assets, but ignores the potential benefits and competitive advantages they bring. While the income approach considers future returns from data assets, it is difficult to accurately predict and quantify. Market approaches require finding similar data asset transaction cases for comparison, but in the data asset market, similar transaction cases are often difficult to obtain or compare.

[0005] Currently, there are several data asset valuation solutions on the market, but they often suffer from the following shortcomings: First, the valuation method is single and fails to fully reflect the value attributes and characteristics of data assets; second, the valuation process is cumbersome, requiring significant time and manpower; and third, the valuation results are inaccurate and difficult to gain widespread recognition from businesses and the market. Therefore, it is particularly important to develop a device that can comprehensively and accurately assess the value of data assets. Summary of the Invention

[0006] The present invention provides a comprehensive data asset value assessment device, which aims to address the limitations and shortcomings of existing data asset assessment methods, provide enterprises with comprehensive and accurate data asset assessment services, and contribute to the efficient management and value maximization of data assets.

[0007] The present invention provides a comprehensive data asset value assessment device, such as Figure 1 Shown, including:

[0008] Data asset inventory module, used to automatically scan the enterprise's data system, identify and classify various types of data assets;

[0009] The data asset compliance review module is used to review the compliance of various types of data assets under the indicator dimension evaluation system;

[0010] The data asset measurement module is used to conduct multi-dimensional quantitative analysis of various types of data assets and determine the main value attributes of various types of data assets;

[0011] The evaluation module is used to evaluate the cost investment, future benefits and market transaction value of data assets according to existing evaluation methods;

[0012] The assessment report module is used to determine the data interaction status of various types of data assets in the corresponding enterprise based on compliance and main value attributes, and generate a data asset assessment report based on the assessment results.

[0013] Preferably, the data asset compliance review module includes:

[0014] A capture unit, configured to capture the security level and user level of each data storage location based on the data storage locations of the enterprise involved in the automatic scanning process;

[0015] a feature determination unit, configured to determine the privacy security feature of the data storage location according to the parameter settings of the security level and the user level;

[0016] A mining unit is used to mine the type source of each data type in the data storage location, the data processing method in the process from the type source to the data storage location, the data management method after the data assets of the corresponding data type reach the data storage location, and the data usage purpose of the data assets of the corresponding data type;

[0017] The tracing unit is used to trace the source of the data type, data processing method, data management method and data usage purpose according to the privacy security characteristics and legal security characteristics, and to build a review matrix based on the indicator dimension evaluation system. ,in, 、 、 、 Respectively represent the evaluation vectors of the j-th data asset under the data storage location based on type source, data processing method, data management method, and data usage purpose;

[0018] Among them, the indicator dimension evaluation system is related to the quality dimension, value dimension, risk dimension and management dimension.

[0019] Preferably, the data asset compliance review module further includes:

[0020] The matrix analysis unit is used to analyze the review matrix and set compliance for data assets under corresponding data types under corresponding data storage locations.

[0021] Preferably, the data asset measurement module includes:

[0022] The quantitative assessment unit is used to quantitatively assess data assets based on their cost, allocation, impairment / increase in value, and future income, and to obtain an assessment coefficient based on each dimension.

[0023] A screening unit, configured to screen a maximum coefficient from the evaluation coefficients under the corresponding data asset, and obtain all influencing attributes of the maximum coefficient and the dimension corresponding to the maximum coefficient;

[0024] The attribute determination unit is used to screen the attribute with the greatest influence from all influencing attributes and regard it as the main value attribute.

[0025] Preferably, the existing evaluation methods include: cost approach, income approach and market approach.

[0026] Preferably, the assessment report module includes:

[0027] A table construction unit, configured to construct an enterprise-based data storage mapping table based on the compliance and main value attributes;

[0028] A graph construction unit is configured to construct a first asset interaction graph for each data storage location based on asset interactions between data assets of different data types in each data storage location, and simultaneously construct a second asset interaction graph for the enterprise based on asset interactions between each data storage location;

[0029] The situation determination unit is used to determine the data interaction situation of each type of asset data based on the data storage mapping table, the second asset interaction diagram and all the first asset interaction diagrams.

[0030] Preferably, the graph construction unit includes:

[0031] The first statistics block is used to collect statistics on interaction information of the same data storage location based on each enterprise business. The interaction information includes the interaction trend between any two data assets and the value impact between the any two data assets. The value impact includes value improvement impact and value loss impact.

[0032] The first building block is used to determine the direction vector diagram of the data assets under each data type based on all the interaction information, and establish the vector pointing between the corresponding data type and each remaining type under the same data storage location, thereby constructing a first asset interaction diagram;

[0033] The second statistics block is used to count the data storage locations involved in each enterprise business, the data interaction trends between the locations, the data types of the interactions, and the data interaction volume based on the data type of each interaction;

[0034] The second building block is used to filter high-frequency interaction volumes from all data interaction volumes under each interaction data type involved in the same data interaction trend, and construct a second asset interaction graph.

