CN120198168B - A comprehensive data asset value assessment device - Google Patents
A comprehensive data asset value assessment deviceInfo
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Abstract
本发明提供一种全面的数据资产价值评估装置，属于数据评估技术领域，包括：数据资产盘点模块，用于自动扫描企业的数据系统，识别并分类各种类型的数据资产；数据资产合规审查模块，用于审查各种类型的数据资产在指标维度评估体系下的合规性；数据资产计量模块，用于对各种类型的数据资产进行多维度量化分析，确定各种类型的数据资产的主要价值属性；评估模块，用于按照现有评估方法分别对数据资产进行评估；评估报告模块，用于基于合规性以及主要价值属性，确定各种类型的数据资产在相应企业的数据交互态势，且结合评估结果，生成数据资产评估报告。为企业提供全面、准确的数据资产评估服务，有助于数据资产的高效管理和价值最大化。
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 an 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 an 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; and an assessment report module for determining the data interaction status of various types of data assets in corresponding enterprises based on compliance and main value attributes, and generating a data asset assessment report based on the assessment results. Providing enterprises with comprehensive and accurate data asset assessment services contributes to the efficient management and value maximization of data assets.
Description
Technical Field
The invention relates to the technical field of data evaluation, in particular to a comprehensive data asset value evaluation device.
Background
With the rapid development of information technology, data has become one of the most important intangible assets for enterprises. Enterprises can acquire valuable information and insight by collecting, storing, processing and analyzing data, thereby optimizing decisions, improving operation efficiency, and creating new business models and income sources. Therefore, effective management and value evaluation of data assets have become key to improving competitiveness and realizing sustainable development for enterprises.
While the importance of data assets is becoming increasingly prominent, there are challenges in evaluating their value. First, the data types are various, including structured data, unstructured data, etc., and different types of data have different value attributes and evaluation methods. Second, the value of data is difficult to quantify, and the value of data assets often appears to be on the potential benefits and competitive advantages that they can bring to the enterprise, which are often difficult to directly quantify and gauge. In addition, the data asset assessment is also constrained and limited in the aspects of laws and regulations, privacy protection, data security and the like, and the complexity and difficulty of the assessment are increased.
The conventional asset evaluation methods, such as cost method, income method and market method, can evaluate the value of the data asset to a certain extent, but often have the problems of complicated evaluation process, inaccurate evaluation result and the like. Cost methods mainly focus on cost investment of data assets, but neglect potential benefits and competitive advantages brought by the data assets, while the benefit methods consider future benefits of the data assets, the benefit methods are difficult to predict and quantify accurately, and market rules need to find similar data asset trading cases for comparison, but similar trading cases are often difficult to acquire or compare in the data asset market.
At present, some data asset evaluation solutions exist in the market, but the method has the defects that firstly, an evaluation method is single, the value attribute and the characteristics of the data asset are difficult to comprehensively reflect, secondly, the evaluation process is complicated, a large amount of time and labor cost are required to be consumed, thirdly, the evaluation result is inaccurate, and wide acceptance of enterprises and markets is difficult to obtain. Therefore, it is important to develop a device that can comprehensively and accurately evaluate the value of a data asset.
Disclosure of Invention
The invention provides a comprehensive data asset value evaluation device, which aims to solve the limitations and defects of the existing data asset evaluation method, provide comprehensive and accurate data asset evaluation service for enterprises, and is beneficial to efficient management and value maximization of data assets.
The invention relates to a comprehensive data asset value assessment device, as shown in fig. 1, comprising:
the data asset inventory module is used for automatically scanning a data system of an enterprise and identifying and classifying various types of data assets;
A data asset compliance review module for reviewing compliance of various types of data assets under an index dimension assessment system;
the data asset metering module is used for carrying out multidimensional quantitative analysis on various types of data assets and determining main value attributes of the various types of data assets;
The evaluation module is used for evaluating cost investment, future benefits and market trading value of the data assets according to the existing evaluation method;
and the evaluation report module is used for determining the data interaction situation of various types of data assets in corresponding enterprises based on the compliance and the main value attribute, and generating a data asset evaluation report by combining the evaluation result.
Preferably, the data asset compliance censoring module comprises:
a capturing unit, configured to capture a security level and a user level to which each data storage location belongs, based on the data storage locations of the enterprise involved in the automatic scanning process;
the feature determining unit is used for determining privacy security features of the data storage position according to the security level and parameter setting conditions of the user level;
The mining unit is used for mining the type source of each data type in the data storage position, the data processing mode in the process from the type source to the data storage position, the data management mode after the data asset under the corresponding data type reaches the data storage position, and the data use application of the data asset under the corresponding data type;
the traceability unit is used for respectively tracing the type source, the data processing mode, the data management mode and the data use according to the privacy security feature and the legal security feature, and constructing an audit matrix based on an index dimension evaluation system , wherein,、、、An assessment vector representing a j-th data asset in the data storage location based on a type source, a data processing mode, a data management mode, and a data usage purpose, respectively;
The index dimension evaluation system is related to a quality dimension, a value dimension, a risk dimension and a management dimension.
Preferably, the data asset compliance censoring module further comprises:
and the matrix analysis unit is used for analyzing the examination matrix and setting compliance for the data assets under the corresponding data types under the corresponding data storage positions.
