CN118278962A - Evaluation method and device for data asset value - Google Patents

Abstract

The embodiment of the invention provides a method and a device for evaluating the value of a data asset, wherein the method comprises the following steps: according to a preset evaluation target, collecting data assets to be evaluated, and processing the data assets to be evaluated through various data processing steps aiming at the data assets to be evaluated to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated; aiming at each data asset table, according to a blood margin propagation algorithm, the data production cost corresponding to the data asset to be evaluated is allocated, and the cost information of the data asset table is obtained; and evaluating the data management elements corresponding to the data asset tables by using an analytic hierarchy process aiming at each data asset table to obtain the data management score of the data asset table, and evaluating the asset return rate of the data asset to be evaluated according to the data management score and the cost information of all the data asset tables.

Description

A method and device for evaluating the value of data assets

Technical Field

The present invention relates to the field of data asset value assessment, and in particular to a method and device for assessing the value of data assets.

Background technique

The value of data assets is difficult to assess. Although the value of data assets has been widely recognized, there is still a lack of cross-regional and cross-industry data pricing and evaluation systems or methods, and data asset value assessment is in a dilemma. The valuation process is highly subjective, lacks quantitative indicators, and has a low level of technology application, making the data asset valuation results inaccurate.

In the process of implementing the present invention, the applicant discovered that there are at least the following problems in the prior art:

Existing technologies lack asset value assessment based on the entire life cycle of data assets.

Summary of the invention

The embodiments of the present invention provide a method and device for evaluating the value of data assets, which solve the problem that the prior art lacks asset value evaluation based on the entire life cycle of data assets.

To achieve the above objectives, on the one hand, an embodiment of the present invention provides a method for evaluating the value of data assets, including:

According to the preset evaluation target, data assets to be evaluated are collected, and the data assets to be evaluated are processed through multiple data processing steps to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated;

For each data asset table, the data production cost corresponding to the data asset to be evaluated is apportioned according to the lineage propagation algorithm to obtain the cost information of the data asset table;

For each data asset table, the data management elements corresponding to the data asset table are evaluated by the hierarchical analysis method to obtain the data management score of the data asset table;

The asset return rate of the data asset to be evaluated is evaluated based on the data management scores and cost information of all data asset tables.

Furthermore, the data assets to be evaluated are collected according to the preset evaluation target, and the data assets to be evaluated are processed through multiple data processing steps to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated, including:

According to the preset evaluation target, determine the scope of the corresponding data assets to be evaluated, and collect the data assets to be evaluated according to the scope of the data assets to be evaluated;

Performing data cleaning on the data assets to be evaluated to obtain cleaned data, wherein the data cleaning includes: removing duplicate data and repairing data errors;

Integrate the cleaned data from different sources, formats and structures, store them according to the preset unified data storage requirements, and obtain stored data;

Classify and label the stored data according to the nature, source and purpose of the stored data;

Replacing the sensitive information in the stored data with specified replacement information to obtain desensitized data corresponding to the stored data, and encrypting the desensitized data;

storing the desensitized and encrypted stored data into at least one data asset table by category;

Collecting data production costs corresponding to the data assets to be evaluated according to the data processing steps from the data assets to be evaluated to the at least one data asset table;

Among them, the data production cost includes: hardware construction cost, software construction cost, operation and maintenance cost, resource cost, data procurement cost, data consulting cost, manpower expenditure cost and site fee cost.

Furthermore, for each data asset table, the data production cost corresponding to the data asset to be evaluated is apportioned according to the lineage propagation algorithm to obtain the cost information of the data asset table, including:

For each data asset table, a processing procedure blood relationship corresponding to the data asset table is generated according to a blood relationship propagation algorithm; the processing procedure blood relationship represents a parent-child relationship between data processing steps;

According to the blood relationship of the processing procedures corresponding to the data asset table, the data production cost corresponding to the data asset to be evaluated is apportioned into the data production costs corresponding to each data processing step used to obtain the data asset table;

According to the blood relationship of the processing procedures, the data production costs corresponding to the respective data processing steps used to obtain the data asset table are accumulated to obtain the cost information of the data asset table.

Furthermore, for each data asset table, the data management elements corresponding to the data asset table are evaluated by the analytic hierarchy process to obtain the data management score of the data asset table, including:

Constructing a judgment matrix of the hierarchical analysis method according to the data management elements corresponding to the data asset table;

Comparing the relative importance of different data management elements corresponding to the data asset table according to the judgment matrix, and determining the weight of each data management element corresponding to the data asset table;

Normalizing each column of the judgment matrix and calculating the weight vector of each data management element corresponding to the data asset table;

Calculating the comprehensive weight of each data management element corresponding to the data asset table according to the weight vector of each data management element corresponding to the data asset table;

Determine the data management score of the data asset table according to the data management elements corresponding to the data asset table and their comprehensive weights;

The data management elements include basic elements, quality elements and usage elements;

The basic elements include data size;

The quality elements include: accuracy, consistency, completeness, standardization, and timeliness;

The usage factors include: accessibility and usage popularity.

Furthermore, evaluating the asset return rate of the data asset to be evaluated based on the data management scores and cost information of all data asset tables includes:

For each data asset table, add the data management score and cost information of the data asset table to obtain the overall value of the data asset table;

Determine the weight corresponding to the overall value of the data asset table according to the application scenario and contribution degree of the data asset table;

According to the weights corresponding to the overall values of all data asset tables, the overall values of all data asset tables are weighted and averaged to obtain the asset return rate of the data assets to be evaluated.

On the other hand, an embodiment of the present invention provides a data asset value evaluation device, including:

A data asset collection unit, configured to collect data assets to be evaluated according to preset evaluation targets, and process the data assets to be evaluated through a plurality of data processing steps to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated;

A data asset cost propagation unit, for allocating the data production cost corresponding to the data asset to be evaluated according to a lineage propagation algorithm for each data asset table, to obtain cost information of the data asset table;

A data asset management unit, for evaluating the data management elements corresponding to each data asset table by means of a hierarchical analysis method, to obtain a data management score for the data asset table;

The data asset evaluation unit is used to evaluate the asset return rate of the data asset to be evaluated based on the data management scores and cost information of all data asset tables.

Furthermore, the data asset collection unit includes:

A data asset collection module, used to determine the scope of corresponding data assets to be evaluated according to a preset evaluation target, and collect the data assets to be evaluated according to the scope of the data assets to be evaluated;

A data asset cleaning module is used to clean the data assets to be evaluated to obtain cleaned data, wherein the data cleaning includes: removing duplicate data and repairing data errors;

The data asset storage module is used to integrate cleaned data from different sources, formats and structures, store them according to preset unified data storage requirements, and obtain stored data;

A data asset classification module, used to classify and label the stored data according to the nature, source and purpose of the stored data;

A data asset desensitizing and encryption module, used to replace sensitive information in the stored data with specified replacement information, obtain desensitized data corresponding to the stored data, and encrypt the desensitized data;

A data asset table generation module, used to store the desensitized and encrypted storage data into at least one data asset table by category;

A data production cost collection module, configured to collect data production costs corresponding to the data asset to be evaluated according to the data processing steps from the data asset to be evaluated to the at least one data asset table;

Among them, the data production cost includes: hardware construction cost, software construction cost, operation and maintenance cost, resource cost, data procurement cost, data consulting cost, manpower expenditure cost and site fee cost.

Furthermore, the data asset cost propagation unit includes:

A processing procedure blood relationship determination module is used to generate, for each data asset table, a processing procedure blood relationship corresponding to the data asset table according to a blood relationship propagation algorithm; the processing procedure blood relationship represents a parent-child relationship between data processing steps;

A cost allocation module, for allocating the data production cost corresponding to the data asset to be evaluated into the data production costs corresponding to each data processing step for obtaining the data asset table according to the blood relationship of the processing steps corresponding to the data asset table;

The cost information determination module is used to accumulate the data production costs corresponding to each data processing step used to obtain the data asset table according to the processing process blood relationship, so as to obtain the cost information of the data asset table.

Furthermore, the data asset management unit includes:

A judgment matrix construction module, used to construct a judgment matrix of the hierarchical analysis method according to the data management elements corresponding to the data asset table;

A management element weight determination module, used to compare the relative importance of different data management elements corresponding to the data asset table according to the judgment matrix, and determine the weight of each data management element corresponding to the data asset table;

A weight vector determination module, used to normalize each column of the judgment matrix and calculate the weight vector of each data management element corresponding to the data asset table;

A comprehensive weight determination module, used to calculate the comprehensive weight of each data management element corresponding to the data asset table according to the weight vector of each data management element corresponding to the data asset table;

A data management score determination unit, used to determine the data management score of the data asset table according to the data management elements corresponding to the data asset table and their comprehensive weights;

The data management elements include basic elements, quality elements and usage elements;

The basic elements include data size;

The quality elements include: accuracy, consistency, completeness, standardization, and timeliness;

The usage factors include: accessibility and usage popularity.