[0035] Preferably, the situation determination unit includes:

[0036] An initial block is used to extract a first interaction result and a second interaction result based on the same data type from the first asset interaction graph and the second asset interaction graph, respectively, and input them into the interaction analysis model to obtain an initial interaction situation;

[0037] An adjustment block is used to keep the corresponding initial interaction posture unchanged when the compliance of the same data type settings under the data storage location is greater than or equal to the preset compliance;

[0038] When the compliance of the same data type setting under the data storage location is less than the preset compliance, the data storage location with the compliance less than the preset compliance is regarded as the first location;

[0039] Adjusting the initial interaction posture based on the attribute relationship of each first position and in combination with the compliance set for the corresponding data type of the first position;

[0040]

[0041] in, The value representing the adjusted data interaction situation; represents the value of the initial interaction state; ln represents the sign of the logarithmic function; represents a constant, with a value of 2.7; Indicates the quantity of the first position; The value of the attribute relationship representing the first position of u0; Indicates the compliance of the x-th data type under the u0-th first position; x represents the preset property for the xth data type; The number of positions where the value of the attribute relationship involving the first position is less than 0.6; express The factorial function of

[0042] Control block, used to Determine the data interaction status from the value-situation comparison table.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] This device not only considers multiple aspects of data assets, including costs, benefits, and market prices, but also conducts compliance reviews to ensure that the assessed data assets comply with legal and regulatory requirements. Furthermore, the device can flexibly select a cost-based, income-based, or market-based approach to conduct assessments based on actual needs, automatically generating an assessment report based on the results, thereby improving the accuracy and efficiency of assessments. Furthermore, the device's automated and intelligent features significantly enhance the efficiency and accuracy of data asset assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 This is a structural diagram of a comprehensive data asset value assessment device provided by an embodiment of the present invention;

[0047] Figure 2 This is a flow chart of a comprehensive data asset value assessment method provided by an embodiment of the present invention;

[0048] Figure 3 This is a first asset interaction structure diagram provided by an embodiment of the present invention;

[0049] Figure 4 is a second asset interaction structure diagram provided by an embodiment of the present invention;

[0050] Figure 5 This is a diagram of the initial interaction situation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0052] The present invention provides a comprehensive data asset value assessment device, such as Figure 1 Shown, including:

[0053] Data asset inventory module, used to automatically scan the enterprise's data system, identify and classify various types of data assets;

[0054] The data asset compliance review module is used to review the compliance of various types of data assets under the indicator dimension evaluation system;

[0055] The data asset measurement module is used to conduct multi-dimensional quantitative analysis of various types of data assets and determine the main value attributes of various types of data assets;

[0056] The evaluation module is used to evaluate the cost investment, future benefits and market transaction value of data assets according to existing evaluation methods;

[0057] The assessment report module is used to determine the data interaction status of various types of data assets in the corresponding enterprise based on compliance and main value attributes, and generate a data asset assessment report based on the assessment results.

[0058] In this embodiment, the data asset inventory module is the foundational module of the device, used to comprehensively inventory and organize an enterprise's internal data assets. This module automatically scans the enterprise's data systems, identifying and classifying various types of data assets, including structured and unstructured data. Through data asset inventory, enterprises can clearly understand the types, quantities, and distribution of their data assets, providing basic data support for subsequent data asset assessments.

[0059] In this embodiment, the data asset compliance review module is used to review the compliance of data assets. Compliance is an important consideration in the data asset assessment process. This module can check whether the module complies with relevant laws and regulations and privacy protection requirements. The module mainly focuses on the four core aspects of the legitimacy of data sources, the legitimacy of data processing, the legitimacy of data management, and the legitimacy of data operations. Through a series of review processes and methods, this module constructs an indicator system that includes evaluation dimensions such as quality, value, risk, and management. It uses the data asset scoring card model to set scoring criteria for each indicator and calculate the overall score based on the weight. This module helps enterprises identify and correct non-compliant behaviors in the use of data assets, reduce legal risks, and improve data security and operational efficiency. At the same time, the module can also provide compliance recommendations to help enterprises optimize data asset management processes and ensure the legitimacy and security of data assets.

[0060] In this embodiment, the data asset measurement module is used to perform quantitative analysis of data assets. This module can quantitatively assess data assets based on dimensions such as cost, allocation, impairment / increase in value, and future returns. This module includes data asset cards, cost aggregation and allocation, asset impairment / increase in value change management, and asset retirement. Data asset amortization (asset depreciation) methods include the straight-line method, the sum-of-the-years'-digits method, and the double-declining balance method. Data asset value changes include asset impairment orders, asset appreciation orders, and data asset retirement. Furthermore, the module measures the asset's expected returns over the next five or ten years, including direct benefits (such as sales revenue and profit) and indirect benefits (such as increased brand awareness and customer satisfaction). Through quantitative analysis, enterprises can more intuitively understand the value attributes and characteristics of data assets, providing an important basis for subsequent data asset evaluation.