Preferably, the data asset metering module includes:
The quantitative evaluation unit is used for quantitatively evaluating the data asset based on the cost, the allocation, the value reduction/increment variation and the future profit dimension of the data asset, and obtaining an evaluation coefficient based on each dimension;
the screening unit is used for screening the maximum coefficient from the evaluation coefficients under the corresponding data resources and acquiring the maximum coefficient and all influence attributes of the dimension corresponding to the maximum coefficient;
And the attribute determining unit is used for screening the attribute with the largest influence from all the influence attributes to be regarded as the main value attribute.
Preferably, the existing assessment method comprises a cost method, a benefit method and a market method.
Preferably, the evaluation report module includes:
The table construction unit is used for constructing a data storage mapping table based on enterprises based on the compliance and the main value attribute;
the map construction unit is used for constructing a first asset interaction map of each data storage position according to the asset interaction conditions among the data assets of different data types in each data storage position, and simultaneously constructing a second asset interaction map of an enterprise according to the asset interaction conditions among each data storage position;
And the situation determining unit is used for determining the data interaction situation of the asset data of each type based on the data storage mapping table, the second asset interaction graph and all the first asset interaction graphs.
Preferably, the graph construction unit includes:
The first statistics block is used for counting interaction information of the same data storage position based on business of enterprises each time, wherein the interaction information is interaction trend between any two data assets and value influence between any two data assets, and the value influence comprises value improvement influence and value loss influence;
The first building block is used for determining a trend vector diagram of the data asset under each data type according to all interaction information, and building vector directions of the corresponding data type and each residual type under the same data storage position to obtain a first asset interaction diagram;
the second statistics block is used for counting the related data storage positions under each enterprise service, the related data interaction trend among the positions, the interactive data types and the data interaction quantity under the data type based on each interaction;
and the second building block is used for screening the high-frequency interaction quantity from all the data interaction quantities under the data type of each interaction related to the same data interaction trend, and building a second asset interaction graph.
Preferably, the situation determining unit includes:
The initial block is used for respectively extracting a first interaction result and a second interaction result based on the same data type from the first asset interaction diagram and the second asset interaction diagram, inputting the first interaction result and the second interaction result into an interaction analysis model and obtaining an initial interaction situation;
The adjusting block is used for keeping the corresponding initial interaction situation unchanged when the compliance of the same data type set in the data storage position is larger than or equal to the preset compliance;
when the compliance of the data storage position set by the same data type is smaller than the preset compliance, the data storage position with the compliance smaller than the preset compliance is regarded as a first position;
Based on the attribute relation of each first position, and combining the compliance of the first position for the corresponding data type, adjusting the initial interaction situation;
wherein, the A value representing the adjusted data interaction situation; Ln represents a logarithmic function symbol; a constant is represented, and the value is 2.7; representing the number of first locations; a value representing an attribute relationship of the u0 th first position; indicating compliance for the x-th data type at the u 0-th first location; x represents a presettability for the xth data type; a number of positions having a value representing the attribute relationship of the first position concerned of less than 0.6; Representation of Is a factorial function of (a);
control block for according to And determining the data interaction situation from the value-situation comparison table.
Compared with the prior art, the application has the following beneficial effects:
The device not only considers the cost, income, market price and other aspects of the data asset, but also ensures that the assessed data asset meets the requirements of laws and regulations through compliance examination. In addition, the device can flexibly select a cost method, a benefit method or a market method for evaluation according to actual requirements, and automatically generates an evaluation report from an evaluation result, thereby improving the accuracy and efficiency of evaluation. Meanwhile, the device has the characteristics of automation and intellectualization, and can greatly improve the efficiency and accuracy of data asset evaluation.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a comprehensive data asset value assessment device provided by an embodiment of the present invention;
FIG. 2 is a flow chart of a comprehensive data asset value assessment method provided by an embodiment of the present invention;
FIG. 3 is a first asset interaction structural diagram provided by an embodiment of the present invention;
FIG. 4 is a second asset interaction structural diagram provided by an embodiment of the present invention;
Fig. 5 is a diagram of an initial interaction situation provided by an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention relates to a comprehensive data asset value assessment device, as shown in fig. 1, comprising:
the data asset inventory module is used for automatically scanning a data system of an enterprise and identifying and classifying various types of data assets;
A data asset compliance review module for reviewing compliance of various types of data assets under an index dimension assessment system;
the data asset metering module is used for carrying out multidimensional quantitative analysis on various types of data assets and determining main value attributes of the various types of data assets;
The evaluation module is used for evaluating cost investment, future benefits and market trading value of the data assets according to the existing evaluation method;
and the evaluation report module is used for determining the data interaction situation of various types of data assets in corresponding enterprises based on the compliance and the main value attribute, and generating a data asset evaluation report by combining the evaluation result.
In this embodiment, the data asset inventory module is the base module of the device for comprehensive inventory and carding of data assets within the enterprise. The module is capable of automatically scanning the enterprise's data system, identifying and classifying various types of data assets, including structured data, unstructured data, and the like. Through data asset inventory, enterprises can clearly know the types, the quantity, the distribution conditions and the like of the data assets, and basic data support is provided for subsequent data asset evaluation.