Furthermore, the data asset evaluation unit includes:

An overall value determination module, for each data asset table, adding the data management score and cost information of the data asset table to obtain the overall value of the data asset table;

An overall value weight determination module, used to determine the weight corresponding to the overall value of the data asset table according to the application scenario and contribution degree of the data asset table;

The asset return rate determination module is used to perform weighted averaging on the overall values of all data asset tables according to the weights corresponding to the overall values of all data asset tables to obtain the asset return rate of the data assets to be evaluated.

The above technical solution has the following beneficial effects: by acquiring data assets, processing them through multiple data processing steps, and then allocating the data production costs corresponding to the data assets to be evaluated to determine the cost information of the data asset table, analyzing the data management elements through the hierarchical analysis method, and obtaining the data management score of the data asset table, combining the cost information and the data management score to determine the value of the data assets, and realizing the asset value evaluation of the entire life cycle from the data source of the data asset to the use of the data asset, so as to simplify the complex evaluation steps and evaluate the value of the data assets more comprehensively and accurately. Establish a data asset value evaluation indicator system, starting from the influencing factors of data at different stages, and improve the data asset value evaluation framework. It can have a wide range of uses in industries and enterprise-level applications that have large amounts of data and are expected to conduct data transactions.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a flow chart of a method for evaluating the value of data assets according to one embodiment of the present invention;

FIG2 is a schematic diagram of a data asset value evaluation device according to one embodiment of the present invention;

FIG3 is a schematic diagram of a retrieval process of a graph retrieval algorithm according to one embodiment of the present invention;

FIG4 is a schematic diagram of a data asset evaluation model according to an embodiment of the present invention;

FIG. 5 is another flow chart of a method for evaluating the value of data assets according to one embodiment of the present invention.

Detailed ways

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

On the one hand, as shown in FIG1 , an embodiment of the present invention provides a method for evaluating the value of data assets, including:

Step S10, according to the preset evaluation target, collect the data assets to be evaluated, and process the data assets to be evaluated through multiple data processing steps to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated;

Step S11, for each data asset table, allocating the data production cost corresponding to the data asset to be evaluated according to the lineage propagation algorithm to obtain the cost information of the data asset table;

Step S12, for each data asset table, evaluating the data management elements corresponding to the data asset table by using the analytic hierarchy process to obtain the data management score of the data asset table;

Step S13, evaluating the asset return rate of the data asset to be evaluated based on the data management scores and cost information of all data asset tables.

In some embodiments, the data life cycle can be divided into four stages: "collection", "construction", "management" and "use" based on the link from production to consumption. "Collection" represents the whole process of gathering heterogeneous data, "construction" represents the completeness and coherence of data governance, "management" refers to the governance and management of all data assets, and "use" emphasizes the application and return of data assets. Establish an asset value assessment method based on the data life cycle, starting from the factors affecting data at different stages, and improve the data asset value assessment framework. Taking into full consideration all the factors that are meaningful to the value factors in the path of data assets to realize their value, data assets are analyzed and classified, and different technical means are used to calculate different types of factors. The data asset evaluation model shown in Figure 4 is complete and in line with the true attributes of data assets. In the acquisition stage, the whole starts with the acquisition of data assets (data assets to be evaluated) and the acquisition of data production costs (such as data cost identification and entry). In the construction stage, data assets are processed and based on the cost allocation "calculator" dominated by the bloodline propagation algorithm (bloodline graph algorithm). In the management stage, data management scores are scored according to the basic elements, quality elements, and usage elements of data management. In the use stage, the value of data assets is evaluated by combining data management scores and data production costs, and considering non-economic factors and economic factors. Combined with the data asset factor evaluation system and data application market value analysis, the value of data assets is evaluated to determine the rate of return on data assets. Different technical means can be used to calculate and evaluate the value of different types of data assets. The following are some commonly used technical means and corresponding factors: Quantitative analysis: calculation and evaluation through the quantity and attributes of data. For example, for sales data assets, data mining and statistical analysis methods can be used to calculate its sales, growth rate and other indicators; Financial analysis: calculation and evaluation through financial data. For example, for financial data assets, financial ratio analysis, financial models and other methods can be used to evaluate its value and potential benefits; Risk assessment: calculation of data assets by evaluating their risks and uncertainties. For example, for market data assets, risk models and probability analysis can be used to evaluate their market value and risks; Brand value assessment: calculation of brand data assets by evaluating their impact on corporate value. For example, brand evaluation models and market research data can be used to calculate brand value; Technology assessment: calculation of technology data assets by evaluating their innovation, patent value, etc. For example, patent evaluation models and technology competition analysis can be used to evaluate the value of technology data assets. The following are the steps of the lineage propagation algorithm: Data cost identification: First, it is necessary to identify and record the cost items related to data assets, which include the costs of data collection, storage, processing, maintenance, etc. The cost items can be determined by reviewing financial records, discussing with relevant teams, and analyzing the data life cycle; Data cost entry: Associate the identified cost items with the corresponding data assets, and enter the cost data into the calculator. This can be done by manual entry or integration with the financial system; Lineage graph algorithm: Based on lineage relationships, use the lineage graph algorithm to track the source, flow and usage of data assets. Lineage relationships can be identified and captured through data tags, metadata, log records, etc.; Cost allocation calculation: Based on the lineage graph algorithm and cost data, the calculator can perform cost allocation calculations. The calculator allocates and calculates relevant costs based on factors such as the usage of data assets and flow paths to determine the cost of each data asset; Value assessment: Based on the calculated data asset costs, value assessment can be performed, which can be compared with the expected returns and market value of the data assets to determine the value of the data assets. Comprehensively sort out and summarize the data production costs, summarize the various data costs, and summarize the data costs into hardware construction costs, software construction costs, operation and maintenance costs, resource costs, data procurement costs, data consulting costs, human expenditures, site costs, etc. Lineage transmission is used for data cost assessment. Track data costs and allocate all the collectible costs to the root node of the data asset table. Through the lineage transmission relationship of data, the data costs are propagated along the data processing lineage. Based on the summary of the existing data management scoring dimensions, a data management scoring system was established, including basic attribute elements (basic elements), data quality elements (quality elements) and usage elements. The data management factors mainly explain the operation and operation of data after R&D, using basic evaluation indicators (corresponding to basic elements), data quality evaluation indicators (corresponding to quality elements) and usage evaluation indicators (corresponding to usage elements), and the non-economic factors of data (data management elements) are evaluated through the hierarchical analysis method, so as to obtain the data management score of a single data asset table. The overall industry return rate is obtained by analyzing the business conditions of the enterprise, and the data management score and cost information are fitted into the data asset return rate of each table. The overall return on assets of an enterprise is related to the industry it is in and the digital construction capabilities of the industry. Cost entry of data asset evaluation refers to the process of recording and managing cost information related to data asset evaluation. Its purpose is to ensure that the cost of data asset evaluation is accurately, comprehensively and traceably recorded so that it can be audited and analyzed when necessary. Specifically, the main tasks of cost entry include the following aspects: Identify and collect cost information: identify and collect cost information related to data asset evaluation through coordination with relevant departments and teams. These costs may include human resources, hardware equipment, software tools, external services, etc.; record and classify costs: record and classify the collected cost information, which can be achieved by establishing a cost classification system or using an existing financial system. Common cost classifications include personnel costs, equipment costs, software costs, training costs, etc.; allocate costs: allocate costs to relevant data asset evaluation projects or activities according to the principle of cost attribution, which can be allocated according to the project's workload, resource consumption, time allocation, etc.; calculate and summarize costs: calculate and summarize various costs based on cost classification and allocation, which can be automatically calculated and summarized by using spreadsheets or financial systems; review and verify costs: conduct regular cost audits and verifications to ensure the accuracy and reliability of costs, which can be achieved by cooperating with the financial department to conduct financial audits or using internal audit methods; analyze and report costs: conduct cost analysis and reporting based on needs, which can include generating cost reports, formulating cost budgets, conducting cost-benefit analysis, etc., to support management decisions and optimize resource allocation. Cost maintenance of data asset evaluation refers to the activities of updating and managing the cost information involved in the data asset evaluation process. Specifically, it includes the following aspects of work: Cost data collection: Collect all cost data related to data asset evaluation, including cost information on hardware equipment, software licenses, human resources, training, etc.; Cost classification and archiving: Classify and archive the collected cost data to ensure that each cost item has a clear identification and classification for subsequent cost analysis and management; Cost update and adjustment: Update and adjust cost data regularly according to actual conditions. For example, when the price of hardware equipment changes, the cost data needs to be updated accordingly; Cost analysis and reporting: Use the collected cost data to analyze and evaluate the cost-effectiveness and return on investment of data assets. At the same time, generate reports based on cost data to provide decision makers with information on data asset evaluation costs; Cost control and optimization: Take corresponding control measures based on the analysis results of cost data to optimize the cost-effectiveness of data asset evaluation. For example, reduce costs by saving software licensing fees or optimizing human resource allocation.