[0061] In this embodiment, the assessment report module is used to generate a data asset assessment report. This module integrates the results of various assessment modules, including data asset inventory results, compliance review results, quantitative analysis results, and assessment results using cost, income, and market approaches, to produce a comprehensive and detailed data asset assessment report. The assessment report module provides enterprises with a complete record and conclusions of data asset assessments, helping them better manage and utilize data assets.

[0062] In this embodiment, the data asset inventory module is first used to automatically scan the enterprise's data system to identify and classify various types of data assets. The data asset compliance review module is used to conduct compliance reviews on the collection, storage, processing and use of data assets. Based on the review results, compliance recommendations for data asset management are put forward to optimize the data management process and ensure the legality and security of the data. The data asset measurement module is used to conduct quantitative analysis on the dimensions of cost, allocation, impairment / increase in value and future income of data assets. Based on the results of the quantitative analysis, the main value attributes of the data assets are determined to provide an important basis for subsequent evaluation. The device then uses three or more of the cost method device, the income method device and the market method device to evaluate the asset value. Finally, the evaluation report device is used to integrate the results of each evaluation module, including the data asset inventory results, compliance review results, quantitative analysis results and the evaluation results of the cost method, income method and market method, to generate a comprehensive and detailed data asset evaluation report. The report should include the evaluation purpose, evaluation method, evaluation process, evaluation results, conclusions and recommendations, etc., such as Figure 2 shown.

[0063] In this embodiment, the automated scanning of an enterprise's data systems utilizes specially designed software tools or programs, following pre-set rules and paths, to autonomously traverse and inspect all internal data systems involved in data storage, processing, and transmission. These data systems include database management systems (e.g., MySQL, Oracle), file servers, cloud storage platforms, and various business application systems (e.g., Enterprise Resource Planning (ERP) systems and Customer Relationship Management (CRM) systems).

[0064] In this embodiment, acquired data is accurately identified as different types of data assets based on characteristics such as its nature, purpose, and source, and categorized according to specific standards. Data assets come in a variety of types, with common examples including customer data assets (including basic customer information and consumer preferences), operational data assets (such as production process data and supply chain logistics data), and financial data assets (such as financial statement data and cost accounting data).

[0065] In this embodiment, the indicator dimension evaluation system is a set of pre-established measurement standards.

[0066] In this embodiment, the primary value attribute refers to the attribute that has the greatest impact on the data asset.

[0067] In this embodiment, the data interaction situation refers to whether the interaction of data assets under this data type is in an upward trend, a downward trend, a stable trend, etc.

[0068] The data interaction volume is a measure of the amount of data flowing between different data storage locations for each interactive data type. It can be the number of bytes transmitted, the number of records, etc., and is used to measure the scale of data interaction.

[0069] In this embodiment, high-frequency interaction volume: under the same data interaction trend, for each interactive data type, filter out data interaction volume values ​​that appear more frequently from a large number of data interaction volume records.

[0070] In this embodiment, the second asset interaction diagram is a graph that reflects the asset interaction between different data storage locations. It is constructed based on the screened high-frequency interaction volume, and uses the graph to display the main flow and scale of data interaction between different data storage locations, highlighting the important pattern of data interaction between different storage locations of the enterprise.

[0071] The beneficial effects of this technical solution are: the device not only considers multiple aspects of data assets, such as costs, benefits, and market prices, but also ensures that the assessed data assets comply with legal and regulatory requirements through compliance reviews. Furthermore, the device can flexibly select a cost-based, income-based, or market-based approach to conduct assessments based on actual needs, automatically generating an assessment report based on the results, thereby improving the accuracy and efficiency of assessments. Furthermore, the device's automated and intelligent features significantly enhance the efficiency and accuracy of data asset assessments.

[0072] The present invention provides a comprehensive data asset value assessment device, wherein the data asset compliance review module includes:

[0073] A capture unit, configured to capture the security level and user level of each data storage location based on the data storage locations of the enterprise involved in the automatic scanning process;

[0074] a feature determination unit, configured to determine the privacy security feature of the data storage location according to the parameter settings of the security level and the user level;

[0075] A mining unit is used to mine the type source of each data type in the data storage location, the data processing method in the process from the type source to the data storage location, the data management method after the data assets of the corresponding data type reach the data storage location, and the data usage purpose of the data assets of the corresponding data type;

[0076] The tracing unit is used to trace the source of the data type, data processing method, data management method and data usage purpose according to the privacy security characteristics and legal security characteristics, and to build a review matrix based on the indicator dimension evaluation system. ,in, 、 、 、 Respectively represent the evaluation vectors of the j-th data asset under the data storage location based on type source, data processing method, data management method, and data usage purpose;

[0077] Among them, the indicator dimension evaluation system is related to the quality dimension, value dimension, risk dimension and management dimension.