In this embodiment, the data asset compliance review module is configured to review compliance of the data asset. Compliance is an important consideration in the data asset assessment process. The module can be used for checking whether the module accords with related laws and regulations and privacy protection requirements mainly around four core aspects of data source legitimacy, data processing legitimacy, data management legitimacy and data management legitimacy, the module constructs an index system containing evaluation dimensions of quality, value, risk, management and the like through a series of examination processes and methods, sets a scoring standard for each index by utilizing a data asset scoring model, and calculates overall scores according to weights. The module helps enterprises to identify and correct the non-compliance behavior in the use of the data assets, reduces legal risks, and improves data safety and operation efficiency. Meanwhile, the module can also provide compliance advice to help enterprises optimize the data asset management flow and ensure the legitimacy and safety of the data asset.
In this embodiment, the data asset metering module is used to quantitatively analyze the data asset. The module can quantitatively evaluate the data asset based on the cost, the allocation, the value reduction/increment change, the future benefits and other dimensions of the data asset, including data asset cards, cost collection and allocation, asset value reduction/increment change management and asset rejection. The data asset amortization (asset depreciation) method includes a plurality of amortization depreciation methods such as average year law, total year law and double balance progressive subtraction. The data asset value change includes an asset value reduction list, an asset value increase list, and a data asset discard. The data asset value change includes an asset value reduction list, an asset value increase list, and a data asset discard. And measuring revenue for the asset for the next 5 years or 10 years, including direct revenue (e.g., sales revenue, profit, etc.) and indirect revenue (e.g., brand awareness improvement, customer satisfaction improvement, etc.). Through quantitative analysis, enterprises can more intuitively know the value attributes and characteristics of the data assets, and provide important basis for subsequent data asset evaluation.
In this embodiment, the assessment reporting module is configured to generate a data asset assessment report. The module can integrate the results of all the evaluation modules, including the data asset inventory results, the compliance examination results, the quantitative analysis results, the evaluation results of cost methods, income methods and market methods, and the like, and generate a comprehensive and detailed data asset evaluation report. The assessment reporting module provides the enterprise with a complete record and conclusion of the assessment of the data asset, helping the enterprise to better manage and utilize the data asset.
In this embodiment, the data asset inventory module is first utilized to automatically scan the enterprise's data system to identify and sort various types of data assets. And utilizing a data asset compliance examination module to carry out compliance examination on links such as acquisition, storage, processing and use of the data asset, and according to examination results, proposing compliance suggestions for data asset management, optimizing a data management flow and ensuring the legitimacy and safety of the data. And (3) quantitatively analyzing the cost, the allocation, the value reduction/increment variation, the future benefits and other dimensions of the data asset by utilizing the data asset metering module, and determining the main value attribute of the data asset according to the quantitative analysis result so as to provide an important basis for subsequent evaluation. And then the device respectively uses three or more of a cost method device, a benefit method device and a market method device to evaluate the asset value, and finally utilizes an evaluation reporting device to integrate the results of all evaluation modules, including a data asset inventory result, a compliance examination result, a quantitative analysis result, evaluation results of the cost method, the benefit method and the market method, and the like, so as to generate a comprehensive and detailed data asset evaluation report. The report should include the contents of evaluation purpose, evaluation method, evaluation process, evaluation result, conclusion, suggestion, etc., as shown in fig. 2.
In this embodiment, the data system of the automatic scanning enterprise uses specially designed software tools or programs to autonomously perform traversal checks on all data systems related to data storage, processing and transmission within the enterprise according to preset rules and paths. These data systems encompass database management systems (e.g., mySQL, oracle), file servers, cloud storage platforms, various business application systems (e.g., enterprise resource planning ERP systems, customer relationship management CRM systems), and the like.
In this embodiment, the acquired data is accurately identified as different types of data assets according to characteristics of the nature, purpose, source, etc. of the data, and is categorized according to a certain standard. The data asset types are various, and common are customer data assets (including customer base information, consumption preferences, etc.), operational data assets (e.g., production flow data, supply chain logistics data), financial data assets (financial statement data, cost accounting data), etc.
In this embodiment, the index dimension assessment system is a set of pre-established metrics.
In this embodiment, the primary value attribute refers to the attribute that most affects the data asset.
In this embodiment, the data interaction situation refers to whether the interaction of the data asset under the data type is in an upward trend, a downward trend, a steady trend, or the like.
The data interaction amount is a measure of the amount of data flowing between different data storage locations, for each type of data interacted, and may be the number of bytes of data transferred, the number of records, etc., used to scale the data interaction.
In the embodiment, the high-frequency interaction quantity is that the data interaction quantity values with more occurrence times and more frequent occurrence times are screened out from a large number of data interaction quantity records according to the data type of each interaction under the same data interaction direction.
In the embodiment, the second asset interaction graph, which reflects asset interaction conditions among different data storage locations, is constructed based on the screened high-frequency interaction quantity, and highlights important modes of data interaction among different storage locations of an enterprise by displaying main flow directions and scales of data interaction among different data storage locations through the graph.
The technical scheme has the beneficial effects that the device not only considers the cost, income, market price and other aspects of the data asset, but also ensures that the assessed data asset meets the requirements of legal regulations through compliance examination. In addition, the device can flexibly select a cost method, a benefit method or a market method for evaluation according to actual requirements, and automatically generates an evaluation report from an evaluation result, thereby improving the accuracy and efficiency of evaluation. Meanwhile, the device has the characteristics of automation and intellectualization, and can greatly improve the efficiency and accuracy of data asset evaluation.
