Enterprise data asset evaluation method and system, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an enterprise data asset assessment method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a business request, determining a data asset range based on the business request, and obtaining a data asset assessment object according to the data asset range; analyzing the data asset range to obtain data features of a data asset assessment object; constructing the data asset evaluation model based on the data features of the data asset evaluation object; and in response to the service request, performing value evaluation on the data asset evaluation object based on a constructed data asset evaluation model to obtain a value evaluation value. The data asset assessment is standardized and normalized, the data asset management capability is improved, the potential value of the data is revealed, and the data asset mobility is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset evaluation, and in particular to an enterprise data asset evaluation method, system, electronic device and storage medium. Background Art

[0002] In today's digital age, data has become a key factor in promoting economic development and innovation. The digital economy is booming around the world, and its strategic layout and implementation are being promoted simultaneously. Countries have introduced digital economic development strategies, focusing on key areas such as digital technology, data elements and integrated development, accelerating the implementation of strategies through multi-departmental collaboration, and the strategic subjects are constantly enriched, and emerging economies are also actively involved. With the rapid development of the digital economy, the amount of corporate data has exploded, and data resources have become basic, strategic and leading resources. Data asset evaluation is of great significance to enterprises in optimizing the digital ecosystem and promoting the development of the digital economy. It can improve the operational and management efficiency of various business lines, assist management in analyzing the relationship between data asset value and corporate value, tap high-value data, help enterprises formulate or adjust strategies, and accelerate digital transformation.

[0003] In recent years, countries around the world have focused on the research of data assets in the fields of connotation characteristics, value management, value assessment and pricing, and value creation. However, although data assets have unique properties such as strong versatility, externalities, and inexhaustibility, their value is affected by multiple factors such as data quality and application scenarios. Although there have been many data asset transaction cases, a complete and mature data asset evaluation system and method has not yet been formed, and the existing evaluation methods all have certain applicability limitations and problems. For example, some methods may only focus on cost considerations, while ignoring the potential benefits or market value fluctuations of data in different application scenarios; or they are not accurate and comprehensive enough when evaluating the impact of the uniqueness and externalities of data on value. Therefore, there is an urgent need for an innovative method and solution that can adapt to the diversified business characteristics of enterprises, break through the limitations of a single evaluation method, enhance the comparability of evaluation results, and build a comprehensive and systematic data asset evaluation method covering the data value chain based on data element mining methods. Summary of the invention

[0004] Based on the above problems, the embodiments of the present application provide an enterprise data asset evaluation method, system, electronic device and storage medium to solve the problems existing in the above-mentioned prior art.

[0005] In a first aspect of the implementation manner of the present application, a method for evaluating enterprise data assets is provided, which includes:

[0006] Receiving a business request, determining a data asset scope based on the business request, and acquiring a data asset assessment object according to the data asset scope;

[0007] In response to the business request, a value assessment is performed on the data asset assessment object based on the constructed data asset assessment model to obtain a value assessment value.

[0008] Optionally, the data asset scope is determined based on at least one of the following dimensions: data value chain dimension, business domain dimension, and system table dimension, in the following specific manner:

[0009] The data asset scope is determined based on the data value chain dimension, and the determined data asset scope of the data value chain dimension includes at least one of original data assets, process data assets and application data assets; the data value chain is used to characterize the value of data at different stages of circulation;

[0010] When determining the data asset scope based on the business domain dimension, the determined data asset scope of the business domain dimension includes at least one of management, production, service and finance;

[0011] When determining the data asset range based on the system table dimension, the determined system table dimension data asset range includes at least one of a basic table, a code table, a report table, and an indicator table.

[0012] Optionally, the method includes: executing the following steps to construct the data asset evaluation model:

[0013] Parsing the data asset scope to obtain data characteristics of the data asset assessment object;

[0014] The data asset evaluation model is constructed based on the data characteristics of the data asset evaluation object.

[0015] Optionally, constructing the evaluation model based on the data features of the data asset evaluation object includes:

[0016] Based on the data characteristics of the data asset assessment object, generate a description of the internal measurement indicator system of the data asset;

[0017] The evaluation model is constructed based on the description of the internal measurement indicator system of the data assets.

[0018] Optionally, generating a description of an internal measurement indicator system of data assets based on the data characteristics of the data asset evaluation object includes:

[0019] Based on the data features of the data asset assessment object, the data features are dimensionally classified to obtain the dimensional classification results of the data features; the dimensional classification of the data features includes: the value of the data asset itself after governance, the use value of the data when it is applied, and the transaction value in the circulation of the data;

[0020] Based on the dimensional classification results of the data characteristics, an internal measurement indicator system for data assets is constructed to obtain a description of the internal measurement indicator system for data assets; the description of the internal measurement indicator system includes at least one of the data's own value, the data's use value, and the data's transaction value.

[0021] Optionally, constructing the evaluation model according to the description of the internal measurement indicator system of the data asset specifically includes:

[0022] Based on the description of the internal measurement indicator system of data assets, compile and process the indicators of factors affecting data asset evaluation to obtain a list of indicators of factors affecting data asset evaluation;

[0023] Based on the data characteristics of the data asset evaluation object and the internal measurement indicator system of the data assets, the data asset evaluation influencing factor indicators suitable for the data asset evaluation object are identified to obtain the data asset evaluation model of the data asset evaluation object.

[0024] Optionally, the method further comprises:

[0025] Analyze applicable evaluation scenarios according to the data asset evaluation object to obtain data asset evaluation scenarios that are suitable for the data asset evaluation object;

[0026] The data asset evaluation scenario of the data asset evaluation object and the data asset evaluation model of the data asset evaluation object are fused to obtain a data asset evaluation model adapted to the data asset evaluation scenario.

[0027] Optionally, the method further comprises:

[0028] By abstracting and reconstructing the data asset evaluation model, the basic model framework of the data asset evaluation model is obtained;

[0029] The basic model framework of the asset valuation model is split according to different data value scenarios to obtain a scenario branch valuation model based on applicable scenarios;

[0030] The data value scenarios include at least one of internal use of data, no comparable cases in the market, products with expected returns, and comparable cases in the market.

[0031] Optionally, the basic model framework of the data asset valuation model is obtained by abstractly reconstructing the data asset valuation model, which also includes taking the stage division of the value chain into consideration and selecting at least one valuation method among the cost method, the income method and the market method for application, so as to construct a basic model framework with multi-method integration and adaptability to the value chain.

[0032] Optionally, the data asset evaluation object is evaluated based on the constructed data asset evaluation model to obtain a value evaluation value, which specifically includes:

[0033] Load the data asset evaluation object into the corresponding scenario branch evaluation model, and set the evaluation factors and their corresponding weights;

[0034] An evaluation score of the data asset is calculated based on the scenario branch evaluation model, and the calculation result is output.

[0035] In a second aspect of the implementation of the present application, there is provided an enterprise data asset evaluation system, which is applied to implement the enterprise data asset evaluation method described in any one of the first aspects of the implementation of the present application, and includes:

[0036] A data asset evaluation system, the system comprising a data resource integration module and a value evaluation calculation module;

[0037] The data resource integration module is used to construct a data resource pool for data assets to be evaluated by a group enterprise, and the data resource pool is used to store data asset evaluation objects;

[0038] The value assessment calculation module is used to perform value assessment on the data asset assessment object based on the constructed data asset assessment model to obtain a value assessment value.

[0039] Optionally, the resource integration module further includes:

[0040] Data collection unit, used to obtain data resources of the enterprise;

[0041] A resource allocation unit, configured to allocate data resource management spaces corresponding to the group-type enterprise according to business fields and management levels based on the data resource pool, wherein the data resource management space includes at least one of: a headquarters, a business field, and an enterprise;

[0042] A resource storage unit, used to store data resources according to the management space of the data resources; the data resource storage level includes at least one of: a near-source layer, a governance layer, and an application layer;

[0043] The resource integration unit is used to uniformly manage and process the aggregated data to form a data resource metadata management list.

[0044] Optionally, the system further comprises:

[0045] The data value scenario analysis module is used to analyze the data value scenarios applicable to the data asset assessment objects based on the data asset assessment objects after the implementation of data governance; the data value scenarios include at least one of the following: internal use of data, no comparable cases in the market, products with expected returns, and comparable cases in the market.

[0046] Optionally, the system further comprises:

[0047] The data value scenario analysis module includes a governance factor estimation unit, an application value evaluation unit, a transaction value evaluation unit, and a model management unit;

[0048] A governance factor estimation unit, based on the data characteristics of the data asset evaluation object, analyzes the index items related to the data governance factors, parses the meaning of the index, and obtains a list of governance factor indicators;

[0049] The application value evaluation unit analyzes the index items related to the data application value factors based on the data characteristics of the data asset evaluation object, parses the meaning of the index, and obtains a list of application factor indicators;

[0050] The transaction value evaluation unit analyzes the index items related to the data transaction value factors based on the data characteristics of the data asset evaluation object, parses the meaning of the index, and obtains a transaction factor index list;

[0051] The model management unit, based on the basic model framework of the asset valuation model, derives a data asset valuation model based on applicable scenarios, and obtains a scenario branch valuation model based on applicable scenarios; the valuation method of the valuation model includes at least one of the cost method, the income method, and the market method; the subdivision of the model applicable scenarios includes at least one of the internal use of data, no comparable cases in the market, products with expected returns, and comparable cases in the market; the model valuation method for the scenario of internal use of data and no comparable cases in the market should at least include the cost method; the model valuation method for the scenario of products with expected returns and no comparable cases in the market should at least include at least two of the cost method and the income method; the model valuation method for the scenario of comparable cases in the market should at least include at least two of the cost method, the income method, and the market method.

[0052] Optionally, the value assessment calculation module includes:

[0053] An evaluation object management unit, which expands the data resource metadata content item and adds an evaluation object management item for managing the evaluation object; the evaluation object management item includes at least one of the name of the evaluation object, the evaluation unit, the creation date of the evaluation object, the adaptation scenario, and the selection model;

[0054] An adaptation scene unit, used to adapt the applicable scene of the evaluation object according to the evaluation object; the evaluation object can be adapted to one or more applicable scenes;

[0055] A model application unit, according to the applicable scenario adapted by the assessment object, associates the assessment model corresponding to the applicable scenario; based on the assessment model, adopts the assessment method to construct an assessment formula; the calculation factor in the assessment formula includes at least one item based on the governance factor indicator list, the application factor indicator list, and the transaction factor indicator list;

[0056] The model calculation unit analyzes the calculation factors corresponding to each indicator according to the evaluation object and the evaluation formula corresponding to the adapted evaluation scenario, obtains the calculation parameters and the calculation parameter values, and obtains the calculation results.