[0078] Preferably, the data asset compliance review module further includes:

[0079] The matrix analysis unit is used to analyze the review matrix and set compliance for data assets under corresponding data types under corresponding data storage locations.

[0080] In this embodiment, the data storage location is a physical or virtual space in the enterprise for storing data, such as a local server hard disk, a specific storage area of ​​a cloud storage platform, a specific table space in a database, etc.

[0081] In this embodiment, the security level is a security protection level divided according to the importance, sensitivity and potential risk of the data. For example, a high security level may mean that strict access control, encrypted storage and other measures are required; a low security level is relatively loose.

[0082] In this embodiment, user levels are levels of access rights to data storage locations based on the user's role and responsibilities in the enterprise. For example, an administrator may have the highest authority, while ordinary employees may have lower authority.

[0083] In this embodiment, the parameter setting conditions are a series of rules and conditions pre-set for different security levels and user levels, such as access control policies, encryption methods, audit requirements, and the like.

[0084] In this embodiment, the privacy and security features describe the characteristics of the data storage location in terms of privacy protection and security protection, including the degree of protection of the confidentiality, integrity, availability, etc. of the data.

[0085] For a high-security setting in a local data center, data is encrypted and stored using the AES-256 encryption algorithm. Access is restricted to advanced users who have undergone multi-factor authentication, and all access operations are subject to detailed auditing. Based on these parameters, its privacy and security features include high confidentiality (data encryption), integrity (encryption to prevent tampering), and strictly controlled availability (access is restricted to specific advanced users who must undergo strict authentication). In contrast, a medium-security setting in a cloud storage setting uses simple SSL encryption, is accessible to intermediate users and above, and performs regular data backups. Its privacy and security features include moderate confidentiality (SSL encryption), a certain degree of integrity (backups ensure data recoverability), and relatively relaxed availability (accessibility to a wider range of users).

[0086] In this embodiment, the type source is the source of data generation, which may be an internal business system of an enterprise, an external data provider, user input, etc.

[0087] In this embodiment, the data processing method is the operation performed on the data from the generation of the data to the storage process, such as cleaning, conversion, aggregation, etc.

[0088] In this embodiment, the data management method is management activities such as maintenance, update, and backup performed after data is stored.

[0089] In this embodiment, the purpose of data usage is the purpose of the enterprise using the data, such as for decision-making analysis, product optimization, customer service, etc. Specifically: the type source is the user's browsing behavior record on the e-commerce platform. From the source to the storage process, the data processing method includes removing invalid records (such as browsing records generated by erroneous jumps) and converting the browsing time format to a standard time format. After the data is stored, the data management method is to regularly back up to another cloud storage area to prevent data loss, and according to the timeliness of the data, delete browsing history that exceeds a certain time every six months. The purpose of data usage is mainly used to analyze user browsing habits,

[0090] In this embodiment, the legal security features are data security-related features that comply with legal and regulatory requirements, such as the legality of data collection, the compliance of data storage, etc.

[0091] In this embodiment, the rows of the matrix represent the evaluation results of different data assets in terms of quality, value, risk, and management, and the columns represent different evaluation dimensions (type source, data processing method, etc.).

[0092] In this embodiment, for each evaluation dimension, a set of numerical values ​​or descriptive indicators is used to represent the evaluation results of the data assets in this dimension, which is an evaluation vector. In other words, each row in the matrix is ​​regarded as a vector.

[0093] In this embodiment, the indicator dimension evaluation system is constructed from a set of evaluation criteria across multiple dimensions, including quality (data accuracy, completeness, etc.), value (value contribution to the business), risk (security risk, legal risk, etc.), and management (effectiveness of data management processes). From a privacy and security perspective, data encryption and storage ensure confidentiality, requiring tracing of the data source to verify legal access to user payment information (e.g., whether explicit user authorization was obtained). From a legal and security perspective, data processing methods are examined to verify compliance with relevant payment data regulations, such as whether encryption during payment information transmission meets standards. Regarding data management methods, backup strategies are examined to verify compliance with legal retention periods. Regarding data usage, data is verified to verify whether it is used solely for order payment processing and compliance with financial audits. Based on these tracing results, a review matrix is ​​constructed. For example, for the first user payment information data asset, in the source evaluation vector, if legally authorized access is recorded as "yes," the corresponding value is 1; in the data processing method evaluation vector, if encryption complies with regulations, the corresponding value is 5 (assuming a maximum score of 10). In the indicator dimension evaluation system, the quality dimension focuses on the accuracy of payment information, the value dimension evaluates its importance to transaction completion, the risk dimension evaluates the risk of payment information leakage, and the management dimension evaluates the standardization of the payment information management process.

[0094] Based on the results of the review matrix analysis, the data asset's compliance status is determined. Corrective measures are proposed for any non-compliance to ensure compliance with privacy and legal security requirements. For example, compliance is set to "non-compliant" and corrective measures are proposed, such as immediately adjusting the backup strategy to daily backups. Similar checks and compliance settings are also performed on other relevant data assets in the review matrix. For user browsing history data assets in cloud storage, if the review matrix analysis shows that all assessment dimensions meet privacy and legal requirements, the compliance status is set to "compliant."