The invention relates to a comprehensive data asset value assessment device, which comprises a data asset compliance examination module, wherein the data asset compliance examination module comprises:
a capturing unit, configured to capture a security level and a user level to which each data storage location belongs, based on the data storage locations of the enterprise involved in the automatic scanning process;
the feature determining unit is used for determining privacy security features of the data storage position according to the security level and parameter setting conditions of the user level;
The mining unit is used for mining the type source of each data type in the data storage position, the data processing mode in the process from the type source to the data storage position, the data management mode after the data asset under the corresponding data type reaches the data storage position, and the data use application of the data asset under the corresponding data type;
the traceability unit is used for respectively tracing the type source, the data processing mode, the data management mode and the data use according to the privacy security feature and the legal security feature, and constructing an audit matrix based on an index dimension evaluation system , wherein,、、、An assessment vector representing a j-th data asset in the data storage location based on a type source, a data processing mode, a data management mode, and a data usage purpose, respectively;
The index dimension evaluation system is related to a quality dimension, a value dimension, a risk dimension and a management dimension.
Preferably, the data asset compliance censoring module further comprises:
and the matrix analysis unit is used for analyzing the examination matrix and setting compliance for the data assets under the corresponding data types under the corresponding data storage positions.
In this embodiment, the data storage locations are physical or virtual spaces in the enterprise for storing data, such as local server hard disks, specific storage areas of the cloud storage platform, specific tablespaces in the database, and the like.
In this embodiment, the security level is a security protection level that is classified 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, etc. is required, while a low security level is relatively relaxed.
In this embodiment, the user level is a division of access rights levels to the data storage locations based on the roles and responsibilities of the user in the enterprise. If the administrator may have the highest authority, the ordinary employee authority is lower.
In this embodiment, the parameter setting condition is a series of rules and conditions preset for different security levels and user levels, such as access control policy, encryption mode, audit requirement, etc.
In this embodiment, the privacy security feature describes the characteristics of the data storage location in terms of privacy protection and security protection, including the degree of assurance in terms of confidentiality, integrity, availability, etc. of the data.
For the setting of a high security level of a local data center, the parameters are that AES-256 encryption algorithm is started to encrypt and store data, only advanced users with multiple identity verifications can access the data, and all access operations are audited in detail. Based on these parameters, their privacy security features are manifested as high confidentiality (data encryption), integrity (encryption tamper resistance) and tightly controlled availability (specific advanced users are tightly authenticated for access). And setting security level in cloud storage, wherein parameters are simple SSL encryption, and the data backup is carried out regularly, wherein the security level is set in the cloud storage, and the parameters are accessible to middle-level and above users. Its privacy security features are moderate confidentiality (SSL encryption), a degree of integrity (backup ensures data recovery), and relatively loose availability (more user accessible).
In this embodiment, the type source is the source of data generation, possibly an enterprise internal business system, an external data provider, user input, etc.
In this embodiment, the data processing mode is operations performed on the data from the generation to the storage of the data, such as cleaning, conversion, aggregation, and the like.
In this embodiment, the data management method is management activities such as maintenance, update, and backup performed after data storage.
In this embodiment, the data usage purpose is the purpose of the enterprise to use the data, such as for analysis decisions, product optimization, customer service, etc. The type source is a browsing behavior record of the user on the e-commerce platform. The data processing method comprises removing invalid records (such as browsing records generated by error skip) and uniformly converting browsing time format into standard time format. After data storage, the data management mode is to regularly backup to another cloud storage area to prevent data loss, and according to timeliness of the data, browsing histories exceeding a certain time are deleted every half year. The data use purpose is mainly to analyze the browsing habit of the user,
In this embodiment, the legal security features are data security related features that meet legal regulations, such as legitimacy of data collection, compliance of data storage, and the like.
In this embodiment, the matrix rows represent the evaluation results of different data assets in the quality dimension, the value dimension, the risk dimension, and the management dimension, and the columns represent different evaluation dimensions (type sources, data processing modes, etc.).
In this embodiment, for each evaluation dimension, the evaluation result of the data asset in that dimension is represented by a set of numerical or descriptive indices, i.e., an evaluation vector, i.e., each row in the matrix is considered as a vector.
In this embodiment, the index dimension evaluation system is an evaluation standard set constructed from multiple dimensions such as quality (data accuracy, integrity, etc.), value (contribution value to business of an enterprise), risk (security risk, legal risk, etc.), management (validity of a data management flow), and the like. From the aspect of privacy security, the data encryption storage ensures confidentiality, and whether the data type source is legal or not needs to be traced back to obtain the user payment information (such as whether the user is explicitly authorized or not). From the legal security features, it is checked whether the data processing means complies with the regulations relating to the payment data, such as whether the encryption during the transmission of the payment information is up to standard. For the data management mode, whether the backup strategy meets the legal storage life is traced back. In terms of data usage, it is confirmed whether or not only for order payment processing and financial auditing of compliance. From these trace-back results, an audit matrix is constructed. For example, for the 1 st user payment information data asset, in the type source evaluation vector, if the legal authorization acquisition is marked as "yes", the corresponding value is 1, in the data processing mode evaluation vector, if the encryption is in compliance with the regulation is marked as "pass", the corresponding value is 5 (assuming full 10 minutes), and the like. In the index dimension evaluation system, the quality dimension pays attention to the accuracy of the payment information, the value dimension evaluates the importance of the payment information to the completion of the transaction, the risk dimension evaluates the risk of the leakage of the payment information, and the management dimension evaluates the standardization of the payment information management flow.