[0057] In a third aspect of the implementation manner of the present application, an electronic device is provided, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the enterprise data asset evaluation method described in any one of the embodiments of the present application.

[0058] In a fourth aspect of the implementation manner of the present application, a computer storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the enterprise data asset evaluation method described in any one of the embodiments of the present application is implemented.

[0059] The present application provides an enterprise data asset evaluation method, system, electronic device and storage medium, which have the following beneficial effects:

[0060] 1. Optimization and standardization of evaluation process

[0061] 1) Clear and specific evaluation methods: It provides group-type enterprises with a complete and clear data asset evaluation process, from accurately identifying the scope of data assets, reasonably dividing the evaluation objects, to building an adaptive evaluation model based on the evaluation objects. It effectively solves the problem of unclear evaluation methods caused by the wide range and diverse forms of data assets in group-type enterprises, so that enterprises have rules to follow when evaluating data assets.

[0062] 2) Standardized and normalized evaluation: The use of standardized and normalized data asset evaluation methods helps enterprises avoid the limitations of traditional asset evaluation and effectively ensures the accuracy and reliability of evaluation results. At the same time, it significantly improves the efficiency and comparability of data asset evaluation, making the evaluation results in different periods and different business scenarios more valuable for reference, and facilitating enterprises to conduct horizontal and vertical comparative analysis.

[0063] 2. Improvement of data asset management capabilities

[0064] 1) Comprehensive identification and optimization: With the help of this data asset assessment method, enterprises can conduct comprehensive identification, standardization and optimization of data assets with a wide range of business coverage and rich and diverse data forms. Through this process, a dynamic data asset operation closed loop is formed, realizing effective control of the entire process of data assets from acquisition to application.

[0065] 2) Work quantification and traceability: It helps enterprises to achieve quantitative evaluation of work and full traceability of the process in the process of data asset management. Enterprises can clearly understand the effectiveness of various data asset management tasks, identify problems in a timely manner and make adjustments and optimizations, thereby comprehensively improving the overall data asset management capabilities.

[0066] 3. Data value mining and decision support

[0067] 1) Revealing potential value: A systematic data asset evaluation method can deeply explore the potential value of data. By analyzing the multi-dimensional characteristics of data assets and applying adaptive evaluation models, the potential contribution of data in different business scenarios can be accurately located, providing enterprises with a comprehensive insight into the value of data.

[0068] 2) Guiding resource allocation and decision-making: Based on a clear understanding of the potential value of data, enterprises can allocate resources more scientifically and rationally, and invest limited resources in data asset-related business areas with the greatest value potential. At the same time, it provides a strong basis for the strategic decision-making of enterprises, helping them make more wise choices in market competition, which is of vital importance to the long-term development and competitiveness of enterprises.

[0069] 3. Promotion of data asset market circulation

[0070] 1) Improve asset liquidity: Reasonable data asset evaluation can effectively improve the liquidity of data assets. By accurately evaluating the value of data assets, a reliable value reference is provided for data resource entry and data transactions, breaking the value uncertainty barrier that may exist in the circulation of data assets.

[0071] 2) Promote market development: It not only opens up new valuation methods and revenue sources for enterprises, but also further promotes the rapid development of the data factor market. It promotes the efficient circulation and reasonable allocation of data among different entities, and plays a positive role in promoting the improvement and development of the digital economic ecology. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0073] Figure 1 This is a flow chart of a method for evaluating enterprise data assets according to an embodiment of the present application;

[0074] Figure 2 This is a schematic diagram of an enterprise data asset evaluation system according to an embodiment of the present application. DETAILED DESCRIPTION

[0075] The implementation of any technical solution of the embodiments of the present application does not necessarily require achieving all of the above advantages at the same time.

[0076] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] Figure 1 This is a flow chart of a method for evaluating enterprise data assets according to an embodiment of the present application. Figure 1 As shown, the enterprise data asset evaluation method includes:

[0078] Receiving a business request, determining a data asset scope based on the business request, and acquiring a data asset assessment object according to the data asset scope;

[0079] In response to the business request, a value assessment is performed on the data asset assessment object based on the constructed data asset assessment model to obtain a value assessment value.

[0080] To this end, by receiving and parsing business requests, determining the scope of data assets and the objects of assessment, we can accurately focus on the content to be assessed, making the assessment more targeted and improving work efficiency. Secondly, using the constructed data asset assessment model for value assessment can not only ensure the consistency, scientificity and objectivity of the assessment process, but also significantly improve the accuracy of the assessment results and avoid errors caused by human factors. Finally, through this process, a clear value assessment value can be obtained, which enhances the response speed and flexibility of the system, provides a quantitative basis for corporate decision-making, and helps to rationally plan resource allocation and formulate development strategies.

[0081] Optionally, the data asset scope is determined based on at least one of the following dimensions: data value chain dimension, business domain dimension, and system table dimension, in the following specific manner:

[0082] The data asset scope is determined based on the data value chain dimension, and the determined data asset scope of the data value chain dimension includes at least one of original data assets, process data assets and application data assets; the data value chain is used to characterize the value of data at different stages of circulation;

[0083] When determining the data asset scope based on the business domain dimension, the determined data asset scope of the business domain dimension includes at least one of management, production, service and finance;

[0084] When determining the data asset range based on the system table dimension, the determined system table dimension data asset range includes at least one of a basic table, a code table, a report table, and an indicator table.

[0085] In this embodiment, determining the scope of data assets from the dimension of data value chain can clearly understand the value evolution of data at different stages of development. Original data assets are the source of data. Although they have not been deeply processed, they may contain huge potential; process data assets reflect the intermediate state of data in the processing flow, which is crucial to understanding the conversion logic and value-added process of data; application data assets directly act on business practices, and their value is reflected in the support for business decisions and operations. This division helps to deeply analyze the value creation and transmission of data assets throughout the life cycle, provides a dynamic perspective for accurate evaluation, and enables enterprises to reasonably plan resource investment and value mining strategies according to the stage of data. In addition, determining the scope based on the business field dimension is closely linked to the actual operating structure of the enterprise. The data generated and relied on by different business fields (such as management, production, services and finance, etc.) are unique. Data in the management field is related to the organizational structure, personnel deployment and strategic planning of the enterprise; data in the production field involves core links such as product manufacturing process and quality control; data in the service field focuses on customer experience and service optimization; data in the financial field is closely related to capital flow, financial status, etc. Through this division, we can achieve a deep fit between data asset evaluation and business needs, provide customized evaluation results for data-driven decision-making in various business fields, and enhance the targeted support of data assets for the business. Furthermore, starting from the system table dimension, consider different types of data tables such as basic tables, code tables, reports, and indicator tables. Basic tables store the most original and basic business data and are the foundation of data assets; code tables provide a basis for data standardization and classification to ensure data consistency and accuracy; reports are an important form of data aggregation and presentation, which intuitively reflects the comprehensive situation of data; indicator tables focus on key business indicator data and play a key role in measuring business performance and data value. Determining the scope of data assets in this way facilitates in-depth analysis of the composition and characteristics of data assets from the data storage and structure level, provides a structured analysis path for subsequent evaluations, and improves evaluation efficiency and accuracy.

[0086] The management business area mainly involves the internal management, operation and IT data of petrochemical enterprises, including data assets such as human resources data, financial data, supply chain data, project management data, information infrastructure support data, etc. These data assets help enterprises realize the digitization and intelligence of internal processes, promote more efficient application of technology, and improve management efficiency and decision-making level.

[0087] The production business area mainly involves data related to production, including data assets in raw material procurement, production process, product quality control, equipment maintenance, etc. They are crucial for optimizing production processes, improving production efficiency, and ensuring product quality.

[0088] The service business area mainly involves data related to customer service and marketing, including customer data, sales data, market research data and other data assets. These data assets help companies better understand market demand, provide personalized services and enhance customer experience.

[0089] The financial business field mainly involves data related to financial transactions and fund management, including payment settlement data, financing and loan data, risk management data and other data assets. They are of great significance for ensuring the security and efficiency of financial transactions, optimizing fund allocation and reducing financial risks.

[0090] To this end, in the implementation of the data value chain dimension, this embodiment first builds a data flow monitoring system to record and mark each link of the data from the source of generation (such as sensor acquisition, user input, etc.). Through data marking technology, it is distinguished whether the data is in the original, process or application stage. For example, the original class mark is assigned to the newly collected unprocessed data, and the mark is updated to the process class according to the processing steps during the data processing process. When the data is used for a specific business application, it is marked as an application class. At the same time, a data value chain analysis model is established, and the big data analysis algorithm is used to analyze indicators such as the frequency of use of data at different stages, the number of related business applications, and the frequency of data updates, and comprehensively judge the value level of the data at each stage, so as to determine the scope of data assets in the data value chain dimension.

[0091] For the business domain dimension, establish a data classification framework that corresponds to the enterprise's business architecture. Through data docking with the enterprise's business management system (such as ERP, CRM, etc.), extract data metadata related to each business domain. For example, obtain order data, inventory data, etc. in the production field from the ERP system, and obtain customer feedback data, basic customer information, etc. in the service field from the CRM system. Then, according to the pre-set business domain classification rules, classify these data into the corresponding business domain data asset categories such as management, production, service and finance. In this process, it is also necessary to use data cleaning and integration technology to remove duplicate and erroneous data to ensure the accuracy and completeness of the data asset scope of each business domain.

[0092] In the process of implementing the system table dimension, first conduct a comprehensive scan and analysis of the enterprise's database system to identify different types of table structures such as basic tables, code tables, reports, and indicator tables. Use the metadata extraction function of the database management tool to obtain metadata information such as the name, field definition, data type, primary key and foreign key relationship of each table. For basic tables, focus on their data storage capacity, data update cycle and other characteristics; for code tables, analyze the integrity and standardization of their code system; for reports, analyze their data source tables and summary logic; for indicator tables, clarify their indicator calculation methods and data sources. Through detailed analysis and integration of these system tables, build a data asset scope model for the system table dimension, thereby determining the scope of data assets based on the system table dimension.