In other embodiments, as shown in FIG5 , the four stages of “collection”, “construction”, “management” and “use” are embodied in the four stages of data asset inventory, data asset governance, data asset operation and data asset evaluation. Data asset inventory: Conduct data asset inventory, giving priority to important data with business use value. Classify and grade data, and do a good job in data asset confirmation. Data asset governance: Establish a data asset management system and supporting systems and processes. Standardize activities such as data standard verification, data quality management, and data security. Data asset operation: Sort out existing data assets and form an enterprise-level data asset catalog. Users apply for consumption and use of assets, and record the element information required for daily management, accounting processing, and information disclosure. Data asset evaluation: According to the value evaluation plan, select the influencing factors of data elements, build a data asset value evaluation framework, and perform data asset pricing and valuation. Data asset inventory, data asset governance, and data asset operation are very useful for data asset evaluation. Data asset inventory: Before evaluating data assets, you first need to conduct a data asset inventory, which includes collecting and recording all data assets owned by the enterprise, including structured data (such as data in databases) and unstructured data (such as files, documents, etc.). Through data asset inventory, you can establish a clear list of data assets to provide a basis for subsequent evaluation work; Data asset governance: Data asset governance refers to the formulation and implementation of data management strategies to ensure the quality, security and compliance of data. In data asset evaluation, data asset governance plays an important role. By conducting data quality assessments, data security risk assessments and compliance assessments, the reliability and value of data assets can be determined, and relevant indicators and guidance can be provided for the evaluation; Data asset operations: Data asset operations refer to the process of using data assets to achieve business goals. In data asset evaluation, data asset operations can provide information on data usage and benefits. By analyzing the usage and business value of data assets, the return on data assets and potential value can be evaluated, and a basis for decision-making can be provided. In summary, data asset inventory, data asset governance, and data asset operations are very useful for data asset evaluation. They provide basic information, quality, and security assessments for evaluating data assets, and help determine the return on investment and potential value of data assets.

In some embodiments, in the data asset assessment, the four stages of "collection", "construction", "management" and "use" integrate the hierarchical processing architecture of the data warehouse unique to big data, which specifically includes the following layers: data collection layer: this layer is mainly responsible for collecting data from various data sources, which may include structured data, unstructured data, semi-structured data, etc. These data sources may include various business systems, log files, third-party data, etc.; data preprocessing layer: cleaning, conversion, merging and other preprocessing operations are performed on the collected raw data for the next step of processing. ETL (Extract, Transform, Load) tools are generally used to complete this step; data warehouse layer: storage and management of preprocessed data. This layer usually includes one or more data warehouses for storing historical data and supporting complex analytical queries; data analysis layer: analysis and mining of data stored in the data warehouse to extract valuable information and knowledge. This layer usually uses some statistical analysis, data mining, machine learning and other methods; data application layer: applying the analysis results to the business, such as providing support for decision-making through report display, early warning prompts, intelligent recommendations, etc. Throughout the process, we will evaluate the quality, integrity, consistency, etc. of the data to ensure the value of the data. After integrating the hierarchical processing architecture of data warehouse unique to big data, data asset assessment will have the following obvious effects, improving data processing efficiency: the hierarchical processing architecture can decompose large-scale data into smaller and more manageable parts, which can not only improve the speed of data processing, but also reduce errors in the data processing process; enhance data availability and accessibility: through the hierarchical processing architecture, data can be classified according to its importance and purpose, which makes it easier for users to obtain the data they need, thereby improving data availability and accessibility; improve data quality and accuracy: during the data processing process, the hierarchical processing architecture can effectively prevent data duplication. In addition, it can also improve data quality and accuracy by performing data cleaning and data verification at each level; better protect data security: through the hierarchical processing architecture, different access permissions can be set for data at different levels, thereby effectively protecting data security; assist in decision making: for enterprises, data processed by the hierarchical processing architecture of data warehouse unique to big data can help enterprises better understand their own business conditions and make more scientific and reasonable decisions.

The embodiment of the present invention has the following technical effects: by acquiring data assets, processing them through multiple data processing steps, and then allocating the data production costs corresponding to the data assets to be evaluated to determine the cost information of the data asset table, analyzing the data management elements through the hierarchical analysis method, obtaining the data management score of the data asset table, combining the cost information and the data management score, determining the value of the data asset, and realizing the asset value evaluation of the entire life cycle from the data source of the data asset to the use of the data asset, so as to simplify the complex evaluation steps and more comprehensively and accurately evaluate the value of the data asset. Through the in-depth analysis of data assets and the exploration of different technical means, it is clarified that the core idea of "taking blood relationship allocation as the core and management and transaction elements as the adjustment" is adopted for the data table in the "acquisition, construction, management, and use" stage within the enterprise. Through the perspective of blood relationship analysis, the detailed process of the accumulation and release of the cost value of each data from the source to the application process is completely and clearly revealed to the enterprise. Then it helps enterprises to identify and analyze high-value, high-cost processes or nodes in a targeted manner, and further optimize the data resource configuration within the enterprise. By establishing a value assessment indicator system based on the entire life cycle of assets, the complex value assessment process is simplified and the technical threshold for enterprises to conduct asset value assessment is lowered. Through the data asset value assessment model, the detailed process of cost value accumulation and release of each data from the source to the application of the entire life cycle is completely and clearly recorded for the enterprise. It integrates the data warehouse hierarchical processing architecture and data lineage characteristics unique to big data. Complete the calculation of the allocation of the overall data construction cost to the construction cost of a single data table.

Furthermore, the data assets to be evaluated are collected according to the preset evaluation target, and the data assets to be evaluated are processed through multiple data processing steps to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated, including:

According to the preset evaluation target, determine the scope of the corresponding data assets to be evaluated, and collect the data assets to be evaluated according to the scope of the data assets to be evaluated;

Performing data cleaning on the data assets to be evaluated to obtain cleaned data, wherein the data cleaning includes: removing duplicate data and repairing data errors;

Integrate the cleaned data from different sources, formats and structures, store them according to the preset unified data storage requirements, and obtain stored data;

Classify and label the stored data according to the nature, source and purpose of the stored data;

Replacing the sensitive information in the stored data with specified replacement information to obtain desensitized data corresponding to the stored data, and encrypting the desensitized data;

storing the desensitized and encrypted stored data into at least one data asset table by category;

Collecting data production costs corresponding to the data assets to be evaluated according to the data processing steps from the data assets to be evaluated to the at least one data asset table;

Among them, the data production cost includes: hardware construction cost, software construction cost, operation and maintenance cost, resource cost, data procurement cost, data consulting cost, manpower expenditure cost and site fee cost.