And according to the analysis result of the examination matrix, determining whether the data asset is in a compliant state, and providing a rectifying measure aiming at the non-compliance situation to meet the requirements of privacy safety and legal safety, for example, setting the compliance as 'non-compliance', and providing a rectifying measure, for example, immediately adjusting the backup strategy to be a daily backup, and simultaneously, performing similar examination and compliance setting on other related data assets in the examination matrix. And for a user in cloud storage to browse historical data assets, through analysis of an examination matrix, each evaluation dimension accords with privacy and legal requirements, and the compliance is set as compliance.
The technical scheme has the beneficial effects that the privacy security characteristics are determined from the user level and the security level, and the privacy security characteristics, the type source, the data processing mode, the data management mode and the data use purpose are used for constructing a matrix to analyze compliance, so that a basis is provided for report generation.
The invention relates to a comprehensive data asset value assessment device, which comprises a data asset metering module, a data asset analysis module and a data asset analysis module, wherein the data asset metering module comprises:
The quantitative evaluation unit is used for quantitatively evaluating the data asset based on the cost, the allocation, the value reduction/increment variation and the future profit dimension of the data asset, and obtaining an evaluation coefficient based on each dimension;
the screening unit is used for screening the maximum coefficient from the evaluation coefficients under the corresponding data resources and acquiring the maximum coefficient and all influence attributes of the dimension corresponding to the maximum coefficient;
And the attribute determining unit is used for screening the attribute with the largest influence from all the influence attributes to be regarded as the main value attribute.
In this embodiment, in the cost dimension, acquiring user behavior data assets takes 100 tens of thousands of yuan for purchasing data acquisition equipment and paying data provider fees, 20 tens of thousands of yuan per year for maintaining the data storage and processing system. Compared with the same industry, the cost is in a reasonable range, and the cost dimension evaluation coefficient is set to be 0.8 (full 1 minute). In the apportionment dimension, the advertising department, the market research department, and the customer service department of the company use these data together. According to the frequency and duration of using data by each department, the advertising department allocates 60% of cost, the market research department allocates 30% of cost and the customer service department allocates 10% of cost. The advertising income of the advertising department is obviously increased due to the use of the data asset, and the apportionment dimension evaluation coefficient is 0.9 after accounting. In the dimension of the reduction/increment variation, along with the stricter privacy protection regulations of users, partial data acquisition is limited, so that the value of the data asset is reduced to some extent, the evaluated reduction amplitude is 10%, and the evaluation coefficient of the dimension of the reduction/increment variation is 0.7. In the future profit dimension, predicting that the user behavior data asset will bring additional advertising revenue of 50 ten thousand yuan per year to the company for three years in the future, considering the risk discount rate of 10%, comparing the discount value with the current input cost, and the evaluation coefficient of the future profit dimension is 0.85.
In this example, the cost dimension evaluation coefficient 0.8, the split dimension evaluation coefficient 0.9, the reduced/increased variation dimension evaluation coefficient 0.7, and the future gain dimension evaluation coefficient 0.85 were compared, and the split dimension evaluation coefficient 0.9 was found to be the largest. Under the allocation dimension, the influence attribute mainly comprises the frequency of use of the data by each department, the time of use and the association tightness degree of the business and the data. The frequency of using data by the advertisement delivery department is high, the duration is long, and the service is closely related to the user behavior data, which is an important reason for higher evaluation coefficient of the shared dimension.
The frequency of use of the data by each department is most sensitive to the impact of the shared dimension assessment coefficients. When the frequency of using data by an advertisement delivery department is increased by 10%, the allocation dimension evaluation coefficient can be improved to 0.95, and other influence attributes such as the using time length and the service association tightness degree have relatively smaller improvement effect on the evaluation coefficient under the same change amplitude. Thus, the "frequency of use of data by departments" is determined as the primary value attribute of the user behavior data asset.
The technical scheme has the beneficial effects that the data assets are quantitatively evaluated from four dimensions to screen the maximum coefficient and the influence attribute, so that the main value attribute can be conveniently obtained, and the main value attribute is used as a basis for subsequent analysis.
The invention relates to a comprehensive data asset value assessment device, which comprises a cost method, a benefit method and a market method.
In this embodiment, the cost evaluation algorithm is as follows:
cost method evaluation value = reset cost× (1-devaluation rate);
reset cost = direct cost + indirect cost + associated tax;
;
i=1 is the asset cost reduction start month and n is the asset cost reduction stop month.
And the manual cost of accounting month is the manual data acquisition, processing and product research and development time cost value of the data asset number accounting month. The non-artificial cost term cost is the monthly cost sum in the non-artificial cost of the data asset number, and the indirect cost is the water and electricity house renting cost and the related amortization cost of the hardware network cost for asset research and development and operation maintenance. The enterprise data asset primary entry uses cost methods to perform data asset valuation entry.