[0093] Optionally, the method includes: executing the following steps to construct the data asset evaluation model:

[0094] Parsing the data asset scope to obtain data characteristics of the data asset assessment object;

[0095] The data asset evaluation model is constructed based on the data characteristics of the data asset evaluation object.

[0096] In this embodiment, by analyzing the scope of data assets to obtain data features to build an evaluation model, the intrinsic value factors of data assets can be deeply explored. Accurate data feature analysis can comprehensively consider the scale, health and other characteristics of the data, so that the evaluation model closely fits the actual situation of the data assets. This helps to improve the accuracy and scientificity of the evaluation and avoid evaluation bias caused by the disconnection between the model and the data. Building a model based on data features can also enhance the adaptability of the model, and can flexibly adjust the evaluation parameters and logic for data assets of different types and industries, thereby providing enterprises with more targeted and practical evaluation results.

[0097] Optionally, parsing the data asset range to obtain data features of the data asset assessment object includes:

[0098] Determine the data storage system and business process based on the data asset scope to determine the data asset basic data table, and load the basic data table into the data resource pool to obtain the data asset set to be analyzed;

[0099] Performing data processing on the data asset set, wherein the data processing includes data cleaning, deduplication, missing value filling and data format conversion to obtain standardized data;

[0100] Parsing the standardized data to obtain the structure and content of the data set;

[0101] Based on the structure and content of the data set, obtain data characteristics of the data asset assessment object;

[0102] The data characteristics include data scale, data health, connectivity, activity, application depth, application breadth, contribution, equity, externality and economy.

[0103] In this embodiment, first, the relevant elements are determined based on the scope of data assets and data processing is performed to obtain data features, which can effectively improve data quality. Operations such as data cleaning, deduplication and missing value filling can eliminate noise and errors in the data, ensure the accuracy and completeness of the data, just like building a solid and reliable foundation for the evaluation model, reducing the evaluation bias caused by data defects. Secondly, by analyzing the scope of data assets, multi-dimensional data features are obtained, which injects rich connotations into the evaluation model. Data scale allows enterprises to understand the volume of data assets. For example, massive and continuously growing data may contain greater mining potential. Data health guarantees the credibility of data, and healthy data is more valuable in analysis and decision-making. Connectivity helps to discover the synergistic relationship between data and other business systems or data assets, and expand the application scenarios and potential value of data. Activity reflects the dynamic nature of data. Active data can often reflect business changes in a timely manner and has more timeliness advantages in market competition. Application depth and breadth can accurately locate the penetration and coverage of data in the business process of the enterprise. Deeply applied data may have a key impact on core business decisions, while data with a large breadth can play a role in multiple business areas. Contribution clarifies the direct role of data in driving business results, helping companies identify key data assets. Equity ensures the legal and compliant use and clear ownership of data assets, avoiding potential legal risks. Externality and economics consider the role of data assets in the market environment and corporate economic benefits from a macro perspective, making the evaluation results more strategically instructive.

[0104] To this end, in this embodiment, based on the determined data asset scope, the data storage system architecture and business process logic related thereto are deeply analyzed. By interactively docking with the enterprise's database management system, data warehouse, and various business application systems, the basic data tables of data assets are accurately located and extracted. These basic data tables cover various types of original data generated during the operation of the enterprise, such as customer information tables, transaction record tables, product inventory tables, etc. Subsequently, these basic data tables are loaded into the data resource pool using data integration tools. In this process, data from different data sources and different formats are preliminarily sorted according to the preset data integration rules, thereby forming a data asset collection to be analyzed. Next, comprehensive data processing work is carried out for the data asset collection, and professional data cleaning algorithms are used to identify and remove duplicate data records according to the business rules and logic of the data, such as the same customer information that may be repeatedly entered in the customer information table. At the same time, through a specific data filling algorithm, combined with the statistical laws of the data and business background knowledge, the missing values ​​in the data are reasonably filled. For example, for some data with time series characteristics, mean filling or filling methods based on trend prediction can be used. In addition, with the help of data format conversion tools, data in different formats (such as text format, date format, etc.) are uniformly converted into a standard format suitable for subsequent analysis and processing to ensure data consistency and compatibility, and finally obtain standardized data. Then, the standardized data is deeply analyzed to obtain the structure and content information of the data set. Using the data structure analysis algorithm, the name, type, length and other attributes of each data field in the data set, as well as the relationship structure between data records, are analyzed, such as whether there is a primary key and foreign key relationship in the data set, the hierarchical structure of the data, etc. At the same time, the data content in the data set is statistically analyzed to understand the distribution characteristics of the data, the range of data values ​​and other information. Finally, the data characteristics of the data asset assessment object are determined based on the structure and content of the data set.

[0105] Optionally, obtaining data features of the data asset evaluation object based on the structure and content of the data set specifically includes:

[0106] Based on the structure and content of the data set, the total number of records, the total number of fields, and the total amount of data are statistically analyzed to obtain quantitative indicators of data scale;

[0107] Based on the completeness, accuracy, consistency, and timeliness of the structure and content of the data set, the evaluation results of data health are obtained by calculating the error rate, outlier ratio, and duplicate record ratio indicators;

[0108] By identifying the association relationship between different fields and records in the data set, the association index is calculated to obtain the connectivity characteristics of the data. The association index includes the correlation coefficient between fields and the number of connections between records.

[0109] By analyzing the update frequency, access frequency, and usage frequency of the data in the dataset, and by counting the update cycle, access log, and usage record data, we can obtain the data activity index;

[0110] By evaluating the depth of data in the data set in business applications, collecting business application feedback and analyzing data usage logs, quantitative indicators of application depth are obtained; the depth of data in the data set in business applications includes the business scenarios in which the data is used and the depth of data analysis; the depth of data analysis includes data mining and machine learning model training;

[0111] By counting the number of different business scenarios, systems, departments or users in which the data set is used, and collecting data such as the scope of use and the number of users, the evaluation results of the breadth of application are obtained;

[0112] By analyzing the contribution of data to business decision-making, business growth, and risk prevention and control, and by comparing changes in business indicators before and after using the data and collecting feedback from business personnel, we can obtain data contribution indicators;

[0113] By evaluating whether the data involves user privacy, commercial secrets and sensitive information, as well as the legal compliance of data ownership and usage rights, and by reviewing the data content and consulting relevant legal documents, the rights and interests characteristics of the data can be obtained;

[0114] Analyze the impact of data on the external environment and the performance of data in terms of economic benefits, and obtain the externality and economic characteristics of data by collecting market analysis reports and financial data; the external environment includes industry, market, and policy; the economic benefits include data transactions and data service revenue.

[0115] In this embodiment, there are many significant advantages in obtaining multi-dimensional data asset data features based on the structure and content of the data set.

[0116] 1. Comprehensive and accurate assessment of data asset value

[0117] 1) Data scale quantification: The data scale quantification indicators obtained by counting the total number of records, the total number of fields and the total amount of data can enable enterprises to clearly understand the size of data assets. This provides a basic basis for evaluating the potential value of data assets, storage requirements and subsequent processing costs, and helps to rationally plan resource allocation and data management strategies.

[0118] 2) Data health assessment: Data health is assessed by calculating error rates, outlier ratios and other indicators based on the integrity and accuracy of the data set to ensure the quality of the data used is reliable. High-quality data is a prerequisite for accurate analysis and effective decision-making, avoiding deviations in assessment results due to data errors or omissions, and improving the credibility of the entire data asset assessment.

[0119] 2. Gain in-depth insights into the relationship between data assets and business

[0120] 1) Clear connectivity features: Connectivity features are obtained by identifying the associations between different fields and records in the data set and calculating the association index. This helps to reveal the relationships within the data assets and with other related data, explore the synergy of data in business processes, provide guidance for optimizing data integration and improving data utilization efficiency, and enable enterprises to better leverage the overall value of data assets.

[0121] 2) Mastering activity indicators: The activity indicators obtained by analyzing the update frequency, access frequency and usage frequency can reflect the dynamic use of data assets. Understanding the activity of data is crucial for grasping the real-time status of the business, predicting business development trends and determining the timeliness value of data, so that enterprises can adjust business strategies and data application methods in a timely manner according to data activity.

[0122] 3) Application depth and breadth considerations: Evaluate the depth of data in business applications to obtain quantitative indicators of application depth, and obtain application breadth evaluation results by counting the usage in different business scenarios and systems, which can fully present the penetration and importance of data assets in the enterprise business. This helps enterprises identify key data in core business applications, reasonably allocate resources to strengthen the support role of data for business, and also provides a reference for expanding data application scenarios.

[0123] 3. Effectively measure the contribution of data assets to business and compliance

[0124] 1) Contribution index determination: Analyze the contribution of data to business decision-making, growth and risk prevention and control, and obtain contribution indexes by comparing changes in business indicators and collecting feedback, so that enterprises can clearly understand the role of data assets in promoting business results. This is of great significance for enterprises to accurately locate valuable data assets, optimize business processes and formulate development strategies.

[0125] 2) Guarantee of rights and interests characteristics: Evaluate the rights and interests characteristics of data to ensure its legal compliance with aspects such as user privacy and commercial secrets. This can effectively protect the legitimate rights and interests of enterprises and users, avoid legal disputes caused by data rights and interests issues, and provide protection for the safe use and legal transactions of data assets.

[0126] 4. Macro-control of the external impact and economic benefits of data assets

[0127] 1) Externality and economic analysis: Analyze the impact of data on the external environment and its performance in economic benefits, collect market analysis reports and financial data to obtain externality and economic characteristics. This allows companies to examine the status and role of data assets in the industry and market from a macro perspective, reasonably evaluate the potential economic benefits of data assets, and provide a comprehensive decision-making basis for the pricing, trading and integration of data assets into the market competition environment, helping companies to better maximize the value of data assets in the digital economy era.

[0128] To this end, in the acquisition of quantitative indicators of data scale in this embodiment, first, the target data set is scanned through the query function provided by the database management system or a special data processing tool. A counting operation is performed on each record in the data set to obtain the total number of records. Next, the field definition information of the data set is traversed, and the number of fields is counted to determine the total number of fields. At the same time, the total amount of data is obtained based on the storage space occupied by the data set recorded by the database storage management module, or by accumulating the number of bytes occupied by each data element in the data set. Finally, the total number of records, the total number of fields and the total amount of data are recorded and stored as quantitative indicators of data scale for subsequent analysis.