In some embodiments, according to the preset evaluation target, the scope of the corresponding data assets to be evaluated is determined, and the data assets to be evaluated are collected according to the scope of the data assets to be evaluated, that is, the "collection" stage, which specifically includes one or more of the following steps: determining the evaluation target, collecting data asset information, determining the evaluation index, determining the evaluation target, evaluating the data value, identifying data relevance, designing a data aggregation plan, implementing data aggregation, verifying the data aggregation results and/or updating the data asset value evaluation. Among them, determining the evaluation target is used to clarify the purpose and scope of the evaluation; for example, determining whether the evaluation is only for a specific data asset or the entire data asset library; collecting data asset information is used to collect and organize detailed information about the data assets, including data sources, data types, data volume, data quality, etc.; determining the evaluation index is used to determine the evaluation index according to the evaluation target, and these indicators can be the commercial value, potential risks, market demand, etc. of the data; evaluating the data value is used to evaluate the value of the data assets according to the selected evaluation index, which can include using statistical analysis methods, market research, expert judgment, etc.; identifying data relevance is used to analyze the relevance and mutual influence between data assets to determine the necessity and value of aggregating data; designing a data aggregation plan is used to design a data aggregation plan based on the evaluation results and data relevance. This can include determining the data aggregation method (such as ETL, API integration, etc.), data cleaning and transformation strategies, etc.; implementing data aggregation is used to implement data aggregation according to the designed plan. This may involve steps such as data extraction, transformation, and loading; verifying data aggregation results is used to verify the accuracy and completeness of the aggregated data to ensure that the data meets the expected requirements; updating the data asset value assessment is used to update the data asset value assessment based on the actual data aggregation results to reflect the impact of data aggregation. The "construction" stage includes one or more of the following steps: data cleaning, data integration, data classification and labeling, data desensitization and encryption, data quality monitoring, and/or data governance strategy formulation. Among them, data cleaning is used to pre-process the collected raw data, including removing duplicate data, repairing erroneous data, and unifying data formats. The purpose of this step is to ensure the quality and accuracy of the data and lay the foundation for subsequent data analysis and application. Data integration is used to integrate data from different sources, formats, and structures after cleaning the data to form a unified data storage. This requires the use of data integration technologies such as ETL (extraction, transformation, loading) to meet the needs of subsequent data analysis and application. Data classification and labeling are used to classify the integrated data and divide it into different categories according to the nature, source, purpose, etc. of the data. At the same time, corresponding labels are added to the data to facilitate subsequent data management and application. Data desensitization and encryption are used to protect data privacy. Data desensitization is required, that is, sensitive information is replaced with specified alternative values. In addition, data is encrypted to prevent illegal access to data during transmission and storage. Data quality monitoring is used to establish a data quality monitoring mechanism to continuously monitor the quality indicators of data assets to ensure the accuracy, completeness, timeliness and reliability of data assets. Data governance strategy formulation is used to formulate corresponding data governance strategies according to the business needs and data characteristics of the enterprise, including specifications in data management, data security, data compliance, etc. In the process of "building", the value assessment of data assets is mainly reflected in the following aspects: Data quality: When evaluating the value of data assets, it is necessary to pay attention to the quality of data, including the accuracy, completeness, timeliness, etc. of data. High-quality data assets can better reflect their value. Data value mining: In the process of data integration and classification, the potential value in the data is mined, the correlation and rules between data are discovered, and support is provided for the business decision-making of the enterprise. Data security and compliance: When evaluating the value of data assets, data security and compliance must be considered. Ensure that data assets bring value to the enterprise under the premise of compliance. Data application scenarios: Analyze the application potential of data assets in different business scenarios and evaluate their value contribution in each scenario. Data life cycle: Pay attention to the entire process from data generation to extinction, analyze the value changes of data assets at different stages, and comprehensively evaluate the value of data assets.

From the perspective of the entire life cycle of data assets, the total investment cost of data assets (data production cost) includes hardware construction costs, software construction costs, operation and maintenance costs, resource costs, data procurement costs, data consulting costs, human resources expenditures, site costs, etc., covering data asset collection, purchase, storage, calculation, management, application and other links. The specific cost sub-items and descriptions of each type of cost are shown in Table 1.

Table 1 Description of the total cost elements of data assets

Furthermore, for each data asset table, the data production cost corresponding to the data asset to be evaluated is apportioned according to the lineage propagation algorithm to obtain the cost information of the data asset table, including:

For each data asset table, a processing procedure blood relationship corresponding to the data asset table is generated according to a blood relationship propagation algorithm; the processing procedure blood relationship represents a parent-child relationship between data processing steps;

According to the blood relationship of the processing procedures corresponding to the data asset table, the data production cost corresponding to the data asset to be evaluated is apportioned into the data production costs corresponding to each data processing step used to obtain the data asset table;

According to the blood relationship of the processing procedures, the data production costs corresponding to the respective data processing steps used to obtain the data asset table are accumulated to obtain the cost information of the data asset table.

In some embodiments, the construction stage also includes allocating the data production cost corresponding to the data asset to be evaluated according to the lineage propagation algorithm for each data asset table to obtain the cost information of the data asset table; tracking the data cost, allocating all the data production costs that can be collected to the root node of the data asset table, and propagating the data cost along the data processing lineage through the lineage transmission relationship of the data. The root node refers to the top-level node in the data asset lineage propagation diagram, that is, the starting point of the entire data asset lineage relationship. In the data asset lineage propagation, the root node represents the initial data source or the earliest data generation point. It is the starting point of the entire data process, and no other node can be traced back to it. Usually, the root node can be the point where the original data is generated, such as a table in a database, a file in a file system, etc. When evaluating data costs and allocating costs, the importance of the root node lies in that it represents the initial investment cost of the entire data process. By allocating the cost step by step along the data lineage propagation path starting from the root node, the cost of each data asset can be more accurately evaluated and an appropriate cost can be assigned to each data asset. This can better understand the value of data assets and their contribution to the business, and help make more informed decisions and resource allocation. In summary, the root node is the starting point in the data asset lineage propagation diagram, representing the initial data source or the earliest data generation point. The root node plays an important role in evaluating data costs and allocating costs.

In some embodiments, the data production cost can be propagated along the data processing lineage through the data lineage transmission relationship, and the root node of the data asset can be determined by the following steps: find the starting node in the data processing process, that is, the source or input point of the data, which can be the source system, external data source or other data set; understand the data processing process: understand the various steps and transformations of the data in the processing process in detail, including data cleaning, conversion, integration, etc.; identify the factors affecting the data cost according to the lineage transmission relationship: in each data processing step, determine the factors that affect the data cost, which may include computing resources, storage space, labor costs, etc.; track the data lineage transmission path: establish the data lineage transmission path by recording the input and output relationship of each step in the data processing process, which can be achieved through the metadata management system or data lineage analysis tool; calculate the data cost: calculate the data cost according to the data lineage transmission path based on the cost factors determined in each data processing step. This can be a simple summation or a weighted calculation based on different factors; analyze the data cost: evaluate and analyze the calculated data cost, identify the high-cost data processing steps, find optimization opportunities, and propose corresponding improvement measures.

The bloodline propagation algorithm is an algorithm based on the graph retrieval algorithm, which is a field in graph algorithms. It uses tree theory to search the graph, traces back to the branch after finding the end point, and finally obtains the path from the starting point to the end point. The graph retrieval algorithm is shown in Figure 3. The left side is the graph data structure, and the right side is the retrieval path based on the graph data structure on the left. The present invention adopts breadth-first search. This algorithm advances layer by layer, taking the root node as the starting point, first finding the next node closest to the starting point (each node represents a data processing step), and then reaching the next processing layer from the inside to the outside, searching for the record path in the next processing layer until jumping out of the layer.

To allocate the collected costs to the data asset tables, you can follow the following steps to determine the data asset tables: First, determine the data asset tables that need to be evaluated for costs. This can be a database table, data file, data set, etc. Collect relevant costs: Collect cost information related to the data asset table. This includes costs for data collection, storage, processing, maintenance, etc. Determine cost allocation factors: Determine the factors used to allocate costs. This can be the number of rows, data volume, frequency of use, etc. of the data asset table. Calculate cost allocation ratio: Based on the determined allocation factors, calculate the cost allocation ratio for each data asset table. A simple ratio calculation method can be used to allocate the total cost to each data asset table in proportion. Allocate costs to data asset tables: Based on the calculated cost allocation ratio, allocate the corresponding costs to each data asset table. The allocated cost information can be recorded for subsequent management and analysis. Update cost allocation regularly: Costs may change over time. Therefore, cost allocation needs to be updated regularly to reflect the latest cost situation.

Tracking data costs and evaluating the lineage propagation of data assets can specifically include: Determining the lineage of data assets: First, determine the lineage between data assets, that is, the source, transmission and consumption relationship of data. This can be achieved by analyzing processes such as data flow, data conversion and data storage. Marking the cost information of data assets: For each data asset and related lineage, mark the corresponding cost information. Cost information can include cost data for data collection, data transmission, data storage, data processing and other links. Record and update cost information: Establish a cost management system or database to record and update the cost information of data assets. Ensure that the cost information is synchronized with the actual situation and updated in a timely manner. Tracking the propagation of data costs: By analyzing the lineage and cost information between data assets, the propagation path of data costs can be tracked. Starting from the source of the data, the consumption and related costs of data are tracked along the lineage chain. Evaluating data costs: Evaluate the cost of data assets based on the propagation path of data costs and related cost information. The cost of data assets at each link can be calculated, or the overall cost of data assets can be evaluated based on the required indicators.