The revenue method can predict the revenue situation of the data asset over a period of time in the future, including direct revenue (e.g., sales revenue, profit, etc.) and indirect revenue (e.g., brand awareness improvement, customer satisfaction improvement, etc.). Through the profit method evaluation, enterprises can know the potential value of the data asset more accurately, and important references are provided for the optimal configuration and strategic decision of the data asset. The value evaluation algorithm of the profit method is as follows:
;
P is a data asset assessment value; a profit margin for a future t-th profit period of the data asset; is the residual economic life period, t is the t-th year in the future, and v is the discount rate.
An applicable scenario for revenue methods is data assets that have expected revenue in the future, i.e., inventory-like data assets that can be transacted in circulation.
The market method is to evaluate the value of the data asset by comparing the transaction prices of similar data assets in the market and adopting the market method.
;
Wherein P is an evaluation value of the data asset, n is the number of data resources/data products into which the evaluated data asset is decomposed,Value for a reference data asset; For the purpose of the quality adjustment factor, The supply and demand adjustment coefficients are used; Adjusting the coefficient for the date; Is a capacity adjustment coefficient; Other adjustment coefficients.
The technical scheme has the beneficial effects that the data assets are effectively evaluated in three modes, and a basis is provided for the generation of subsequent reports.
The invention relates to a comprehensive data asset value assessment device, the evaluation report module includes:
The table construction unit is used for constructing a data storage mapping table based on enterprises based on the compliance and the main value attribute;
the map construction unit is used for constructing a first asset interaction map of each data storage position according to the asset interaction conditions among the data assets of different data types in each data storage position, and simultaneously constructing a second asset interaction map of an enterprise according to the asset interaction conditions among each data storage position;
And the situation determining unit is used for determining the data interaction situation of the asset data of each type based on the data storage mapping table, the second asset interaction graph and all the first asset interaction graphs.
In this embodiment, the data storage mapping table is a table obtained by reflecting compliance and main value attributes of each data storage location to the data storage location.
In this embodiment, the asset interaction condition refers to the condition of interaction and interaction between data, and the enterprise can directly capture and obtain the data in the process of executing the service, which belongs to the existing data.
In this embodiment, the asset interaction graph is for the interior of the data storage locations, one for between different data storage locations.
The technical scheme has the beneficial effects that the data interaction situation of the asset data of each type is determined by constructing the data storage mapping table, the first asset interaction diagram and the second asset interaction diagram, so that a foundation is provided for subsequent evaluation analysis.
The invention relates to a comprehensive data asset value assessment device, a graph construction unit comprising:
The first statistics block is used for counting interaction information of the same data storage position based on business of enterprises each time, wherein the interaction information is interaction trend between any two data assets and value influence between any two data assets, and the value influence comprises value improvement influence and value loss influence;
The first building block is used for determining a trend vector diagram of the data asset under each data type according to all interaction information, and building vector directions of the corresponding data type and each residual type under the same data storage position to obtain a first asset interaction diagram;
the second statistics block is used for counting the related data storage positions under each enterprise service, the related data interaction trend among the positions, the interactive data types and the data interaction quantity under the data type based on each interaction;
and the second building block is used for screening the high-frequency interaction quantity from all the data interaction quantities under the data type of each interaction related to the same data interaction trend, and building a second asset interaction graph.
In this embodiment, the first statistics block will record that, as creative A flows to user representation B when interacting with user representation B, and because of the high degree of matching of both, value-enhancing effects are produced on the advertising effectiveness. Through multiple business statistics, the first building block builds a trend vector diagram for the advertising creative data types, and interactive relations among different creative data and interactive relations among the creative data and user portrait data are displayed. For example, creative A points to user representation B, creative C points to user representation D, etc., and constructs a first asset interaction graph that clearly presents interactions of data assets within the local server.
In this embodiment, the second statistics block records that, in a service, the local server transmits the advertising creative data to the cloud storage, the data interaction type is advertising creative file transmission, and the interaction amount is 100MB. Through multiple business statistics, the second building block screens out high-frequency interaction quantities from a plurality of interaction quantities, if the fact that the interaction quantity of the local server transmitting advertisement creative data to the cloud storage in the morning every monday is found to be more than 50MB frequently, a second asset interaction graph is built, the data interaction situation between different data storage positions (the local server and the cloud storage) is shown as a graph in fig. 4, the relationship between a data center, the local server and the cloud storage is also related in the graph, an arrow points to a data flow direction, and the thickness of the arrow is the data interaction quantity.
In this embodiment, the data characteristics of each data storage location are known from the data storage mapping table, the interaction details of the local server and the data assets inside the cloud storage are known from the first asset interaction graph, and the interaction rule between the local server and the cloud storage is grasped from the second asset interaction graph. After comprehensive analysis, determining that, for example, in a new product popularization service, advertisement creative data flows from a local server to cloud storage and is combined with user behavior data, the interaction across storage positions promotes advertisement delivery accuracy and improves advertisement effect, the determination is a data interaction situation, basis is provided for further evaluating the value of data assets of the advertisement service, the value of the data situation is increased, the value is considered to be improved, the value is improved, the value of the data situation is reduced, and the value is considered to be reduced, namely the value loss.