[0129] In this embodiment, when obtaining the data health assessment results, firstly, an integrity check is performed. Through the data verification algorithm, each record and each field of the data set is traversed to check whether there are null values ​​or missing values. The number of records with missing values ​​is counted and divided by the total number of records to obtain the integrity ratio. For example, if the data set has 1000 records, of which 50 records have missing values, the integrity ratio is (1000-50) / 1000=0.95. Secondly, accuracy verification is performed, and the data in the data set is compared with a known accurate data source (such as a data sample that has been manually reviewed and confirmed to be correct), or the data is logically verified according to predefined business rules. For example, for numeric data, check whether it is within a reasonable value range; for date data, check whether its format is correct, etc. Count the number of data records that do not meet the accuracy requirements and divide it by the total number of records to obtain the error rate. And perform consistency detection to analyze the data consistency between different records in the data set and between the associated data sets. For example, in an associated data set containing customer order information and customer basic information, check whether the customer ID in the order information matches the customer ID in the customer basic information. Count the number of records with inconsistencies and calculate the ratio of the number of records to the total number of records as the consistency ratio. In addition, consider timeliness: combine the timestamp information of the data (if any) and the time period requirements related to the business to determine whether the data is within the valid time range. For example, for real-time transaction data, if it is not updated for a certain period of time, it is considered outdated data. The ratio of the number of outdated data records to the total number of records is counted as part of the timeliness index. By combining the above-mentioned integrity ratio, error rate, consistency ratio, timeliness index, etc., and performing weighted calculation according to the pre-set weights (, the final evaluation result of data health is obtained.

[0130] In this embodiment, when the data connectivity feature is obtained, first, the data set is processed using the association rule mining algorithm in the data mining and analysis tool. These algorithms can automatically identify the association patterns between different fields in the data set. For the calculation of the correlation coefficient between fields, the correlation analysis method in statistics, such as the Pearson correlation coefficient calculation method, is adopted. For each pair of fields in the data set that need to analyze the association relationship, the correlation coefficient is obtained by the corresponding calculation formula according to its data value. For example, for two numerical fields A and B, the Pearson correlation coefficient formula is used to calculate to quantify the degree of linear correlation between them. For the statistics of the number of connections between records, by traversing the data set, records with the same key attribute values ​​(such as the same customer ID, order ID, etc.) are identified, and these records with connection relationships are counted to obtain the number of connections between records. Association indicators such as the correlation coefficient between fields and the number of connections between records are sorted and stored as the connectivity features of the data.

[0131] In this embodiment, when obtaining the data activity index, the update timestamp information of each record (if any) is extracted from the data set, and the data set is scanned at a certain time interval (such as every day, every week, etc.) through a database query statement or a special data processing tool, and the number of times the data record is updated within the time interval is counted, and then divided by the total number of records in the data set to obtain the update frequency per unit time. And perform access frequency statistics, with the help of the log recording function of the database management system, obtain the log information of the data being accessed. Analyze these access logs, count the number of times the data is read or called within a certain time interval, and also divide it by the total number of records to obtain the access frequency per unit time. In addition, perform usage frequency statistics, combine the business application system's records of data usage (such as the number of calls of data in each business module), summarize the number of times the data is used in actual business applications within a certain time interval, and then divide it by the total number of records to obtain the usage frequency per unit time. Indicators such as update frequency, access frequency and usage frequency are combined and recorded and saved as data activity indicators.

[0132] In this embodiment, when the quantitative index of application depth is obtained, a business scenario analysis is performed, by collecting records about data usage in the business application system, including information about which specific business scenarios the data is used for, such as customer analysis scenarios in sales business, quality control scenarios in production business, etc. These scenarios are classified and counted to determine the application distribution of data in different business scenarios. And a data analysis depth assessment is performed, and the data usage log is analyzed to see whether the data is used for basic operations such as simple report generation and data query, or for more in-depth data analysis, such as data mining, machine learning model training, etc. The corresponding weights are set according to different application levels. For example, a higher weight is set for deep applications such as data mining and machine learning model training, and a lower weight is set for simple report generation. By comprehensively considering the application distribution of data in different business scenarios and the weights of different application levels, according to a pre-set calculation method (such as weighted summation, etc.), the quantitative index of application depth is calculated.

[0133] In this embodiment, when the application breadth evaluation results are obtained, first, information about the use of data sets in different business scenarios, systems, departments or users is collected from relevant data sources such as business application systems and department management systems. This information includes which business scenarios the data set is used in (such as the budget preparation scenario of the financial department, the marketing promotion scenario of the market department, etc.), which systems it is used in (such as enterprise resource planning system ERP, customer relationship management system CRM, etc.), which departments use it (such as sales department, R&D department, etc.), and which users use it (identified by user ID or user name). Then, the collected information is sorted and counted. The number of occurrences of the data set in different business scenarios, systems, departments and users is counted, or more complex statistical indicators are set according to business needs, such as setting weights according to the importance of different business scenarios, systems, departments and users, and then performing weighted statistics. Finally, the statistical results are recorded and saved as the evaluation results of the application breadth.

[0134] In this embodiment, when the contribution index is obtained, the business index is collected, and the business index data before and after the use of specific data, such as key business indicators such as sales, profit, and production efficiency, are collected from relevant data sources such as the business management system and financial system of the enterprise. Then the business index value after using the data is compared with the business index value before using the data. For example, if the sales increase from 1 million yuan to 1.2 million yuan after using a certain set of data, the change in sales is 200,000 yuan. Feedback collection: By issuing questionnaires to business personnel and conducting face-to-face interviews, feedback information from business personnel on the impact of data on business decisions, business growth, risk prevention and control, etc. is collected. Secondly, business personnel may mention that a certain set of data helps them predict market demand more accurately, thereby adjusting production plans in advance and improving production efficiency. Finally, the changes in business indicators and the feedback information of business personnel are comprehensively analyzed. According to factors such as the importance of business indicator changes to the business and the credibility of business personnel feedback, corresponding weights are set, and then the contribution index of data to the business is calculated according to calculation methods such as weighted summation.

[0135] In this embodiment, when the equity characteristics are obtained, the content of the data set is reviewed in detail by manual review or using data content analysis tools. Check whether the data involves user privacy (such as personal identity information, contact information, etc.), commercial confidential sensitive information (such as the company's core technical parameters, undisclosed marketing strategies, etc.). And conduct a legal document review, review relevant legal and regulatory documents, such as data protection laws, intellectual property laws, etc., as well as documents on data ownership and usage rights formulated within the enterprise. Determine the legal compliance requirements of the data in terms of user privacy, commercial secrets, etc. According to the review results and the provisions of the legal documents, the equity characteristics of the data are judged and classified. For example, if the data does not involve any user privacy and commercial secrets issues, and complies with the internal regulations of the enterprise on data ownership and usage rights, the equity characteristics can be judged as compliant; if the data involves user privacy but has been processed in accordance with relevant legal requirements (such as anonymization), the equity characteristics can be judged as partially compliant, etc. The judgment results are recorded and saved as the equity characteristics of the data.

[0136] In this embodiment, when acquiring externality and economic characteristics, market analysis reports are collected, and market analysis reports related to the industry and market where the enterprise is located are collected from professional market research institutions, industry associations and other channels. These reports usually contain information such as industry development trends, market competition patterns, and changes in market demand. At the same time, financial data is collected, and financial data related to data assets are collected from the financial system of the enterprise, such as the acquisition cost, storage cost, and processing cost of data assets, as well as the direct or indirect economic benefits brought by data assets (such as additional income brought by data-driven business growth). Then analyze and integrate, and analyze and integrate the collected market analysis reports and financial data. By analyzing the market analysis report, evaluate the impact of data on the external environment (such as industry, market, and policy), for example, whether the data has promoted technological innovation in the industry, whether it has changed the market competition pattern, etc. By analyzing the financial data, calculate indicators such as the acquisition cost, storage cost, processing cost, and economic benefits of data assets, and then evaluate the performance of the data in terms of economic benefits. The analysis results are recorded and saved as the externality and economic characteristics of the data.

[0137] Optionally, constructing the evaluation model based on the data features of the data asset evaluation object includes:

[0138] Based on the data characteristics of the data asset assessment object, generate a description of the internal measurement indicator system of the data asset;

[0139] The evaluation model is constructed based on the description of the internal measurement indicator system of the data assets.

[0140] In this embodiment, by generating a description of the internal measurement index system of data assets based on the data characteristics of the data asset evaluation object, the characteristics of data assets in different dimensions can be deeply explored. These characteristics are the unique performances of data assets in the process of enterprise business operations. The description of the measurement index system constructed in this way can accurately capture the essential characteristics of data assets, ensure that the evaluation model is closely aligned with the real state of data assets, and avoid the situation where the model is out of touch with the actual data, thereby improving the accuracy of the evaluation. In addition, the generated internal measurement index system description covers the data characteristics of data assets in many aspects, including data scale, health, connectivity, etc. This enables the value factors of data assets in different dimensions to be fully considered when constructing the evaluation model. Since the evaluation model is constructed based on the specific data asset data characteristics, different data assets will present different data feature combinations due to their own characteristics. This feature-based model construction method makes the evaluation model more flexible and can automatically adjust the evaluation parameters and logic according to different types of data assets. Regardless of the data assets in any industry or business scenario, an adaptive evaluation model can be constructed by analyzing its specific data characteristics, thereby improving the versatility and applicability of the model.

[0141] Optionally, generating a description of an internal measurement indicator system of data assets based on the data characteristics of the data asset evaluation object includes:

[0142] Based on the data features of the data asset assessment object, the data features are dimensionally classified to obtain the dimensional classification results of the data features; the dimensional classification of the data features includes: the value of the data asset itself after governance, the use value of the data when it is applied, and the transaction value in the circulation of the data;

[0143] Based on the dimensional classification results of the data characteristics, an internal measurement indicator system for data assets is constructed to obtain a description of the internal measurement indicator system for data assets; the description of the internal measurement indicator system includes at least one of the data's own value, the data's use value, and the data's transaction value.