[0095] The beneficial effect of the above technical solution is: starting from the user level and security level to determine the privacy and security characteristics, and building a matrix with the type source, data processing method, data management method and data usage purpose to analyze compliance and provide a basis for report generation.

[0096] The present invention provides a comprehensive data asset value assessment device, wherein the data asset measurement module includes:

[0097] The quantitative assessment unit is used to quantitatively assess data assets based on their cost, allocation, impairment / increase in value, and future income, and to obtain an assessment coefficient based on each dimension.

[0098] A screening unit, configured to screen a maximum coefficient from the evaluation coefficients under the corresponding data asset, and obtain all influencing attributes of the maximum coefficient and the dimension corresponding to the maximum coefficient;

[0099] The attribute determination unit is used to screen the attribute with the greatest influence from all influencing attributes and regard it as the main value attribute.

[0100] In this example, in terms of cost, acquiring user behavior data assets cost 1 million yuan for data collection equipment and data provider fees, and annual maintenance of the data storage and processing system costs 200,000 yuan. Compared to similar industries, these costs are within a reasonable range, and the cost dimension assessment coefficient is set at 0.8 (out of a maximum score of 1). In terms of allocation, the company's advertising, market research, and customer service departments share this data. Based on the frequency and duration of data use by each department, the advertising department bears 60% of the cost, the market research department bears 30%, and the customer service department bears 10%. The advertising department has seen significant growth in advertising revenue from the use of this data asset, resulting in an allocation coefficient of 0.9. In terms of impairment / value change, tightening user privacy regulations have restricted access to some data, leading to a decline in the value of the data assets. The assessed impairment is 10%, and the impairment / value change coefficient is 0.7. In terms of future revenue, it is predicted that the user behavior data assets will bring the company an additional advertising revenue of 500,000 yuan per year in the next three years. Considering a risk discount rate of 10%, the present value is converted and compared with the current investment cost, the evaluation coefficient of the future revenue dimension is 0.85.

[0101] In this example, comparing the cost dimension evaluation coefficient of 0.8, the allocation dimension evaluation coefficient of 0.9, the impairment / increase change dimension evaluation coefficient of 0.7, and the future income dimension evaluation coefficient of 0.85, it is found that the allocation dimension evaluation coefficient of 0.9 is the largest. Under the allocation dimension, the main influencing attributes are the frequency and duration of data use by each department, and the close connection between the business and the data. The advertising department uses data frequently and for a long time, and the business is closely related to user behavior data. This is a major reason for the high allocation dimension evaluation coefficient.

[0102] The frequency of data usage by each department is the most sensitive factor affecting the apportionment dimension's evaluation coefficient. When the advertising department's data usage frequency increases by 10%, the apportionment dimension's evaluation coefficient rises to 0.95. However, other influencing attributes, such as usage duration and business relevance, have relatively little impact on the evaluation coefficient at the same rate of change. Therefore, "frequency of data usage by each department" is determined to be the primary value attribute of this user behavior data asset.

[0103] The beneficial effect of the above technical solution is: quantitatively evaluating data assets from four dimensions to screen the maximum coefficients and influencing attributes, so as to obtain the main value attributes as a basis for subsequent analysis.

[0104] The present invention provides a comprehensive data asset value assessment device, wherein the existing assessment methods include: cost method, income method and market method.

[0105] In this embodiment, the cost evaluation algorithm is as follows:

[0106] Cost method valuation = replacement cost × (1-depreciation rate);

[0107] Replacement cost = direct cost + indirect cost + related taxes;

[0108] ;

[0109] i=1 is the starting month for the asset cost collection, and n is the ending month for the asset cost collection.

[0110] Monthly labor costs are calculated as follows: The labor hours spent on manual data collection, processing, and product development for the data asset number in the accounting month. Non-labor costs are calculated as the monthly total of non-labor costs for the data asset number. Indirect costs include the shared expenses related to utilities, rent, hardware, and network fees used in asset development and operations. Enterprise data assets are initially valued and recorded using the cost method.

[0111] The income approach can predict the future returns of data assets, including direct benefits (such as sales revenue and profits) and indirect benefits (such as increased brand awareness and customer satisfaction). Through the income approach, companies can more accurately understand the potential value of data assets, providing important reference for optimizing data asset allocation and strategic decision-making. The income approach valuation algorithm is as follows:

[0112] ;

[0113] Where: P is the data asset valuation; The income of the data asset in the tth income period in the future; is the remaining economic life; t is the tth year in the future; v is the discount rate.

[0114] The applicable scenario of the income method is data assets with expected future returns, that is, inventory data assets that can be circulated and traded.

[0115] The market approach is to evaluate the value of data assets by comparing the transaction prices of similar data assets in the market.