In this embodiment, the interaction trend refers to the direction of flow from one data asset to another.
In this embodiment, the value improvement effect refers to that after one data asset interacts with another data asset, the value of the target data asset to the enterprise business is increased, for example, accurate user portrait data interacts with advertisement delivery data, the advertisement delivery effect is improved, and the value of the advertisement delivery data is further increased, and the value loss effect refers to that after the interaction, the value of the target data asset is reduced, for example, outdated market trend data interacts with product research and development data, deviation of the product research and development direction may occur, and the value of the product research and development data is reduced.
In this embodiment, the trend vector graph is to show the interaction trend between the data assets under each data type by using vector arrows, the start point of the arrow is the asset from which the data flows, and the end point is the asset from which the data flows, and through the graph, the flowing direction and the relation of the data between different assets can be intuitively seen.
In this embodiment, all data types are in the same data storage location except for the particular data type currently being analyzed. For example, if the customer data type is currently being analyzed, then the product data type, the financial data type, etc. are all of the remaining types.
In this embodiment, the first asset interaction graph reflects a graph of interactions between data assets of different data types within the same data storage location, and fully shows an interaction network of the data assets within the storage location through trend vector graphs and vector directives, as shown in fig. 3.
The technical scheme has the beneficial effects that the first asset interaction diagram is constructed based on the interaction condition of the interaction information under the same data storage position, and the second asset interaction diagram is constructed according to the interaction condition between the data storage positions, so that a convenience basis is provided for the analysis of the value of the follow-up data asset.
The invention relates to a comprehensive data asset value assessment device, the situation determination unit includes:
The initial block is used for respectively extracting a first interaction result and a second interaction result based on the same data type from the first asset interaction diagram and the second asset interaction diagram, inputting the first interaction result and the second interaction result into an interaction analysis model and obtaining an initial interaction situation;
The adjusting block is used for keeping the corresponding initial interaction situation unchanged when the compliance of the same data type set in the data storage position is larger than or equal to the preset compliance;
when the compliance of the data storage position set by the same data type is smaller than the preset compliance, the data storage position with the compliance smaller than the preset compliance is regarded as a first position;
Based on the attribute relation of each first position, and combining the compliance of the first position for the corresponding data type, adjusting the initial interaction situation;
wherein, the A value representing the adjusted data interaction situation; Ln represents a logarithmic function symbol; a constant is represented, and the value is 2.7; representing the number of first locations; a value representing an attribute relationship of the u0 th first position; indicating compliance for the x-th data type at the u 0-th first location; x represents a presettability for the xth data type; a number of positions having a value representing the attribute relationship of the first position concerned of less than 0.6; Representation of Is a factorial function of (a);
control block for according to And determining the data interaction situation from the value-situation comparison table.
In this embodiment, the interaction analysis model is obtained by training the neural network model based on two interaction results and interaction situations (value improvement, value loss, value stability) under the same type, so that the initial interaction situation can be directly obtained, as shown in fig. 5.
The corresponding values under different state potentials are different and are 0 to 2, the values are set in advance, and the preset value is 0.6.
In this embodiment, the value-situation comparison table includes values of different data interaction situations and interaction situations matched with the values, and is a known table, and the values are directly matched.
In this embodiment, the value range of the attribute relationship corresponds to 0 to 1, and the closer the attribute is, the more the corresponding value tends to 1, for example, the value of attribute a corresponds to 0.9.
In this embodiment of the present invention, the process is performed,Obtained from an initial interaction situation value corresponding table, the table takes interaction situations (value improvement, value loss, value stability and the like) as indexes to correspond to different situationsValues ranging from 0 to 2, the table specifically comprising interaction situation categories (e.g., value improvement, value loss, value stabilization), corresponding detailed descriptions (e.g., value improvement may be described as increased business gain, increased resource utilization efficiency, etc.), and correspondingAnd (5) taking a value.
In this embodiment of the present invention, the process is performed,The method comprises the steps of obtaining a first position attribute relation value corresponding table, taking a first position (a data storage position with compliance smaller than preset) as an index, and recording the attribute relation value of each position, wherein the table specifically comprises a first position identifier (such as a position number, a name and the like), a detailed description of a position attribute (such as geographical position information, equipment configuration parameters and the like) and a corresponding attribute relation value.
In this embodiment of the present invention, the process is performed,And acquiring from a data type compliance corresponding table under the first position, recording compliance values corresponding to different data types under each first position by taking the first position and the data type as indexes, wherein the table specifically comprises a first position identifier, a data type identifier (such as a data type name, a code and the like), a compliance detection standard description, an actual detection result and corresponding compliance values.
In this embodiment of the present invention, the process is performed,The first position related information, which is obtained from a related table (a first position information table related to the attribute relation value being smaller than 0.6), and used for recording the attribute relation value being smaller than 0.6, can comprise a position identifier and a corresponding attribute relation value, wherein the table specifically comprises the first position identifier, the attribute relation value and a screening identifier (marking whether the position attribute relation value is smaller than 0.6).
The technical scheme has the beneficial effects that the analysis of the situation under the same data type is continuously performed on the two interaction results based on the model, and then the situation is adjusted by judging the compliance and combining the attribute relationship, so that the latest situation is effectively obtained, and convenience is brought to the follow-up acquisition of the report.