[0144] In this embodiment, by dimensional classification of data features, it is divided into dimensions such as the value of data assets after governance, the use value of data applications, and the transaction value in data circulation, which can clearly define the value performance of data assets in different stages and scenarios. This enables enterprises to have a more intuitive and in-depth understanding of the value composition of data assets, no longer vaguely looking at the overall value of data assets, but can accurately understand the specific value contained in its own characteristics, practical applications, and market transactions, which helps enterprises better grasp the core value of data assets. In addition, the description of the internal measurement indicator system of data assets constructed based on the dimensional classification results covers many aspects such as the value of the data itself, the use value, and the transaction value. This comprehensiveness ensures that no important value dimensions are omitted when evaluating data assets, and can comprehensively consider the value factors of the entire process of data assets from internal governance to external applications and transactions. Furthermore, the clear classification of value dimensions and the corresponding description of the measurement indicator system provide a strong basis for enterprises in resource allocation and decision-making. Enterprises can allocate resources in a targeted manner according to the performance of data assets in different value dimensions. For example, if the application value of a certain type of data asset is high, the enterprise can increase its investment in the development of relevant application scenarios and data mining; if its transaction value potential is large, it can invest more resources in market promotion, transaction compliance, etc. At the same time, when making decisions such as the retention and abandonment of data assets, data sharing and cooperation, more scientific and reasonable choices can be made based on these clear value analyses to improve the operational efficiency of enterprises and the utilization efficiency of data assets. At the same time, with the continuous changes in the market environment and the development and evolution of data assets themselves, the value performance of data will also change accordingly. This description of the measurement indicator system constructed according to different value dimensions has strong adaptability and can be flexibly adjusted according to market dynamics and new situations of data assets. For example, when the market demand for transactions of a certain type of data increases, by paying attention to and updating the indicators related to the transaction value dimension, the new value of data assets in market transactions can be reflected in a timely manner, thereby helping enterprises better adapt to market changes, seize opportunities for data asset value enhancement, and maintain competitive advantages in the data economy era.

[0145] To this end, in this embodiment, when classifying the self-value dimension after data asset governance, data health characteristics, such as accuracy, completeness, consistency and other indicators, are classified as the self-value dimension after data asset governance. Data with high accuracy scores higher in the self-value dimension. For example, by calculating the proportion of erroneous data, data with an error rate below a certain threshold (such as 5%) can be given a higher self-value weight. The amount of data in the data scale is also related to its own value. Large-scale data sets may contain more potential value, and different self-value coefficients can be set according to the classification of data volume (such as small, medium, and large data volumes). The equity characteristics of data are also reflected in this dimension. If the ownership of the data is clear and there is no legal risk, its own value is more secure, and the corresponding equity compliance indicator weights can be set, and compliance will be given a higher self-value score.

[0146] In this embodiment, when classifying data according to the use value dimension when applying data, the data is classified according to the application depth and application breadth characteristics. For application depth, if the data is used for complex data mining or machine learning model training and has a significant impact on business decisions, such as measuring the application depth value by the improvement in model accuracy, the greater the improvement, the higher the score in the use value dimension. In terms of application breadth, the scope of use of statistical data in different business departments, business systems and business scenarios, the wider the scope of use, the higher the use value. For example, data that has applications in multiple core business departments (such as sales, marketing, and R&D) will have a higher weighting coefficient in the use value dimension than data that is only used in a single department. The update frequency, access frequency, and use frequency in the activity feature are also closely related to the use value. A high update frequency means that the data is highly timely and can better reflect the current business situation. The corresponding use value weight can be set according to different update frequency intervals; high access and use frequencies indicate the importance of data in business processes, which can also be converted into use value scores through frequency statistical data.

[0147] In this embodiment, when classifying the transaction value dimension in data circulation, externality characteristics are important in the transaction value dimension. If the data has a leading role in industry trends or has a great impact on the market competition pattern, such as data that can change the formulation of industry standards, it can be given a higher evaluation in the transaction value dimension. The impact of externalities on transaction value can be quantified by analyzing the correlation between data in industry reports and changes in market share. Economic characteristics are the core consideration of transaction value. Calculate the acquisition cost, storage cost, processing cost, and potential transaction income or data service income of data assets. For example, data with low acquisition and maintenance costs and higher potential transaction income scores higher in the transaction value dimension. The transaction value coefficient is calculated through a cost-benefit analysis model and included in the classification results of the transaction value dimension.

[0148] In this embodiment, when constructing the internal measurement index system of data assets, a data accuracy index is established, which is quantified by the proportion of erroneous data obtained by the data verification algorithm. The lower the error rate, the higher its own value. For example, the data accuracy index score with an error rate of 0 can be set to 100, and the score decreases accordingly for every 1% increase in the error rate. A data integrity index is also established, which is measured by the proportion of missing data. The lower the missing ratio, the higher the integrity index score. If the missing ratio is less than 10%, a higher integrity score is given, and the score decreases as the missing ratio increases. The data scale index is divided into levels according to the size of the data volume. For example, the number of data records above 1 million is large-scale, 500,000-1 million is medium-scale, and less than 500,000 is small-scale, corresponding to different scale value coefficients, and the large-scale coefficient is higher than the medium and small. Secondly, the use value index is determined, including the establishment of an application depth index, which is set according to the data analysis level involved in the business application and the degree of influence on business decisions. For example, the application depth index score is low for simple report generation, while the data application depth index score is high for deep data mining and major adjustments to business strategies. The score can be determined by combining expert evaluation and business results quantification. Application breadth index: statistics on the application scope of statistics in different business fields and user groups, measured by the number of business fields covered and the proportion of users. For example, the application breadth index that covers more than 5 business fields and the user usage ratio exceeds 30% has a higher score, and vice versa. Activity index: calculates the activity score by combining update frequency, access frequency and usage frequency. For example, if the update frequency exceeds 10 times per month, the access frequency exceeds 50 times per week, and the usage frequency exceeds 100 times per day, a higher activity score will be given, and the corresponding scoring formula will be set according to different frequency combinations. Secondly, the transaction value index is determined, and the externality index is analyzed to analyze the influence of data on the industry, market and policy. For example, industry experts evaluate the role of data in promoting industry innovation, and divide it into three levels of high, medium and low according to the degree of promotion, corresponding to different externality scores. At the same time, analyze the differentiated advantages of data in market competition, and give higher externality scores to those with obvious advantages. Economic indicators: accurately calculate the costs and benefits of data assets. Costs include the total costs of purchasing data collection equipment, labor costs, storage equipment leasing, etc., and benefits consider direct data transaction income, additional profits brought by data-driven business growth, etc. The transaction value score is determined by the cost-benefit ratio. The higher the cost-benefit ratio, the higher the transaction value. Finally, the determined self-value, use value and transaction value related indicators and their weights are integrated to form a description of the internal measurement indicator system of data assets.For example, the weight of the self-value index is 30%, the weight of the usage value index is 40%, and the weight of the transaction value index is 30%. The scores of the data on each indicator are multiplied by the corresponding weights and then summed up to obtain a comprehensive score described by the internal measurement index system of the data asset. This will comprehensively reflect the value status of the data asset and provide an accurate data basis for the subsequent construction of the evaluation model.

[0149] Optionally, constructing the evaluation model according to the description of the internal measurement indicator system of the data asset specifically includes:

[0150] Based on the description of the internal measurement indicator system of data assets, compile and process the indicators of factors affecting data asset evaluation to obtain a list of indicators of factors affecting data asset evaluation;

[0151] Based on the data characteristics of the data asset evaluation object and the internal measurement indicator system of the data assets, the data asset evaluation influencing factor indicators suitable for the data asset evaluation object are identified to obtain the data asset evaluation model of the data asset evaluation object.

[0152] In this embodiment, by compiling a list of data asset evaluation influencing factor indicators based on the description of the internal measurement indicator system of data assets, various influencing factors closely related to data asset evaluation can be comprehensively and meticulously sorted out. These influencing factors are extracted from the multi-dimensional characteristics of the data assets themselves, so the indicators in the list can accurately correspond to the actual situation of the data asset evaluation object. In addition, when identifying the data asset evaluation influencing factor indicators adapted to the data asset evaluation object to construct the evaluation model, the model is further ensured to be highly consistent with the specific evaluation object. Different data asset evaluation objects may have differences in data feature performance, such as some data assets are outstanding in application value, while others may have more potential in transaction value. This construction method can accurately screen out the influencing factor indicators that match each evaluation object according to its unique data characteristics, so that the constructed evaluation model can accurately reflect the true value of the object, avoiding the problem that the general model may not match the specific object and the evaluation result is inaccurate. In addition, the description of the internal measurement indicator system of data assets covers the multi-dimensional value information of data assets from its own value after governance, the use value during application to the transaction value in circulation. Building an evaluation model based on this means that the impact of these different dimensional value factors on data asset evaluation will be fully considered during the model building process. As the business of the enterprise develops and the market environment changes, the data assets themselves will continue to change, and their value performance and data characteristics may also change accordingly. The method of building an evaluation model based on the description of the internal measurement indicator system of data assets has strong flexibility.

[0153] To this end, this embodiment first deeply analyzes the various parts of the description of the internal measurement indicator system of data assets, including the specific indicators involved in the dimensions of data's own value, use value, and transaction value. For example, the indicators under the dimension of own value may include data accuracy, completeness, data scale, etc.; the dimension of use value may include indicators such as application depth, application breadth, and activity; the dimension of transaction value involves indicators such as externality and economy. The indicators of these different dimensions are further classified according to their nature and function, for example, they can be divided into different categories such as basic data feature indicators (such as data scale, data type, etc.), value contribution indicators (such as the contribution of application depth to business decisions, economic benefits brought by data transactions, etc.), and quality-related indicators (such as data accuracy, completeness, etc.), so as to sort out the influencing factor indicators more clearly in the future. And conduct an influencing factor correlation analysis: for each type of indicator, analyze its potential impact relationship with data asset evaluation. For example, for the data accuracy indicator, consider how it affects the credibility of data assets and thus affects the evaluation results; for the application depth indicator, analyze its impact on reflecting the importance of data to business applications and its role in value evaluation. Through this correlation analysis, we can determine the positive or negative impact of each indicator in the evaluation process. For example, high data accuracy is usually a positive factor, which makes the evaluation results more reliable; while low activity may be a negative factor, suggesting that the value utilization of data assets may be low.

[0154] Based on the above indicator classification and the results of the correlation analysis of influencing factors, all relevant indicators and their corresponding influencing factors are sorted out to compile a list of data asset evaluation influencing factor indicators. Based on the data characteristics of the data asset evaluation object and the internal measurement indicator system of data assets, the data asset evaluation influencing factor indicators suitable for the data asset evaluation object are identified to obtain the data asset evaluation model of the data asset evaluation object.