[0116] ;

[0117] Where: P is the data asset valuation, n is the number of data resources / data products that the assessed data asset is decomposed into, To refer to the value of data assets; is the quality adjustment factor, is the supply and demand adjustment coefficient; The daily adjustment factor; is the capacity adjustment factor; is other adjustment factors.

[0118] The beneficial effects of the above technical solution are: effectively evaluating data assets through three methods, providing a basis for subsequent report generation.

[0119] The present invention provides a comprehensive data asset value assessment device, wherein the assessment report module includes:

[0120] A table construction unit, configured to construct an enterprise-based data storage mapping table based on the compliance and main value attributes;

[0121] A graph construction unit is configured to construct a first asset interaction graph for each data storage location based on asset interactions between data assets of different data types in each data storage location, and simultaneously construct a second asset interaction graph for the enterprise based on asset interactions between each data storage location;

[0122] The situation determination unit is used to determine the data interaction situation of each type of asset data based on the data storage mapping table, the second asset interaction diagram and all the first asset interaction diagrams.

[0123] In this embodiment, the data storage mapping table is a table that reflects the correspondence between the compliance and main value attributes of each data storage location and the data storage location.

[0124] In this embodiment, the asset interaction situation refers to the mutual interaction and mutual influence between data, which can be directly captured by the enterprise in the process of executing business and belongs to existing data.

[0125] In this embodiment, asset interaction Figure 1 One is for data storage within the location, the other is for data storage between different locations.

[0126] The beneficial effect of the above technical solution is: by constructing a data storage mapping table, a first asset interaction diagram, and a second asset interaction diagram, the data interaction status of each type of asset data is determined, providing a basis for subsequent evaluation and analysis.

[0127] The present invention provides a comprehensive data asset value assessment device, a graph construction unit, comprising:

[0128] The first statistics block is used to collect statistics on interaction information of the same data storage location based on each enterprise business. The interaction information includes the interaction trend between any two data assets and the value impact between the any two data assets. The value impact includes value improvement impact and value loss impact.

[0129] The first building block is used to determine the direction vector diagram of the data assets under each data type based on all the interaction information, and establish the vector pointing between the corresponding data type and each remaining type under the same data storage location, thereby constructing a first asset interaction diagram;

[0130] The second statistics block is used to count the data storage locations involved in each enterprise business, the data interaction trends between the locations, the data types of the interactions, and the data interaction volume based on the data type of each interaction;

[0131] The second building block is used to filter high-frequency interaction volumes from all data interaction volumes under each interaction data type involved in the same data interaction trend, and construct a second asset interaction graph.

[0132] In this embodiment, the first statistical block records that, for example, when Creative A interacts with User Profile B, Creative A flows to User Profile B, and because the two are highly compatible, this has a value-added impact on the effectiveness of advertising. After multiple business statistics, the first building block constructs a trend vector diagram for each type of creative data, showing the interaction between different creative data and between creative data and user profile data. For example, Creative A points to User Profile B, Creative C points to User Profile D, and so on. This constructs a first asset interaction diagram, clearly showing the interaction of data assets within the local server.

[0133] In this embodiment, the second statistical block records a transaction in which the local server transmits advertising creative data to the cloud storage. The data interaction type is advertising creative file transfer, and the interaction volume is 100MB. After multiple business statistics, the second building block filters out high-frequency interactions from many such interactions. For example, it is found that the interaction volume of advertising creative data transmitted from the local server to the cloud storage every Monday morning often exceeds 50MB. Based on this, the second asset interaction graph is constructed to show the data interaction between different data storage locations (local server and cloud storage), such as Figure 4 As shown, the figure also involves the relationship between the data center, local server and cloud storage, and the arrow points to the data flow direction, and the thickness of the arrow is the amount of data interaction.

[0134] In this embodiment, the data characteristics of each data storage location are understood from the data storage mapping table, the interaction details of the data assets within the local server and cloud storage are known from the first asset interaction diagram, and the interaction rules between the local server and cloud storage are grasped from the second asset interaction diagram. After comprehensive analysis, it is determined that, for example, in the new product promotion business, advertising creative data flows from the local server to the cloud storage and is combined with user behavior data. This cross-storage location interaction promotes the accuracy of advertising delivery and improves advertising effectiveness. This is a determination of the data interaction situation, which provides a basis for further evaluating the value of data assets in the advertising business. If the value of the data situation increases, it is considered that the situation has improved, that is, the value has increased. If the value of the data situation decreases, it is considered that the situation has decreased, that is, the value has been lost.

[0135] In this embodiment, the interaction direction refers to the direction of flow from one data asset to another data asset.

[0136] In this embodiment, the value enhancement impact refers to the increase in the value of the target data asset to the enterprise business after the interaction between one data asset and another data asset. For example, the interaction between accurate user portrait data and advertising data improves the effectiveness of advertising, thereby increasing the value of advertising data; the value loss impact is the opposite, which means that the value of the target data asset decreases after the interaction. For example, the interaction between outdated market trend data and product development data may lead to deviations in the product development direction, thereby reducing the value of the product development data.