In other embodiments, the graph retrieval algorithm can be applied to the process of data asset evaluation to help us process specific asset data. The specific processing steps are as follows: Construct a data asset graph: All data assets are represented in the form of nodes, and the relationship between nodes is represented in the form of edges. This data asset graph may include data tables, databases, files, etc. Define evaluation indicators: According to the evaluation requirements, determine the evaluation indicators, such as data value, data reliability, data usage, etc. These indicators can be used as attributes of nodes. Apply graph retrieval algorithms: According to the required evaluation indicators, select appropriate graph retrieval algorithms to search and traverse the data asset graph. Commonly used graph retrieval algorithms include depth-first search (DFS), breadth-first search (BFS), Dijkstra algorithm, minimum spanning tree algorithm, etc. Process asset data: In the process of graph retrieval, specific asset data can be processed according to the results of the algorithm. For example, calculate data value, evaluate data reliability, determine data usage, etc. Output evaluation results: Generate an evaluation result report based on the processed asset data, including the evaluation indicator values of each asset and the overall evaluation conclusion. By applying graph retrieval algorithms, we can find data assets related to evaluation indicators from the data asset graph, process and evaluate them, and then obtain the evaluation results of data assets. This can help us better understand the value and availability of data assets and make better decisions. In graph retrieval algorithms, "table" usually refers to the storage form of data, which can also be understood as a table in a database. There is a certain relationship between various aspects of data cost and tables. Hardware construction cost: related to the storage of the table, including the purchase and configuration costs of hardware devices such as servers and storage devices. Software construction cost: related to the database system of the table, including the purchase and development costs of database software. Operation and maintenance cost: related to the maintenance and management of the table, including the salary of the database administrator, server maintenance costs, etc. Resource cost: related to the access and use of the table, including the use fee of server resources, network bandwidth fees, etc. Data procurement cost: related to the data source of the table, including the cost of obtaining data from the outside. Data consulting cost: related to the data analysis and decision-making of the table, including the cost of consulting services. Human resource expenditure: related to the maintenance, development, analysis, etc. of the table, including staff wages, training and other expenses. Specifically, "table" refers to a data table in a database, which stores a specific type of data. Multiple tables can be related through association keys (such as primary keys and foreign keys), so that data between tables can be associated and queried. The storage organization of the table is usually automatically managed by the database management system, and different storage engines can be used to implement different storage mechanisms (such as B-tree, hash, etc.). By managing the storage organization and data content of the table, we can use graph retrieval algorithms to retrieve and analyze data from the table.

In some embodiments, the lineage transmission relationship of data can be obtained in the following ways: Metadata management system: The lineage relationship of data assets can be tracked and recorded using a dedicated metadata management system. These systems can track the source, conversion, and usage of data, and build a complete lineage transmission relationship diagram. Data collection tools: During the data collection process, the lineage relationship of data can be automatically recorded by using data collection tools. These tools can capture the source, target, and conversion process of data, and associate them to form a lineage transmission relationship. Database log: Some database systems can record data operation logs, including operations such as reading, writing, and modifying data. By analyzing these database logs, the lineage transmission relationship of data can be restored. Manual recording: In the absence of automated tools, manually recording the lineage relationship of data is also a feasible way. By communicating and investigating with relevant personnel, you can understand the source and usage of the data and manually draw a lineage transmission relationship diagram.

In some embodiments, the allocation calculation of the overall data construction cost to the construction cost of a single data table can be completed by following the steps below to determine the total data construction cost (the data production cost corresponding to the data asset to be evaluated): the total data construction cost usually includes hardware facility investment, software development and purchase, labor costs (including data collection, cleaning, organization, maintenance, etc.), etc.; calculate the weight of each data table: the weight of a data table can be defined based on factors such as its size (such as the number of records), its complexity (such as the number and type of fields), and its frequency of use; allocate costs: the total cost can be allocated to each data table based on the weight of each data table, that is, the cost of each data table = total data construction cost x the weight of the table; data table cost = total data construction cost x data table weight. This method is an approximate method, and its accuracy depends on how the weights are defined and calculated.

Furthermore, for each data asset table, the data management elements corresponding to the data asset table are evaluated by the analytic hierarchy process to obtain the data management score of the data asset table, including:

Constructing a judgment matrix of the hierarchical analysis method according to the data management elements corresponding to the data asset table;

Comparing the relative importance of different data management elements corresponding to the data asset table according to the judgment matrix, and determining the weight of each data management element corresponding to the data asset table;

Normalizing each column of the judgment matrix and calculating the weight vector of each data management element corresponding to the data asset table;

Calculating the comprehensive weight of each data management element corresponding to the data asset table according to the weight vector of each data management element corresponding to the data asset table;

Determine the data management score of the data asset table according to the data management elements corresponding to the data asset table and their comprehensive weights;

The data management elements include basic elements, quality elements and usage elements;

The basic elements include data size;

The quality elements include: accuracy, consistency, completeness, standardization, and timeliness;

The usage factors include: accessibility and usage popularity.

The Analytic Hierarchy Process (AHP) is a method for multi-criteria decision-making, which can help us rank the weights of different factors. The following are the specific steps of the hierarchical analysis method. Determine the hierarchical structure: First, we need to determine the hierarchical structure of the decision-making problem, that is, decompose the problem into different factor levels. In data asset evaluation, we can divide the data quality evaluation indicators into accuracy, consistency, completeness, standardization and timeliness; Construct a judgment matrix: Next, we need to construct a judgment matrix to compare the relative importance of different factors. The judgment matrix is a square matrix in which each element represents the relative importance comparison of each factor. In the judgment matrix, we need to compare each factor pairwise and give a relative weight according to its importance. Usually, a comparison scale of 1 to 9 is used for scoring; Calculate the weight vector: In the judgment matrix, each column needs to be normalized, and then the weight vector of each factor is calculated. Normalization can be achieved by dividing each element by the sum of the columns. Finally, the average value of each row is calculated to obtain the weight vector of each factor; Consistency test: In order to ensure the consistency of the judgment matrix, we need to calculate the consistency index (Consistency Index, referred to as CI) and the consistency ratio (Consistency Ratio, referred to as CR). If CR is less than 0.1, the judgment matrix is considered consistent, otherwise the comparison matrix needs to be adjusted; Comprehensive weight sorting: According to the weight vector of each factor, the comprehensive weight can be calculated for sorting different factors. The comprehensive weight can be obtained by weighted summation of the weight vector of each factor. Through the above steps, the hierarchical analysis method can be used to sort the data quality evaluation indicators and determine their relative importance. This allows accurate decision-making and prioritization in data asset evaluation. In data asset evaluation, non-economic factors refer to factors related to data management, but do not involve economic costs or benefits. These factors usually include the following aspects: data quality: factors such as data accuracy, completeness, consistency and reliability; data security: the ability to protect data from unauthorized access, modification, deletion or leakage; data availability: the ability to ensure that data is accessible and available at any time when needed; data governance: the rules, processes and mechanisms of data management to ensure that data is properly managed and used within the organization; data compliance: ensuring that data management complies with regulations, laws and industry standards.

In some embodiments, the relationship between the software construction cost, data consulting cost, resource cost, hardware construction cost and operation and maintenance cost of data asset investment can also be evaluated by the analytic hierarchy process. The analytic hierarchy process is a quantitative analysis method used to compare and evaluate the relative importance of multiple factors. In this evaluation system, the software construction cost, data consulting cost, resource cost, hardware construction cost and operation and maintenance cost can be regarded as economic factors, and the analytic hierarchy process is used to determine the relative weight or importance between them. By comparing and evaluating different cost factors, it can be determined which factors have a higher weight in the data asset evaluation, thereby guiding decision makers to make more wise choices in resource allocation and investment decisions. It should be noted that economic factors and non-economic factors are interrelated in data asset evaluation. For example, the improvement of data quality and data security may require more resources and technology, which will involve economic costs. Therefore, when conducting data asset evaluation, economic factors and non-economic factors should be considered comprehensively to achieve comprehensive optimization of data management.

In some embodiments, the data element value assessment is mainly divided into two steps: one is to identify the data elements that need to be assessed, and the other is to perform valuation calculations based on appropriate methods. When enterprises specifically select a set of data management element indicators, they are mainly affected by two factors: on the one hand, they should combine the maturity level of the enterprise's current data infrastructure to ensure that all selected element indicators can be calculated; on the other hand, they should combine the enterprise's current operating conditions and industry attributes to select evaluation indicators that are highly correlated with the value of data assets.

The basis for selecting factor indicators for calculating the total input cost of data assets (the data production cost corresponding to the data assets to be evaluated) is determined according to the specific environment and needs. The following are some common indicator selection factors: Purpose of cost analysis: It is key to determine the purpose of cost analysis, such as determining the return on investment, cost-effectiveness, cost control, etc.; Type of data assets: Different types of data assets may have different cost factor indicators. For example, for hardware assets, purchase costs, maintenance costs, etc. can be considered. For software assets, license fees, update and upgrade costs, etc. can be considered; Organizational needs: Select appropriate indicators based on the needs and priorities of the organization. For example, if the organization is very concerned about cost-effectiveness, it can select benefit-related indicators such as cost savings ratio or return on investment; Industry standards: Refer to industry standards and best practices to select indicators. Industry standards can provide some commonly used indicator selection references to ensure that the selected indicators are consistent with other peer organizations for comparison and evaluation; Data availability: Consider the availability and reliability of data, and select indicators supported by reliable data sources to ensure the accuracy and credibility of cost analysis. The selection of evaluation indicators that are highly correlated with the value of data assets needs to be customized according to the business conditions and industry characteristics of the enterprise. The following is a possible method: Business value: based on the degree of application of data in business processes and the degree of impact of data on business decisions; Rarity: based on the difficulty and uniqueness of data acquisition; Completeness: based on the completeness of data content; Accuracy: based on the quality of data, including data accuracy, consistency, timeliness, etc.; Compliance: based on the compliance of data, including data security, privacy, compliance, etc. The following factors can be considered in combination with the business conditions of the enterprise. For sales-oriented enterprises, sales data and customer data may have higher value; for technology-based enterprises, R&D data and patent data may have higher value; Industry attributes will also affect the value of data. For example, in the financial industry, transaction data and credit data may have higher value, and in the medical industry, patient data and drug data may have higher value. All of the above factors can be used as references for evaluation indicators, and specific indicators need to be customized according to the specific situation of the enterprise.