[0155] In this embodiment, for a specific data asset evaluation object, its determined data features are analyzed in detail. These data features include the specific conditions mentioned above, such as data scale, health, connectivity, activity, etc. At the same time, combined with the internal measurement index system of data assets, the data features of the evaluation object are matched and analyzed one by one with each indicator in the index system. For example, if the data of the evaluation object has a high activity (such as high update frequency, high access frequency, and high use frequency), then the corresponding activity index and other indicators of the relevant use value dimension (such as application depth, application breadth, etc., because activity is often related to the application situation) are found in the index system to judge the importance and relevance of these indicators to the evaluation object. In the process of screening and determining the influencing factor indicators, the influencing factor indicators that are most suitable for the data asset evaluation object are screened out according to the results of the above matching analysis. The principle of screening is to consider both the matching degree of the indicator and the data characteristics of the evaluation object, and its importance in the business scenario of the evaluation object, the purpose of data application, etc. For example, for a data asset evaluation object mainly used for marketing business, its application breadth index may be more important than some other indicators (such as data quality related indicators that are more concerned in the production link). Therefore, the application breadth and related influencing factor indicators (such as activity, market feedback, etc.) will be considered during the screening. Through such a screening process, a set of key influencing factor indicators for the specific evaluation object is determined. After determining the influencing factor indicators suitable for the data asset evaluation object, a data asset evaluation model is constructed based on these indicators. First, select a suitable evaluation method. According to the evaluation methods, including cost method, income method, market method and other methods, one or more of them can be selected and used in combination according to the characteristics of the evaluation object and business needs. For example, if the data assets of the evaluation object are mainly used within the enterprise and there are no comparable cases in the market, it may be more suitable to adopt an evaluation model based on the cost method; if the data assets have clear expected returns and there are comparable cases in the market, the income method and the market method may be used in combination. Then, according to the selected evaluation method and the determined influencing factor indicators, set the parameters and calculation formula of the model. Taking the cost method as an example, if the selected data asset evaluation influencing factors include data acquisition cost, storage cost, data scale, etc., then the evaluation model can be set as: evaluation value = data acquisition cost + storage cost + (data scale coefficient × unit data value), where the data scale coefficient and unit data value can be determined based on industry experience and data analysis. In this way, a data asset evaluation model for a specific data asset evaluation object can be constructed to facilitate accurate value evaluation in the future.

[0156] Optionally, the method further comprises:

[0157] Analyze applicable evaluation scenarios according to the data asset evaluation object to obtain data asset evaluation scenarios that are suitable for the data asset evaluation object;

[0158] The data asset evaluation scenario of the data asset evaluation object and the data asset evaluation model of the data asset evaluation object are fused to obtain a data asset evaluation model adapted to the data asset evaluation scenario.

[0159] In this embodiment, by analyzing the applicable evaluation scenarios according to the data asset evaluation object, it is possible to deeply understand the specific needs of the object under different business scenarios, market environments and data usage purposes. For example, for some data assets mainly used for internal decision support of the enterprise, the applicable evaluation scenarios may focus on cost considerations and value contribution to internal business processes; while for data assets planned to be put on the market for trading, the evaluation scenarios focus more on factors such as market comparability and expected returns. And the evaluation scenario of the data asset evaluation object is integrated with the corresponding evaluation model, further ensuring that the final adaptation model can accurately fit the actual situation of the object in a specific scenario. In this way, it can avoid using a general model and ignoring the particularity of the evaluation object and scenario, thereby improving the accuracy and pertinence of the evaluation results, and providing enterprises with data asset evaluation basis that is more in line with actual needs in different business decision-making scenarios. At the same time, in the process of analyzing the evaluation scenario, many factors will be comprehensively considered, such as the source of data assets (whether it is generated internally by the enterprise or purchased externally), the scope of application of data (only internal use or involving market transactions, etc.), the market environment (whether there are comparable cases, market competition situation, etc.) and business needs (such as for optimizing production processes, expanding market share, etc.). This comprehensive consideration of multiple factors enables the constructed model adapted to the data asset evaluation scenario to incorporate all these factors into the evaluation system. For example, during the integration process, if the evaluation scenario involves market transactions and there are comparable cases, then market-related factors can be better integrated into the model, such as the price of comparable cases, market supply and demand relationships, etc.; if it is an internal use scenario, the model can pay more attention to cost-related factors, such as data acquisition costs, maintenance costs, etc. In this way, the impact of various factors on data asset evaluation is comprehensively and systematically considered, which improves the scientificity and rationality of the evaluation.

[0160] Optionally, the method further comprises:

[0161] By abstracting and reconstructing the data asset evaluation model, the basic model framework of the data asset evaluation model is obtained;

[0162] The basic model framework of the asset valuation model is split according to different data value scenarios to obtain a scenario branch valuation model based on applicable scenarios;

[0163] The data value scenarios include at least one of internal use of data, no comparable cases in the market, products with expected returns, and comparable cases in the market.

[0164] Optionally, the basic model framework of the data asset valuation model is obtained by abstractly reconstructing the data asset valuation model, which also includes taking the stage division of the value chain into consideration and selecting at least one valuation method among the cost method, the income method and the market method for application, so as to construct a basic model framework with multi-method integration and adaptability to the value chain.

[0165] In this embodiment, a variety of evaluation methods are integrated. In the process of constructing the basic model framework of the data asset evaluation model, at least one evaluation method among the cost method, the income method and the market method is selected and applied, realizing the integration of multiple methods. The cost method can consider the cost input of data assets in terms of acquisition, storage, processing, etc. from the perspective of the cost of data assets, which is of great significance for evaluating the basic value of data assets; the income method focuses on the economic benefits that data assets may bring in the future, and quantifies the expected benefits into the current value through discounting and other methods, which can well reflect the profit potential of data assets; the market law evaluates the value of data assets based on the price and other information of comparable cases in the market, and can fully draw on the supply and demand relationship of the market and the transaction of similar assets. By integrating these evaluation methods with different focuses, the basic model framework can comprehensively and comprehensively consider all aspects of the value of data assets, avoiding the limitations that may exist in a single evaluation method, thereby providing a guarantee for more accurate and comprehensive evaluation. In addition, combining the value chain stage division and taking the value chain stage division into consideration at the same time, it means that the model framework can be constructed according to the characteristics and value changes of data at different business process stages (such as data generation, collection, processing, application, etc.). Data assets at different stages may have different value contributions. For example, in the data generation stage, it may mainly involve cost investment, while in the application stage, it may focus more on the benefits it brings or the impact on the business. By combining with the value chain stage division, the basic model framework can accurately capture the value evolution of data assets in various business links, further enrich the evaluation dimension, and enable the evaluation results to more truly reflect the comprehensive value of data assets in the entire business process. In addition, by abstracting and reconstructing the data asset evaluation model, a basic model framework with multi-method integration and value chain adaptation characteristics is obtained, which actually creates a general and widely applicable basic framework. This framework is no longer limited to specific data asset evaluation objects or scenarios, but extracts the core logic and key elements of evaluating the value of data assets. Regardless of the industry or type of data assets, as long as value evaluation is involved, it can be further refined and expanded based on this basic model framework, which greatly improves the versatility and adaptability of the model and lays a solid foundation for the subsequent construction of branch evaluation models according to different specific scenarios. Furthermore, based on this basic model framework, it becomes more convenient and flexible to split the scenario branch evaluation model according to different data value scenarios (such as internal use of data, no comparable cases in the market, products with expected returns, comparable cases in the market, etc.). Since the basic model framework has comprehensively considered factors such as multiple evaluation methods and value chain stage division, when splitting and customizing for specific scenarios, it is only necessary to appropriately adjust the relevant parameters in the framework, the weights of the evaluation methods, etc. according to the specific needs of the scenario.For example, for the internal use scenario of data, the application of the cost method may be more emphasized. At this time, the weight of the cost method in the branch evaluation model of the scenario can be increased on the basis of the basic model framework; for scenarios with comparable cases in the market, more factors related to the market method can be used for customization. This method enables the model to quickly adapt to various evaluation scenarios and meet the needs of enterprises for data asset evaluation in different business situations. Finally, based on the basic model framework with multi-method integration and value chain adaptation characteristics constructed above, and the scenario branch evaluation model derived from it, it can provide scientific and reasonable evaluation results for enterprises. When enterprises make decisions related to data assets, such as the retention and abandonment of data assets, pricing, cooperation and sharing, these accurate evaluation results can serve as important decision-making basis. For example, through evaluation, the value of a data asset at the current value chain stage and its potential value in different market scenarios can be known. Enterprises can judge whether the data asset is worth continuing to invest resources for maintenance or development, or determine a reasonable price range when trading, so as to make more scientific and reasonable decisions and improve the efficiency and benefits of enterprises in data asset operation and management.

[0166] To this end, the construction of data asset evaluation models and branch models is mainly divided into two key steps: one is to abstract and reconstruct the basic model framework, and the other is to split and build scenario branch evaluation models.

[0167] When constructing the basic model framework, we first deeply analyze the existing various evaluation models, determine the key elements such as the characteristics of data assets themselves, business applications, markets and costs, and sort out their roles and relationships in different evaluation scenarios. Then, we divide the value chain stages according to the business processes of the enterprise, clarify the characteristics and value changes of data in each stage, such as focusing on costs in the collection stage and focusing on income-related factors in the analysis and application stage, etc., integrate these with key elements to fully present the value evolution of data assets in the value chain. Then, we select and integrate at least one of the cost method, income method, and market method. The cost method carefully analyzes the cost of the entire process of data assets to build a calculation model and assign weights. The income method predicts future income and discounts it to determine the calculation model. The market method collects comparable case data to build a model to determine the correction coefficient. Finally, according to the status of data assets and evaluation needs, the weights of each method in the framework are assigned to obtain the calculation formula of the basic model framework.

[0168] When constructing a scenario branch evaluation model, first determine the characteristics and requirements of different data value scenarios (internal use of data, no comparable cases in the market, products with expected returns, and comparable cases in the market). The internal use scenario of data focuses on application value and cost, highlights the cost method and application-related factors, and adjusts the weights; the scenario with no comparable cases in the market relies on cost and expected returns, dominates the cost method and the return method, and adjusts the calculation model; the scenario with expected return products focuses on the return method and takes into account costs, and adjusts the calculation model according to the characteristics of the returns; the key to the scenario with comparable cases in the market is the market method and combines the other two methods to strengthen the analysis of comparable cases and adjust the calculation models of each method. Based on these, the basic model framework is split according to the scenario, the relevant elements of each method are extracted, and they are recombined according to the scenario weight distribution to construct a calculation formula for the branch model of the scenario with comparable cases in the market. Repeat the operation to construct the branch evaluation model of each scenario to adapt to the data asset evaluation needs of different scenarios.