[0137] In this embodiment, the direction vector diagram uses vector arrows to show the interaction direction between data assets under each data type. The starting point of the arrow is the asset where the data flows out, and the end point is the asset where the data flows in. Through this diagram, the flow direction and relationship of data between different assets can be intuitively seen.

[0138] In this embodiment, in the same data storage location, all data types except the specific data type currently being analyzed, for example, if the customer data type is currently being analyzed, then the product data type, financial data type, etc. are all remaining types.

[0139] In this embodiment, the first asset interaction diagram reflects the interaction between data assets of different data types within the same data storage location, and comprehensively displays the interaction network of data assets within the storage location through the trend vector diagram and vector direction. Figure 3 shown.

[0140] The beneficial effects of the above technical solution are: based on the interaction information, the interaction situation under the same data storage location is determined to construct a first asset interaction graph, and the second asset interaction graph is constructed according to the interaction situation between the data storage locations, providing a convenient basis for the subsequent analysis of the value of data assets.

[0141] The present invention provides a comprehensive data asset value assessment device, wherein the situation determination unit includes:

[0142] An initial block is used to extract a first interaction result and a second interaction result based on the same data type from the first asset interaction graph and the second asset interaction graph, respectively, and input them into the interaction analysis model to obtain an initial interaction situation;

[0143] An adjustment block is used to keep the corresponding initial interaction posture unchanged when the compliance of the same data type settings under the data storage location is greater than or equal to the preset compliance;

[0144] When the compliance of the same data type setting under the data storage location is less than the preset compliance, the data storage location with the compliance less than the preset compliance is regarded as the first location;

[0145] Adjusting the initial interaction posture based on the attribute relationship of each first position and in combination with the compliance set for the corresponding data type of the first position;

[0146]

[0147] in, The value representing the adjusted data interaction situation; represents the value of the initial interaction state; ln represents the sign of the logarithmic function; represents a constant, with a value of 2.7; Indicates the quantity of the first position; The value of the attribute relationship representing the first position of u0; Indicates the compliance of the x-th data type under the u0-th first position; x represents the preset property for the xth data type; The number of positions where the value of the attribute relationship involving the first position is less than 0.6; express The factorial function of

[0148] Control block, used to Determine the data interaction status from the value-situation comparison table.

[0149] In this embodiment, the interaction analysis model is obtained by training the neural network model based on two interaction results of the same type and the interaction status (value improvement, value loss, and value stability) as samples. Therefore, the initial interaction status can be directly obtained, such as Figure 5 shown.

[0150] The corresponding values ​​in different situations are different, ranging from 0 to 2, which are set in advance, and the preset value is 0.6.

[0151] In this embodiment, the value-state comparison table includes the values ​​of different data interaction states and the interaction states that match the values. It is a known table and can be directly matched.

[0152] In this embodiment, the value range of the corresponding value of the attribute relationship is 0 to 1. The closer the attributes are, the closer the corresponding value is to 1. For example, the corresponding value of attribute A and attribute B is 0.9.

[0153] In this embodiment, Obtained from the initial interaction status value corresponding table, the table is indexed by interaction status (value improvement, value loss, value stability, etc.), corresponding to different The value range is 0 to 2. The table specifically includes: interaction status categories (such as value improvement, value loss, and value stability), corresponding detailed descriptions (such as value improvement can be described as increased business revenue, improved resource utilization efficiency, etc.) and corresponding Get the value.

[0154] In this embodiment, Obtained from the first location attribute relationship value corresponding table, with the first location (the data storage location whose compliance is less than the preset) as the index, the attribute relationship value of each location is recorded, and the table specifically includes the first location identifier (such as location number, name, etc.), a detailed description of the location attributes (such as geographic location information, device configuration parameters, etc.), and the corresponding attribute relationship value.

[0155] In this embodiment, Obtain from the data type compliance correspondence table under the first position, use the first position and data type as index, and record the compliance value corresponding to each different data type under the first position. The table specifically includes the first position identifier, data type identifier (such as data type name, encoding, etc.), compliance detection standard description, actual detection results and corresponding compliance values.

[0156] In this embodiment, Obtained from the relevant table (the first position information table involving the attribute relationship value less than 0.6) to record the first position related information with an attribute relationship value less than 0.6, which may include a position identifier and a corresponding attribute relationship value. The table specifically includes the first position identifier, the attribute relationship value, and the screening identifier (marking whether the attribute relationship value of the position is less than 0.6).