Table 2 is the data management element evaluation system table

Table 2 Data management element evaluation system

The table records of the data element evaluation system in Table 2 are used to evaluate and compare the performance and value of different data management elements. They contain the indicators and related data of each data management element to help decision makers make reasonable decisions. When using these table records, data management elements can be scored and ranked according to different indicators. Depending on the specific evaluation system, each indicator can have different weights and scoring criteria. Evaluators can adjust the weights of different indicators according to their needs and preferences to reflect the importance they attach to different indicators. Then, based on the scores of each data management element on each indicator, a comprehensive score is calculated for comparison and decision-making. The contents of these table records can also be used to track and monitor data management elements. By periodically updating data and re-evaluating indicators, changes and improvements in data management elements can be tracked, and evaluation results and decisions can be adjusted in a timely manner.

In some embodiments, for each data asset table, the data management elements corresponding to the data asset table are evaluated by the hierarchical analysis method to obtain the data management score of the data asset table, which is the management stage. In the "management" stage, the main goal of data asset value assessment is to ensure that all data assets are effectively governed and managed to maximize the value of data assets. The "management" stage may include one or more of the following steps: data combing is used to integrate and comb data sources to ensure data quality, accuracy, timeliness and completeness, which helps to improve the value of data assets; data classification and labeling are used to classify and label data according to data characteristics and business needs, which helps to improve data discoverability and ease of use, thereby increasing the value of data assets; data governance is used to implement data governance strategies, including data quality management, data security management, data privacy protection and data compliance checks, which helps to ensure the reliability and compliance of data assets and further enhance the value of data assets; data value mining is used to use data analysis , data mining and other technologies to explore the potential value and relevance of data assets, which helps to discover new business opportunities and improve the practical value of data assets; the data asset catalog is used to establish and improve the data asset catalog, record the basic information, usage and permission control of data assets, which helps to improve the findability and reusability of data assets; data value assessment is used to regularly assess the value of data assets based on the influencing factors of data assets at various stages, which helps to understand the value changes of data assets in real time and provide a basis for the optimization and adjustment of data assets; data asset application and return is used to promote the application of data assets in business scenarios and realize the implementation of data asset value. At the same time, track the application effect of data assets and continuously optimize data assets to increase the value of data assets. In the process of "management", the embodiment of data asset value assessment is mainly through the following aspects: improving data quality: ensuring the accuracy and completeness of data through data combing, governance and other means, thereby increasing the value of data assets; improving data availability: improving the discoverability and usability of data through data classification, labeling and other means, making data assets easier for business departments to use; ensuring data security and compliance: ensuring the security and compliance of data assets and reducing potential risks through data governance measures; discovering potential value: exploring the potential value and relevance in data assets through data mining and analysis, and providing support for business innovation; improving the reusability of data assets: recording the basic information and usage of data assets by establishing a data asset catalog to facilitate reuse by business departments; optimizing data assets: optimizing and adjusting data assets based on the results of data asset value assessment to better meet business needs; realizing the value of data assets: promoting the application of data assets in actual business scenarios, transforming them into business results, and maximizing the value of data assets.

In some embodiments, the data management scoring system based on data basic attribute elements and data quality elements includes the following dimensions: data availability: assessing whether the data is easy to obtain, share and use, and whether it has sufficient reliability and stability; data integrity: assessing whether the data is complete, whether it contains all the required fields and records, and whether there is data loss or error; data accuracy: assessing the accuracy and correctness of the data, whether it meets the expected standards and specifications, and whether there is data redundancy or inconsistency; data consistency: assessing the consistency of data between different systems or data sources, whether there are data differences or mismatches; data security: assessing the security and confidentiality of the data, whether it is subject to appropriate protection measures, and whether there are potential security risks; data traceability: assessing whether the source and change history of the data are traceable, whether there are sufficient audit and log records; data documentation: assessing the degree and quality of data documentation, whether there are clear data definitions, field descriptions and data dictionaries. The purpose of these scoring dimensions is to evaluate and measure the quality and effectiveness of data management. The dimensions of data management scoring can be obtained in quantitative and qualitative ways. Quantitative scoring can be based on the measurement values of data indicators, such as data missing rate, data error rate, etc. Qualitative scoring can be obtained through expert evaluation, user feedback or questionnaires. These scores can be calculated and measured according to specific evaluation methods and standards, and finally a score for each scoring dimension is obtained.

In some embodiments, the basic evaluation indicators and data quality evaluation indicators in the data management scoring system under data asset evaluation can be used in the following ways: data scale and data volume: These indicators are used to measure the size and quantity of data sets. By evaluating the data scale and data volume, the storage and processing resources required for data management can be determined, and a benchmark can be provided for the data management plan; growth rate: The growth rate indicator is used to evaluate the growth rate of data. By understanding the growth rate of data, future data needs can be predicted, and data management strategies can be planned and adjusted accordingly; accuracy: The accuracy evaluation indicator is used to measure the accuracy and correctness of the data, which can be evaluated by comparing the degree of match between the data and the actual situation. By improving the accuracy of the data, the reliability and credibility of the decision can be enhanced; consistency: The consistency evaluation indicator is used to evaluate the consistency of data from different sources and at different time points. This can be evaluated by comparing whether the values, formats, and definitions of the data are consistent. Ensuring data consistency can reduce errors and conflicts and improve the credibility and reliability of the data; integrity: The integrity evaluation indicator is used to evaluate whether the data is complete and contains all the required attributes and records. By ensuring the integrity of the data, problems caused by missing information and incomplete data can be avoided; normativeness: The normative evaluation indicator is used to evaluate whether the data meets the predetermined standards, specifications, and conventions. By following the specifications, the consistency, comparability and operability of data can be improved; Timeliness: Timeliness evaluation indicators are used to evaluate the update speed and timeliness of data. By ensuring that data is updated in a timely manner, decisions and analysis can be based on the latest data.

Furthermore, evaluating the asset return rate of the data asset to be evaluated based on the data management scores and cost information of all data asset tables includes:

For each data asset table, add the data management score and cost information of the data asset table to obtain the overall value of the data asset table;

Determine the weight corresponding to the overall value of the data asset table according to the application scenario and contribution degree of the data asset table;

According to the weights corresponding to the overall values of all data asset tables, the overall values of all data asset tables are weighted and averaged to obtain the asset return rate of the data assets to be evaluated.

In some embodiments, the return on assets is obtained by adding the data management score to the cost information and then performing a weighted average based on the data application value. The following is a calculation process for the return on assets: collect the data assets to be evaluated, and pre-process the data assets to be evaluated to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated; calculate the cost information of each data asset table through the blood propagation algorithm; use the hierarchical analysis method to obtain the data management score of a single data asset table. Add the data management score to the cost information of each data asset table to obtain the overall value of each data asset table. For each data asset table, assign a corresponding weight according to its application scenario and contribution level. According to the weight, the overall value of each data asset table is weighted averaged to obtain the return on assets.

In some embodiments, the specific steps of the "use" stage are as follows: data application planning: plan the application scenarios and methods of data assets based on business needs and data characteristics. The goal of this step is to ensure that data assets can generate value in appropriate scenarios; data processing and analysis: according to the application plan, perform necessary processing and analysis on the data to meet business needs, which includes data cleaning, data conversion, data mining and other operations; data assetization: converting processed and analyzed data into reusable assets to provide continuous value for the business, which includes establishing data models, data warehouses, data services, etc.; data value realization: achieving business goals, improving business efficiency or creating new business value through data applications. In the process of "using", the manifestation of data asset value assessment mainly includes the following points: data application effect assessment: assess the effect of data assets in actual applications, such as improvement of business indicators, improvement of work efficiency, etc.; data value mining degree assessment: assess the mining degree of data assets in business scenarios, including the depth and breadth of data processing and analysis; data asset reuse rate assessment: assess the utilization rate of data assets in multiple applications, reflecting the sustainable value of data assets; data asset contribution to business innovation assessment: assess the role of data assets in business innovation, including the development of new businesses, product improvements, etc.; data security and compliance assessment: assess the security and compliance of data assets in the application process to ensure the stable value of data assets. Based on the above factors, the value of data assets in the process of "using" is comprehensively assessed, so as to provide a basis for the management and optimization of data assets.