[0169] Optionally, the data asset evaluation object is evaluated based on the constructed data asset evaluation model to obtain a value evaluation value, which specifically includes:

[0170] Load the data asset evaluation object into the corresponding scenario branch evaluation model, and set the evaluation factors and their corresponding weights;

[0171] An evaluation score of the data asset is calculated based on the scenario branch evaluation model, and the calculation result is output.

[0172] In this embodiment, by loading the data asset evaluation object into the corresponding scenario branch evaluation model, it is possible to ensure that the evaluation process is highly consistent with the specific scenario in which the evaluation object is located. Different data assets face different situations in actual applications, such as some are mainly used for internal operations of the enterprise, while others involve market transactions and have comparable cases. This targeted model selection can fully take into account the special factors in each scenario, making the evaluation more targeted, avoiding the inaccuracy that may be caused by the general evaluation model, and thus more accurately reflecting the true value of the data asset evaluation object in a specific scenario. In addition, the link of setting the evaluation factors and their corresponding weights gives the evaluation process great flexibility. Enterprises can independently determine which factors have a greater impact on the value assessment based on the specific characteristics of the data asset evaluation object and the evaluation objectives, and then reasonably set the evaluation factors and their weights. For example, for an evaluation object that focuses on the breadth and depth of data application to reflect the value, the weights of relevant evaluation factors such as application depth and breadth can be appropriately increased. This flexible customization method can better adapt to the diverse characteristics of different data assets and make the evaluation results more in line with the actual situation. At the same time, calculations and output results based on the scenario branch evaluation model ensure the efficiency of the calculation process on the one hand. Since the model is built for a specific scenario, its calculation logic and parameter settings have been optimized, and the evaluation score can be obtained quickly and accurately. On the other hand, the clear and unambiguous output results provide intuitive data support for enterprises, making it easier for enterprise managers, decision makers and other relevant personnel to quickly understand the value status of the data asset evaluation object, thereby providing a strong basis for subsequent data asset operation and management decisions (such as whether to trade, how to price, resource allocation, etc.), which helps to improve the scientific nature of enterprise decision-making and operational efficiency in the field of data assets. Finally, in the entire evaluation process, from selecting a suitable scenario branch evaluation model, to setting evaluation factors and weights, to the final calculation output, it is actually a comprehensive consideration of the various value factors of data assets. It not only takes into account the special needs in different scenarios, but also takes into account the various characteristics of the data assets themselves and the relative importance of each value influencing factor, so that the final value evaluation value can fully and accurately reflect the overall value of the data asset evaluation object, providing a reliable guarantee for enterprises to fully grasp the value of data assets.

[0173] Optionally, the data asset evaluation influencing factor indicators are compiled based on the description of the data asset internal measurement indicator system to obtain a data asset evaluation influencing factor indicator list including:

[0174] Analyze the classification and sub-indicators of each indicator in the description of the internal measurement indicator system of data assets;

[0175] Determine the classification basis of influencing factor indicators based on the scope of data assets;

[0176] According to different classification bases, key indicators are selected from the internal measurement indicator system to form a subset of asset evaluation influencing factors;

[0177] Integrate the asset evaluation influencing factor indicator subsets to obtain a data asset evaluation influencing factor indicator list;

[0178] The data asset evaluation influencing factor indicator list is used to selectively use the calculation factors according to specific applicable scenarios when calculating the data asset evaluation model.

[0179] In this embodiment, the technical benefits brought by the step of compiling a list of indicators of factors affecting data asset assessment are as follows:

[0180] 1. Comprehensively sort out and accurately locate key factors

[0181] By analyzing the classification and sub-indicators of each indicator in the description of the internal measurement indicator system of data assets, we can comprehensively and meticulously sort out the various indicators involved in the value assessment of data assets and their internal relationships. This helps to deeply understand the characteristics of data assets in different dimensions (such as their own value, use value, transaction value, etc.), and lays the foundation for the subsequent precise positioning of key influencing factors. For example, during the analysis process, it can be clearly identified which indicators have a greater impact on the quality and credibility of data assets themselves, and which indicators mainly reflect their value contribution in practical applications, etc., so as to avoid missing important value considerations and make the evaluation more comprehensive.

[0182] 2. Targeted screening based on asset range

[0183] Determine the classification basis of influencing factor indicators according to the scope of data assets, so that indicator screening is more targeted. Different data asset scopes (such as different business fields, different data types, etc.) often have different characteristics and value focuses. Taking financial industry data assets and manufacturing industry data assets as examples, their core business needs and data application scenarios are quite different, and the influencing factor indicators they focus on will also be different. This classification and screening method based on the scope of assets can ensure that the selected influencing factor indicators are closely matched with the specific data asset scope, more in line with the actual business situation, and improve the applicability and accuracy of the indicator list for the evaluation of different data assets.

[0184] 3. Focus on key indicators to improve evaluation efficiency and accuracy

[0185] According to different classification bases, key indicators are selected from the internal measurement indicator system to form a subset of asset evaluation influencing factors. This step effectively focuses on the indicators that play a key role in data asset evaluation. Among the many measurement indicators, not all indicators are equally important for each evaluation. By screening key indicators, some relatively minor or less relevant indicators can be removed to simplify the evaluation process. For example, for a data asset that is mainly used for internal decision support, key indicators such as application depth and data accuracy may be more important for its value assessment, while other indicators related to market transactions can be appropriately weakened. This not only improves the calculation efficiency of the evaluation, but also more accurately highlights the core factors that affect the value of data assets and improves the accuracy of the evaluation results.

[0186] 4. Integrated checklist provides flexible assessment support

[0187] By integrating the subset of indicators of asset valuation influencing factors, we can obtain a list of indicators of data asset valuation influencing factors, which provides a flexible and comprehensive basis for the calculation of data asset valuation models. In different applicable scenarios (such as internal use of data, market transactions, etc.), calculation factors can be selected for evaluation in a targeted manner based on the indicators in the list. For example, in the scenario of internal use of data, calculation factors related to cost control and internal application effects may be mainly selected; while in the scenario of market transactions, calculation factors related to market comparability, expected returns, etc. will be selected. This flexibility enables the evaluation model to better adapt to the needs of various specific scenarios and provide enterprises with more practical data asset evaluation services in different business decision-making scenarios.

[0188] 5. Improve the scientificity and standardization of the overall evaluation system

[0189] The entire compilation process mentioned above, from comprehensive analysis of indicators to targeted screening of key indicators and then to integrated lists, makes the formation of the list of indicators of factors affecting data asset evaluation rigorously logical and scientific. It standardizes the selection and use of influencing factor indicators in the process of data asset evaluation, avoiding arbitrariness and subjectivity. This helps to improve the scientificity and standardization of the entire data asset evaluation system, so that different companies and different personnel can follow unified standards and processes when conducting data asset evaluation, improve the comparability and credibility of evaluation results, and provide a more reliable basis for reasonable pricing, trading, management and other decisions of data assets.

[0190] In one embodiment of the present application, since data is usually obtained by internal collection or external purchase, explicit or implicit costs will be incurred. Therefore, the cost method will be applied to the evaluation methods of data assets at all stages; some data products (data assets with expected returns generally exist in the form of data products, so this section uses data products instead of data assets with expected returns) have expected returns, but there are no comparable cases in the market. When evaluating the value of data assets, it is appropriate to apply the cost method and the income method; for products with comparable cases in the market, the cost method, income method, and market method can be combined with the similarity of the prices of the comparable case reference system for comprehensive evaluation. When the market comparability is very good, the product has good market prospects, and is used internally at the same time, the evaluation results of the market method can be added to the evaluation results of the other two methods to obtain the superimposed data asset value.

[0191] Therefore, the above formula is broken down into scenarios as shown in Table 1-1.

[0192] Table 1-1 Comprehensive evaluation method scenario decomposition

[0193]

[0194] This embodiment also provides a data asset value assessment optimization cost method, and the optimized cost method formula is as follows:

[0195] P=HC×S×δ×(1+R×U)

[0196] Among them, P is the evaluation result, HC is the historical cost of data assets, S is the reset coefficient, δ is the depreciation coefficient of the historical cost of data assets, R is the reasonable profit rate of data assets, and U is the profit adjustment coefficient.

[0197] A comprehensive evaluation method is used to design a calculation framework for data asset costs, and to classify stages and costs. A variety of methods of investment return rates are used to study and determine reasonable returns. The measurement statistics method, Delphi method, and analogy method are used to construct the dimensions and indicators of the model. The analytic hierarchy process method is used to determine the indicator weights and form quantitative and qualitative scoring standards. Depreciation factors are determined based on the life cycle of data assets.

[0198] In this example, the data asset value assessment optimization benefit method provided is as follows:

[0199] The idea of ​​optimized income method, comprehensive income commission method and incremental income method is used to estimate the value of the expected economic benefits of data assets discounted to the valuation time point, and a matching valuation implementation plan is customized based on the characteristics of each valuation object.

[0200] (1) Revenue Commission Method

[0201] Data asset value = ∑(future business income × profit sharing rate) × discount factor (2) Incremental revenue method Data asset value = ∑(change in income before and after data asset application) × discount factor (3) Optimized income method formula The optimized income method forms a valuation parameter system by sorting out the support forms for business value enhancement before and after the application of income-enhancing data assets. Then, according to the value generation characteristics of income-enhancing data assets, the income method valuation parameters are mapped to each valuation object, and the value assessment calculation indicators of each valuation object are determined to form an income method valuation indicator system, which is used to construct a specific valuation algorithm framework for each object.

[0202] In this embodiment, the data asset value assessment optimization market method is provided as follows:

[0203] The market optimization method needs to first establish the identification criteria of comparable cases, use the comparison method to identify comparable data assets, and define the data type and data usage. Then, based on the market and enterprise characteristics, statistical analysis, Delphi method, and analogy method are used to form market value correction parameters. Based on a variety of public market indices, the transaction price reference is corrected, and the price index on the valuation base date and the price index on the comparable case transaction date are finally determined.