[0157] The beneficial effect of the above technical solution is: based on the model, the situation of the two interaction results under the same data type is analyzed, and then the situation is adjusted by judging the compliance and combining the attribute relationship, so as to effectively obtain the latest situation and facilitate the subsequent acquisition of reports.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A comprehensive data asset value assessment device, characterized in that: include: Data asset inventory module, used to automatically scan the enterprise's data system, identify and classify various types of data assets; The data asset compliance review module is used to review the compliance of various types of data assets under the indicator dimension evaluation system; The data asset measurement module is used to conduct multi-dimensional quantitative analysis of various types of data assets and determine the main value attributes of various types of data assets; The evaluation module is used to evaluate the cost investment, future benefits and market transaction value of data assets according to existing evaluation methods; The assessment report module is used to determine the data interaction status of various types of data assets in the corresponding enterprise based on compliance and main value attributes, and generate a data asset assessment report based on the assessment results; The evaluation report module includes: A table construction unit, configured to construct an enterprise-based data storage mapping table based on the compliance and main value attributes; A graph construction unit is configured to construct a first asset interaction graph for each data storage location based on asset interactions between data assets of different data types in each data storage location, and simultaneously construct a second asset interaction graph for the enterprise based on asset interactions between each data storage location; a situation determination unit, configured to determine a data interaction situation of each type of asset data based on the data storage mapping table, the second asset interaction graph, and all first asset interaction graphs; Wherein, the situation determination unit includes: An initial block is used to extract a first interaction result and a second interaction result based on the same data type from the first asset interaction graph and the second asset interaction graph, respectively, and input them into the interaction analysis model to obtain an initial interaction situation; An adjustment block is used to keep the corresponding initial interaction posture unchanged when the compliance of the same data type settings under the data storage location is greater than or equal to the preset compliance; When the compliance of the same data type setting under the data storage location is less than the preset compliance, the data storage location with the compliance less than the preset compliance is regarded as the first location; Adjusting the initial interaction posture based on the attribute relationship of each first position and in combination with the compliance set for the corresponding data type of the first position; in, The value representing the adjusted data interaction situation; represents the value of the initial interaction state; ln represents the sign of the logarithmic function; represents a constant, with a value of 2.7; Indicates the quantity of the first position; The value of the attribute relationship representing the first position of u0; Indicates the compliance of the x-th data type under the u0-th first position; x represents the preset property for the xth data type; The number of positions where the value of the attribute relationship involving the first position is less than 0.6; express The factorial function of Control block, used to Determine the data interaction status from the value-situation comparison table.

2. The comprehensive data asset value assessment device according to claim 1, characterized in that: The data asset compliance review module includes: A capture unit, configured to capture the security level and user level of each data storage location based on the data storage locations of the enterprise involved in the automatic scanning process; a feature determination unit, configured to determine the privacy security feature of the data storage location according to the parameter settings of the security level and the user level; A mining unit is used to mine the type source of each data type in the data storage location, the data processing method in the process from the type source to the data storage location, the data management method after the data assets of the corresponding data type reach the data storage location, and the data usage purpose of the data assets of the corresponding data type; The tracing unit is used to trace the source of the data type, data processing method, data management method and data usage purpose according to the privacy security characteristics and legal security characteristics, and to build a review matrix based on the indicator dimension evaluation system. ,in, 、 、 、 Respectively represent the evaluation vectors of the j-th data asset under the data storage location based on type source, data processing method, data management method, and data usage purpose; Among them, the indicator dimension evaluation system is related to the quality dimension, value dimension, risk dimension and management dimension.

3. The comprehensive data asset value assessment device according to claim 2, characterized in that: The data asset compliance review module also includes: The matrix analysis unit is used to analyze the review matrix and set compliance for data assets under corresponding data types under corresponding data storage locations.

4. The comprehensive data asset value assessment device according to claim 1, characterized in that: The data asset measurement module includes: The quantitative assessment unit is used to quantitatively assess data assets based on their cost, allocation, impairment / increase in value, and future income, and to obtain an assessment coefficient based on each dimension. A screening unit, configured to screen a maximum coefficient from the evaluation coefficients under the corresponding data asset, and obtain all influencing attributes of the maximum coefficient and the dimension corresponding to the maximum coefficient; The attribute determination unit is used to screen the attribute with the greatest influence from all influencing attributes and regard it as the main value attribute.

5. The comprehensive data asset value assessment device according to claim 1, characterized in that: The existing evaluation methods include: cost approach, income approach and market approach.

6. The comprehensive data asset value assessment device according to claim 1, characterized in that: Graph building unit, including: The first statistics block is used to collect statistics on interaction information of the same data storage location based on each enterprise business. The interaction information includes the interaction trend between any two data assets and the value impact between the any two data assets. The value impact includes value improvement impact and value loss impact. The first building block is used to determine the direction vector diagram of the data assets under each data type based on all the interaction information, and establish the vector pointing between the corresponding data type and each remaining type under the same data storage location, thereby constructing a first asset interaction diagram; The second statistics block is used to count the data storage locations involved in each enterprise business, the data interaction trends between the locations, the data types of the interactions, and the data interaction volume based on the data type of each interaction; The second building block is used to filter high-frequency interaction volumes from all data interaction volumes under each interaction data type involved in the same data interaction trend, and construct a second asset interaction graph.

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