It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present invention.
Claims (6)
1. A comprehensive data asset value assessment apparatus, comprising:
the data asset inventory module is used for automatically scanning a data system of an enterprise and identifying and classifying various types of data assets;
A data asset compliance review module for reviewing compliance of various types of data assets under an index dimension assessment system;
the data asset metering module is used for carrying out multidimensional quantitative analysis on various types of data assets and determining main value attributes of the various types of data assets;
The evaluation module is used for evaluating cost investment, future benefits and market trading value of the data assets according to the existing evaluation method;
the evaluation report module is used for determining the data interaction situation of various types of data assets in corresponding enterprises based on compliance and main value attributes, and generating a data asset evaluation report by combining evaluation results;
wherein the evaluation reporting module comprises:
The table construction unit is used for constructing a data storage mapping table based on enterprises based on the compliance and the main value attribute;
the map construction unit is used for constructing a first asset interaction map of each data storage position according to the asset interaction conditions among the data assets of different data types in each data storage position, and simultaneously constructing a second asset interaction map of an enterprise according to the asset interaction conditions among each data storage position;
The situation determining unit is used for determining the data interaction situation of the asset data of each type based on the data storage mapping table, the second asset interaction graph and all the first asset interaction graphs;
wherein, the situation determination unit includes:
The initial block is used for respectively extracting a first interaction result and a second interaction result based on the same data type from the first asset interaction diagram and the second asset interaction diagram, inputting the first interaction result and the second interaction result into an interaction analysis model and obtaining an initial interaction situation;
The adjusting block is used for keeping the corresponding initial interaction situation unchanged when the compliance of the same data type set in the data storage position is larger than or equal to the preset compliance;
when the compliance of the data storage position set by the same data type is smaller than the preset compliance, the data storage position with the compliance smaller than the preset compliance is regarded as a first position;
Based on the attribute relation of each first position, and combining the compliance of the first position for the corresponding data type, adjusting the initial interaction situation;
wherein, the A value representing the adjusted data interaction situation; Ln represents a logarithmic function symbol; a constant is represented, and the value is 2.7; representing the number of first locations; a value representing an attribute relationship of the u0 th first position; indicating compliance for the x-th data type at the u 0-th first location; x represents a presettability for the xth data type; a number of positions having a value representing the attribute relationship of the first position concerned of less than 0.6; Representation of Is a factorial function of (a);
control block for according to And determining the data interaction situation from the value-situation comparison table.
2. The comprehensive data asset value assessment apparatus of claim 1, wherein the data asset compliance review module comprises:
a capturing unit, configured to capture a security level and a user level to which each data storage location belongs, based on the data storage locations of the enterprise involved in the automatic scanning process;
the feature determining unit is used for determining privacy security features of the data storage position according to the security level and parameter setting conditions of the user level;
The mining unit is used for mining the type source of each data type in the data storage position, the data processing mode in the process from the type source to the data storage position, the data management mode after the data asset under the corresponding data type reaches the data storage position, and the data use application of the data asset under the corresponding data type;
the traceability unit is used for respectively tracing the type source, the data processing mode, the data management mode and the data use according to the privacy security feature and the legal security feature, and constructing an audit matrix based on an index dimension evaluation system , wherein,、、、An assessment vector representing a j-th data asset in the data storage location based on a type source, a data processing mode, a data management mode, and a data usage purpose, respectively;
The index dimension evaluation system is related to a quality dimension, a value dimension, a risk dimension and a management dimension.
3. The comprehensive data asset value assessment apparatus of claim 2, wherein the data asset compliance review module further comprises:
and the matrix analysis unit is used for analyzing the examination matrix and setting compliance for the data assets under the corresponding data types under the corresponding data storage positions.
4. The comprehensive data asset value assessment apparatus of claim 1, wherein the data asset metering module comprises:
The quantitative evaluation unit is used for quantitatively evaluating the data asset based on the cost, the allocation, the value reduction/increment variation and the future profit dimension of the data asset, and obtaining an evaluation coefficient based on each dimension;
the screening unit is used for screening the maximum coefficient from the evaluation coefficients under the corresponding data resources and acquiring the maximum coefficient and all influence attributes of the dimension corresponding to the maximum coefficient;
And the attribute determining unit is used for screening the attribute with the largest influence from all the influence attributes to be regarded as the main value attribute.
5. The comprehensive data asset value assessment device of claim 1, wherein the existing assessment methods comprise cost methods, benefit methods, and market methods.
6. The comprehensive data asset value assessment apparatus according to claim 1, wherein the map construction unit includes:
The first statistics block is used for counting interaction information of the same data storage position based on business of enterprises each time, wherein the interaction information is interaction trend between any two data assets and value influence between any two data assets, and the value influence comprises value improvement influence and value loss influence;
The first building block is used for determining a trend vector diagram of the data asset under each data type according to all interaction information, and building vector directions of the corresponding data type and each residual type under the same data storage position to obtain a first asset interaction diagram;
the second statistics block is used for counting the related data storage positions under each enterprise service, the related data interaction trend among the positions, the interactive data types and the data interaction quantity under the data type based on each interaction;
and the second building block is used for screening the high-frequency interaction quantity from all the data interaction quantities under the data type of each interaction related to the same data interaction trend, and building a second asset interaction graph.
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