[0204] The criteria for identifying comparable cases are as follows: To achieve market valuation, it is necessary to select similar data assets as comparable cases based on the relevant attributes of the object, otherwise the reference premise of the market approach will be missing. The overall suggestion can be judged based on the business theme of the data asset application and the characteristics of the data asset itself. From the perspective of business themes, the banking industry usually includes themes such as risk, marketing, operations, customers, and products. When targeting a specific data asset, it can be further divided based on the specific business theme. As for the characteristics of the data asset itself, the type of transaction can be considered, such as the original detailed data set, or the output result after processing based on various data analysis and processing technologies.

[0205] The market optimization model is divided into three steps to achieve valuation calculation: first, use the information of the to-be-valued and comparable cases to predict the basic annual income of the asset, then use the result to calculate the sum of the present value of the asset's future annual income, and finally, adjust the transaction date to obtain the total present value of the asset on the valuation date. The specific valuation method for a certain data asset or a certain type of data asset can refer to the following formula:

[0206]

[0207] Where N is the total number of asset types to be evaluated, Y is the expected trading life (i=1 is the base year), nj is the number of products in the base year of model j, qj is the average trading volume of products in the base year of model j, P0j is the price of similar comparable products in the base year of model j, Rj is the price correction coefficient of model j, gj is the average annual earnings growth rate of model j, rf is the discount rate, and k is the period correction coefficient.

[0208] Figure 2 This application embodiment is intended to be an enterprise data asset evaluation system, such as Figure 2 Said, in another embodiment, there is provided an enterprise data asset evaluation system, including a data resource integration module and a value evaluation calculation module;

[0209] The data resource integration module is used to construct a data resource pool for data assets to be evaluated by a group enterprise, and the data resource pool is used to store data asset evaluation objects;

[0210] The value assessment calculation module is used to perform value assessment on the data asset assessment object based on the constructed data asset assessment model to obtain a value assessment value.

[0211] Optionally, the resource integration module further includes:

[0212] Data collection unit, used to obtain data resources of the enterprise;

[0213] A resource allocation unit, configured to allocate data resource management spaces corresponding to the group-type enterprise according to business fields and management levels based on the data resource pool, wherein the data resource management space includes at least one of: a headquarters, a business field, and an enterprise;

[0214] A resource storage unit, used to store data resources according to the management space of the data resources; the data resource storage level includes at least one of: a near-source layer, a governance layer, and an application layer;

[0215] The resource integration unit is used to uniformly manage and process the aggregated data to form a data resource metadata management list.

[0216] Optionally, the system further comprises:

[0217] A data asset governance module, for implementing data governance on data asset assessment objects within the identified data asset range based on the data resource pool, wherein the data governance content on the data asset assessment objects includes at least one of: data quality, data standards, and data security;

[0218] The data asset governance module specifically includes:

[0219] A business identification unit, based on the data asset assessment object and according to the data resource metadata management list, identifies data resource metadata content items that require data governance;

[0220] A business analysis unit, used to analyze the business data governance requirements in the business request based on the governance requirements of information at all levels, and obtain a business analysis result;

[0221] The data quality management unit refines the requirements for data quality based on the business analysis results, builds a data quality inspection indicator library, configures data quality inspection rules for established data, configures data quality inspection plans, performs data quality inspections, obtains data quality reports, and tracks data quality issues;

[0222] A data standardization execution unit refines the data standardization requirements based on the business analysis results, and executes data standardization according to the data standardization requirements; the data standardization requirements include at least one of master data, reference data, business data, and indicator data;

[0223] The data security configuration unit refines the classification requirements for data security based on the business analysis results, and configures the security level of the data according to the data security classification requirements; the security level configuration of the data includes at least one of the core level, important level, sensitive level, internal level, and public level.

[0224] Optionally, the system further comprises:

[0225] The data value scenario analysis module is used to analyze the data value scenarios applicable to the data asset assessment objects based on the data asset assessment objects after the implementation of data governance; the data value scenarios include at least one of the following: internal use of data, no comparable cases in the market, products with expected returns, and comparable cases in the market.

[0226] Optionally, the system further comprises:

[0227] The data value scenario analysis module includes a governance factor estimation unit, an application value evaluation unit, a transaction value evaluation unit, and a model management unit;

[0228] A governance factor estimation unit, based on the data characteristics of the data asset evaluation object, analyzes the index items related to the data governance factors, parses the meaning of the index, and obtains a list of governance factor indicators;

[0229] The application value evaluation unit analyzes the index items related to the data application value factors based on the data characteristics of the data asset evaluation object, parses the meaning of the index, and obtains a list of application factor indicators;

[0230] The transaction value evaluation unit analyzes the index items related to the data transaction value factors based on the data characteristics of the data asset evaluation object, parses the meaning of the index, and obtains a transaction factor index list;

[0231] The model management unit, based on the basic model framework of the asset valuation model, derives a data asset valuation model based on applicable scenarios, and obtains a scenario branch valuation model based on applicable scenarios; the valuation method of the valuation model includes at least one of the cost method, the income method, and the market method; the subdivision of the model applicable scenarios includes at least one of the internal use of data, no comparable cases in the market, products with expected returns, and comparable cases in the market; the model valuation method for the scenario of internal use of data and no comparable cases in the market should at least include the cost method; the model valuation method for the scenario of products with expected returns and no comparable cases in the market should at least include at least two of the cost method and the income method; the model valuation method for the scenario of comparable cases in the market should at least include at least two of the cost method, the income method, and the market method.

[0232] Optionally, the value assessment calculation module includes:

[0233] An evaluation object management unit, which expands the data resource metadata content item and adds an evaluation object management item for managing the evaluation object; the evaluation object management item includes at least one of the name of the evaluation object, the evaluation unit, the creation date of the evaluation object, the adaptation scenario, and the selection model;

[0234] An adaptation scene unit, used to adapt the applicable scene of the evaluation object according to the evaluation object; the evaluation object can be adapted to one or more applicable scenes;

[0235] A model application unit, according to the applicable scenario adapted by the assessment object, associates the assessment model corresponding to the applicable scenario; based on the assessment model, adopts the assessment method to construct an assessment formula; the calculation factor in the assessment formula includes at least one item based on the governance factor indicator list, the application factor indicator list, and the transaction factor indicator list;

[0236] The model calculation unit analyzes the calculation factors corresponding to each indicator according to the evaluation object and the evaluation formula corresponding to the adapted evaluation scenario, obtains the calculation parameters and the calculation parameter values, and obtains the calculation results.

[0237] The enterprise data asset evaluation system of the embodiment of the present application can be used to implement any enterprise data asset evaluation method of the embodiment of the present application.

[0238] In another embodiment of the present application, an electronic device is also provided, including: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to any enterprise data asset evaluation method implemented in any embodiment of the present application.

[0239] In another embodiment of the present application, a computer storage medium is provided, on which a computer executable program is stored. When the program is executed by a processor, any enterprise data asset evaluation method of the embodiments of the present application is implemented.

[0240] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD, ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, an enterprise data asset evaluation method is implemented.

[0241] It should be noted that the same and similar parts between the various embodiments in this specification can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. The device and system embodiments described above are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components indicated as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. A person of ordinary skill in the art can understand and implement it without paying creative labor.

[0242] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for evaluating enterprise data assets, characterized in that: The method comprises: Receiving a business request, determining a data asset scope based on the business request, and acquiring a data asset assessment object according to the data asset scope; In response to the business request, a value assessment is performed on the data asset assessment object based on the constructed data asset assessment model to obtain a value assessment value.

2. The enterprise data asset evaluation method according to claim 1, characterized in that: The determination of the data asset scope is performed based on at least one of the following dimensions: data value chain dimension, business domain dimension, and system table dimension, and the specific method is as follows: The data asset scope is determined based on the data value chain dimension, and the determined data asset scope of the data value chain dimension includes at least one of original data assets, process data assets and application data assets; the data value chain is used to characterize the value of data at different stages of circulation; When determining the data asset scope based on the business domain dimension, the determined data asset scope of the business domain dimension includes at least one of management, production, service and finance; When determining the data asset range based on the system table dimension, the determined system table dimension data asset range includes at least one of a basic table, a code table, a report table, and an indicator table.

3. The enterprise data asset evaluation method according to claim 1, characterized in that: The method comprises: executing the following steps to construct the data asset evaluation model: Parsing the data asset scope to obtain data characteristics of the data asset assessment object; The data asset evaluation model is constructed based on the data characteristics of the data asset evaluation object.

4. The enterprise data asset evaluation method according to claim 3, characterized in that: The constructing of the data asset evaluation model based on the data features of the data asset evaluation object includes: Based on the data characteristics of the data asset assessment object, generate a description of the internal measurement indicator system of the data asset; The evaluation model is constructed based on the description of the internal measurement indicator system of the data assets.

5. The enterprise data asset evaluation method according to claim 4, characterized in that: The generating of the description of the internal measurement indicator system of the data asset based on the data characteristics of the data asset evaluation object includes: Based on the data features of the data asset evaluation object, the data features are dimensionally classified to obtain dimensional classification results of the data features; Based on the dimensional classification results of the data characteristics, an internal measurement indicator system for data assets is constructed to obtain a description of the internal measurement indicator system for data assets; the description of the internal measurement indicator system includes at least one of the data's own value, the data's use value, and the data's transaction value.

6. The enterprise data asset evaluation method according to claim 4, characterized in that: The method further comprises: By abstracting and reconstructing the data asset evaluation model, the basic model framework of the data asset evaluation model is obtained; The basic model framework of the asset valuation model is split according to different data value scenarios to obtain a scenario branch valuation model based on applicable scenarios; The data value scenarios include at least one of internal use of data, no comparable cases in the market, products with expected returns, and comparable cases in the market.

7. The enterprise data asset evaluation method according to claim 6, characterized in that: The data asset evaluation model constructed is used to evaluate the value of the data asset evaluation object to obtain the value evaluation value, which specifically includes: Load the data asset evaluation object into the corresponding scenario branch evaluation model, and set the evaluation factors and their corresponding weights; An evaluation score of the data asset is calculated based on the scenario branch evaluation model, and the calculation result is output.

8. A data asset evaluation system, characterized in that: The system includes a data resource integration module and a value assessment calculation module; The data resource integration module is used to construct a data resource pool for data assets to be evaluated by a group enterprise, and the data resource pool is used to store data asset evaluation objects; The value assessment calculation module is used to perform value assessment on the data asset assessment object based on the constructed data asset assessment model to obtain a value assessment value.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

Images