In some embodiments, to specifically analyze the business conditions of an enterprise and derive the industry return rate of the overall data assets, the following steps can be followed to collect business data: First, collect the financial statements and business data of the enterprise, including the income statement, balance sheet, and cash flow statement, etc., which can provide key information about the business conditions and financial health of the enterprise; determine data assets: identify and quantify the data assets of the enterprise, which can include customer databases, market research data, sales data, supply chain data, etc., to ensure a clear understanding of the value and importance of data assets; analyze business conditions: use the collected business data to analyze the financial status, profitability, and operating efficiency of the enterprise, which can include calculating financial ratios and indicators, such as net profit margin, return on assets, and current ratio, etc., to evaluate the business conditions of the enterprise; industry comparison: conduct industry comparative analysis, including comparing the financial indicators of competitors and companies in the same industry, which can help determine the relative competitive advantage of the enterprise and the industry average; calculate industry return rate: by comparing the value of the data assets of the enterprise with the industry average, the industry return rate of the data assets can be calculated. This can be calculated by comparing the data asset income of the enterprise with the total income of the industry. The specific analysis method and indicator selection may vary according to different industries and enterprises. When conducting data asset valuation, it is recommended to consult a professional financial analyst or data analyst to ensure accuracy and reliability.

In some embodiments, to fit the data management score to the data asset return rate of each table, the following steps can be followed: first, determine the calculation method of the data management score, which can include indicators such as data accuracy, data integrity, data reliability, etc., and the weights and scoring rules of these indicators can be determined according to actual conditions; then, determine the calculation method of the data asset return rate of each table, which can include indicators such as data usage frequency, data value, data quality, etc., and similarly, the weights and scoring rules of these indicators can be determined according to actual conditions; next, evaluate the data management score and data asset return rate for each table, and score each table according to the defined indicators and scoring rules. Some tools or methods can be used to automate the evaluation process, such as data quality management tools or data asset management systems; match the data management score and data asset return rate, and correspond the data management score and data asset return rate of each table. Linear regression or other related statistical analysis methods can be used to fit the relationship between the two indicators; finally, based on the fitting results, the data asset return rate of other tables can be predicted, and by applying the fitted model, the data asset return rate of other tables can be estimated, thereby providing decision support and optimizing data management strategies. This process may involve some data collection and data analysis work. At the same time, the fitting result is only a prediction value, and the actual return on data assets may be affected by other factors. Therefore, in practical applications, it is necessary to continuously monitor and adjust the model to ensure its accuracy and effectiveness.

On the other hand, as shown in FIG2 , an embodiment of the present invention provides a data asset value evaluation device, including:

The data asset collection unit 200 is used to collect data assets to be evaluated according to a preset evaluation target, and process the data assets to be evaluated through multiple data processing steps to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated;

The data asset cost propagation unit 201 is used to allocate the data production cost corresponding to the data asset to be evaluated according to the lineage propagation algorithm for each data asset table to obtain the cost information of the data asset table;

The data asset management unit 202 is used to evaluate the data management elements corresponding to each data asset table by using the analytic hierarchy process to obtain the data management score of the data asset table;

The data asset evaluation unit 203 is used to evaluate the asset return rate of the data asset to be evaluated based on the data management scores and cost information of all data asset tables.

Furthermore, the data asset collection unit 200 includes:

A data asset collection module, used to determine the scope of corresponding data assets to be evaluated according to a preset evaluation target, and collect the data assets to be evaluated according to the scope of the data assets to be evaluated;

A data asset cleaning module is used to clean the data assets to be evaluated to obtain cleaned data, wherein the data cleaning includes: removing duplicate data and repairing data errors;

The data asset storage module is used to integrate cleaned data from different sources, formats and structures, store them according to preset unified data storage requirements, and obtain stored data;

A data asset classification module, used to classify and label the stored data according to the nature, source and purpose of the stored data;

A data asset desensitizing and encryption module, used to replace sensitive information in the stored data with specified replacement information, obtain desensitized data corresponding to the stored data, and encrypt the desensitized data;

A data asset table generation module, used to store the desensitized and encrypted storage data into at least one data asset table by category;

A data production cost collection module, configured to collect data production costs corresponding to the data asset to be evaluated according to the data processing steps from the data asset to be evaluated to the at least one data asset table;

Among them, the data production cost includes: hardware construction cost, software construction cost, operation and maintenance cost, resource cost, data procurement cost, data consulting cost, manpower expenditure cost and site fee cost.

Furthermore, the data asset cost propagation unit 201 includes:

A processing procedure blood relationship determination module is used to generate, for each data asset table, a processing procedure blood relationship corresponding to the data asset table according to a blood relationship propagation algorithm; the processing procedure blood relationship represents a parent-child relationship between data processing steps;

A cost allocation module, for allocating the data production cost corresponding to the data asset to be evaluated into the data production costs corresponding to each data processing step for obtaining the data asset table according to the blood relationship of the processing steps corresponding to the data asset table;

The cost information determination module is used to accumulate the data production costs corresponding to each data processing step used to obtain the data asset table according to the processing process blood relationship, so as to obtain the cost information of the data asset table.

Furthermore, the data asset management unit 202 includes:

A judgment matrix construction module, used to construct a judgment matrix of the hierarchical analysis method according to the data management elements corresponding to the data asset table;

A management element weight determination module, used to compare the relative importance of different data management elements corresponding to the data asset table according to the judgment matrix, and determine the weight of each data management element corresponding to the data asset table;

A weight vector determination module, used to normalize each column of the judgment matrix and calculate the weight vector of each data management element corresponding to the data asset table;

A comprehensive weight determination module, used to calculate the comprehensive weight of each data management element corresponding to the data asset table according to the weight vector of each data management element corresponding to the data asset table;

A data management score determination unit, used to determine the data management score of the data asset table according to the data management elements corresponding to the data asset table and their comprehensive weights;

The data management elements include basic elements, quality elements and usage elements;

The basic elements include data size;

The quality elements include: accuracy, consistency, completeness, standardization, and timeliness;

The usage factors include: accessibility and usage popularity.

Furthermore, the data asset evaluation unit 203 includes:

An overall value determination module, for each data asset table, adding the data management score and cost information of the data asset table to obtain the overall value of the data asset table;

An overall value weight determination module, used to determine the weight corresponding to the overall value of the data asset table according to the application scenario and contribution degree of the data asset table;

The asset return rate determination module is used to perform weighted averaging on the overall values of all data asset tables according to the weights corresponding to the overall values of all data asset tables to obtain the asset return rate of the data assets to be evaluated.

The embodiments of the present invention are device embodiments that correspond one-to-one to the aforementioned method embodiments. The embodiments of the present invention can be understood based on the aforementioned method embodiments, and will not be described in detail here.

It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

The disclosed embodiments are described above to enable any person skilled in the art to implement or use the present invention. Various modifications of these embodiments are obvious to those skilled in the art, and the general principles defined herein may also be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments given herein, but is consistent with the broadest scope of the principles and novel features disclosed in this application.

The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", as explained by "including:" used as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

Those skilled in the art may also understand that the various illustrative logical blocks, units, and steps listed in the embodiments of the present invention may be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly demonstrate the interchangeability of hardware and software, the various illustrative components, units, and steps described above have generally described their functions. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art may use various methods to implement the described functions for each specific application, but such implementation should not be understood as exceeding the scope of protection of the embodiments of the present invention.

The various illustrative logic blocks or units described in the embodiments of the present invention can be implemented or operated by a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or any combination of the above. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any conventional processor, controller, microcontroller or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

The steps of the method or algorithm described in the embodiments of the present invention can be directly embedded in hardware, a software module executed by a processor, or a combination of the two. The software module can be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or other storage media of any form in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a user terminal. Optionally, the processor and the storage medium can also be arranged in different components in the user terminal.

In one or more exemplary designs, the above functions described in the embodiments of the present invention can be implemented in hardware, software, firmware or any combination of the three. If implemented in software, these functions can be stored on a computer-readable medium, or transmitted in the form of one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media that facilitate the transfer of computer programs from one place to another. The storage medium can be any available medium that can be accessed by any general or special computer. For example, such computer-readable media can include but are not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store program codes in the form of instructions or data structures and other forms that can be read by general or special computers, or general or special processors. In addition, any connection can be appropriately defined as a computer-readable medium, for example, if the software is transmitted from a website site, server or other remote resource through a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wirelessly, such as infrared, wireless and microwave, it is also included in the defined computer-readable medium. The disk and disc include compact disk, laser disk, optical disk, DVD, floppy disk and blue-ray disk. Disks usually copy data magnetically, while discs usually copy data optically with lasers. The above combination can also be included in computer readable media.

The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims (10)

1. A method of evaluating the value of a data asset, comprising:

According to a preset evaluation target, collecting data assets to be evaluated, and processing the data assets to be evaluated through various data processing steps aiming at the data assets to be evaluated to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated;

Aiming at each data asset table, according to a blood margin propagation algorithm, the data production cost corresponding to the data asset to be evaluated is allocated, and the cost information of the data asset table is obtained;

Evaluating data management elements corresponding to each data asset table through an analytic hierarchy process to obtain data management scores of the data asset tables;

And evaluating the asset return rate of the data asset to be evaluated according to the data management scores and the cost information of all the data asset tables.

2. The method for evaluating the value of a data asset according to claim 1, wherein the collecting the data asset to be evaluated according to the preset evaluation target, and processing the data asset to be evaluated through a plurality of data processing steps to obtain at least one data asset table and data production cost corresponding to the data asset to be evaluated, includes:

determining the range of a corresponding data asset to be evaluated according to a preset evaluation target, and collecting the data asset to be evaluated according to the range of the data asset to be evaluated;

performing data cleaning on the data asset to be evaluated to obtain cleaned data, wherein the data cleaning comprises: removing repeated data and repairing data errors;

Carrying out data integration on the cleaned data with different sources, formats and structures, and storing according to preset unified data storage requirements to obtain storage data;

Classifying the stored data according to the nature, source and purpose of the stored data and marking tags;

Replacing sensitive information in the stored data with appointed replacement information to obtain desensitized data corresponding to the stored data, and encrypting the desensitized data;

Storing the desensitized and encrypted stored data as at least one data asset table by category;

Collecting data production costs corresponding to the data assets to be evaluated according to a data processing step from the data assets to be evaluated to the at least one data asset table;

Wherein, the data production cost includes: hardware construction cost, software construction cost, operation and maintenance cost, resource cost, data purchase cost, data consultation cost, manpower expenditure cost and site cost.

3. The method for evaluating the value of a data asset according to claim 1, wherein for each data asset table, the step of distributing the data production cost corresponding to the data asset to be evaluated according to a blood-margin propagation algorithm to obtain cost information of the data asset table comprises the following steps:

Generating a processing procedure blood edge relation corresponding to each data asset table according to a blood edge propagation algorithm; the blood relationship of the processing procedure represents the father-son relationship between the data processing steps;

According to the blood relationship of the processing procedure corresponding to the data asset table, the data production cost corresponding to the data asset to be evaluated is allocated to the data production cost corresponding to each data processing step for obtaining the data asset table;

And accumulating the data production cost corresponding to each data processing step for obtaining the data asset table according to the blood relationship of the processing procedures, and obtaining the cost information of the data asset table.

4. The method for evaluating the value of a data asset according to claim 1, wherein the evaluating the data management element corresponding to the data asset table by a hierarchical analysis method for each data asset table to obtain the data management score of the data asset table comprises:

Constructing a judgment matrix of an analytic hierarchy process according to the data management elements corresponding to the data asset table;

Comparing the relative importance among different data management elements corresponding to the data asset table according to the judgment matrix, and determining the weight of each data management element corresponding to the data asset table;

carrying out normalization processing on each column of the judgment matrix, and calculating weight vectors of all data management elements corresponding to the data asset table;

calculating the comprehensive weight of each data management element corresponding to the data asset table according to the weight vector of each data management element corresponding to the data asset table;

Determining the data management score of the data asset table according to each data management element corresponding to the data asset table and the comprehensive weight of each data management element;

the data management element comprises a basic element, a quality element and a use element;

The base elements include data size;

the mass element comprises: accuracy, consistency, integrity, normalization, timeliness;

the use elements include: accessibility and heat of use.

5. The method of claim 1, wherein the evaluating the asset return of the data asset to be evaluated based on the data management scores and the cost information of all data asset tables comprises:

adding the data management score and the cost information of each data asset table to obtain the total value of the data asset table;

determining the weight corresponding to the overall value of the data asset table according to the application scene and the contribution degree of the data asset table;

and weighting and tie the total values of all the data asset tables according to the weights corresponding to the total values of all the data asset tables to obtain the asset return rate of the data asset to be evaluated.

6. An apparatus for evaluating the value of a data asset, comprising:

The data asset acquisition unit is used for collecting data assets to be evaluated according to a preset evaluation target, and processing the data assets to be evaluated through various data processing steps aiming at the data assets to be evaluated to obtain at least one data asset table and data production cost corresponding to the data assets to be evaluated;

The data asset cost spreading unit is used for spreading the data production cost corresponding to the data asset to be evaluated according to a blood margin spreading algorithm aiming at each data asset table to obtain the cost information of the data asset table;

The data asset management unit is used for evaluating the data management elements corresponding to each data asset table through a hierarchical analysis method to obtain the data management scores of the data asset tables;

And the data asset evaluation unit is used for evaluating the asset return rate of the data asset to be evaluated according to the data management scores and the cost information of all the data asset tables.

7. The apparatus for evaluating the value of a data asset of claim 6, wherein the data asset acquisition unit comprises:

The data asset collection module is used for determining the range of the corresponding data asset to be evaluated according to a preset evaluation target and collecting the data asset to be evaluated according to the range of the data asset to be evaluated;

The data asset cleaning module is used for cleaning the data of the data asset to be evaluated to obtain cleaned data, and the data cleaning comprises: removing repeated data and repairing data errors;

The data asset storage module is used for integrating data of cleaned data with different sources, formats and structures, and storing the cleaned data according to preset unified data storage requirements to obtain storage data;

the data asset classification module is used for classifying the stored data according to the properties, sources and purposes of the stored data and marking tags;

The data asset desensitization encryption module is used for replacing sensitive information in the stored data with appointed replacement information to obtain desensitization data corresponding to the stored data, and encrypting the desensitization data;

A data asset table generation module for storing the desensitized and encrypted stored data as at least one data asset table by category;

The data production cost collecting module is used for collecting the data production cost corresponding to the data asset to be evaluated according to the data processing steps from the data asset to be evaluated to the at least one data asset table;

Wherein, the data production cost includes: hardware construction cost, software construction cost, operation and maintenance cost, resource cost, data purchase cost, data consultation cost, manpower expenditure cost and site cost.

8. The apparatus for evaluating the value of a data asset according to claim 6, wherein the data asset cost propagation unit comprises:

The processing procedure blood edge relation determining module is used for generating a processing procedure blood edge relation corresponding to each data asset table according to a blood edge propagation algorithm; the blood relationship of the processing procedure represents the father-son relationship between the data processing steps;

The cost allocation module is used for allocating the data production cost corresponding to the data asset to be evaluated into the data production cost corresponding to each data processing step for obtaining the data asset table according to the blood relationship of the processing procedure corresponding to the data asset table;

And the cost information determining module is used for accumulating the data production cost corresponding to each data processing step for obtaining the data asset table according to the blood relationship of the processing procedure, and obtaining the cost information of the data asset table.

9. The apparatus for evaluating the value of a data asset according to claim 6, wherein the data asset management unit comprises:

The judging matrix construction module is used for constructing a judging matrix of the analytic hierarchy process according to the data management elements corresponding to the data asset table;

The management element weight determining module is used for comparing the relative importance among different data management elements corresponding to the data asset table according to the judging matrix and determining the weight of each data management element corresponding to the data asset table;

The weight vector determining module is used for carrying out normalization processing on each column of the judging matrix and calculating the weight vector of each data management element corresponding to the data asset table;

the comprehensive weight determining module is used for calculating the comprehensive weight of each data management element corresponding to the data asset table according to the weight vector of each data management element corresponding to the data asset table;

A data management score determining unit, configured to determine a data management score of a data asset table according to each data management element corresponding to the data asset table and a comprehensive weight thereof;

the data management element comprises a basic element, a quality element and a use element;

The base elements include data size;

the mass element comprises: accuracy, consistency, integrity, normalization, timeliness;

the use elements include: accessibility and heat of use.

10. The apparatus for evaluating the value of a data asset according to claim 6, wherein the data asset evaluation unit comprises:

The total value determining module is used for adding the data management score and the cost information of each data asset table to obtain the total value of the data asset table;

the total value weight determining module is used for determining the weight corresponding to the total value of the data asset table according to the application scene and the contribution degree of the data asset table;

And the asset return rate determining module is used for weighting and tie the total values of all the data asset tables according to the weights corresponding to the total values of all the data asset tables to obtain the asset return rate of the data asset to be evaluated.