Business processing system for data assets

Abstract

The invention provides a business processing system for data assets. According to the method, the right of the data assets can be confirmed, for example, the data asset holding right, the data asset processing and using right, the data asset operating right and the like are clearly judged. The implementation path of the financial value of the data assets is considered, financing and transaction of the data assets are supported, and enterprises are helped to use the data assets for financial innovation. According to the method, the data assets can be capalized, the difficulty of cost collection is reduced, and the cost collection is a process of identifying, classifying, metering and summarizing various costs involved in the whole life cycle of the data assets from generation, processing to application.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a business processing system for data assets. Background Art

[0002] With the rapid development of the digital economy, data assets are becoming increasingly prominent as a core competitive element for enterprises. The quantification and accounting treatment of their value have become key issues in corporate financial management. Increasingly, the inclusion of data assets in financial statements has become a fundamental part of corporate financial management. my country was one of the first countries in the world to initiate this process, providing standards for the management and evaluation of corporate data assets. This move not only helps to demonstrate the value of data assets and promote the circulation and use of data, but also fosters a data industry ecosystem and enhances data security management. Summary of the Invention

[0003] This application provides a data asset business processing system, which includes:

[0004] Data asset quality control module, data asset compliance confirmation module, data asset value assessment module, data asset accounting measurement module, and data asset finance module;

[0005] The data asset quality control module is used to detect the quality of data assets and correct data assets that do not meet quality requirements;

[0006] The data asset compliance confirmation module is used to clarify the ownership of data assets, including the right to hold data assets, the right to process and use data assets, and the right to operate data assets;

[0007] The data asset value assessment module is used to evaluate the costs and benefits of data assets during the collection, storage, processing, mining, protection or research and development process;

[0008] The data asset finance module is used to generate digital asset certificates based on the data assets owned or controlled by the enterprise itself.

[0009] In an optional implementation, the data asset quality control module is further configured to determine the lineage path of the data asset.

[0010] In an optional implementation, the data asset quality control module is further configured to provide a complete view of the data asset from the source of the data asset to each application that applies the data asset based on the lineage path of the data asset.

[0011] In an optional implementation, the data asset quality control module is further used to track the flow and / or changes of data assets and evaluate the impact of the flow and / or changes of data assets on downstream systems of the data assets.

[0012] In an optional implementation, the data asset quality control module is further configured to continuously improve the quality of data assets by adopting a Plan-Do-Check-Act (PDCA) approach.

[0013] In an optional implementation, the data asset compliance confirmation module is also used to classify and grade data assets.

[0014] In an optional implementation, the data asset compliance confirmation module also includes an expert think tank system, which includes an external expert knowledge base that brings together expert resources in the fields of law, data protection, and industry standards.

[0015] In an optional implementation, the data asset compliance confirmation module is also used to integrate or share data assets across departments or teams to ensure consistency and integrity of data assets between different departments or teams.

[0016] In an optional implementation, the data asset value assessment module is further configured to estimate the value of the data asset based on the market transaction price of the data asset.

[0017] In an optional implementation, the data asset value assessment module is also used to assess the extent to which the acquisition cost, profitability, transaction price, market application, market size, market share, and duration of competition of data assets affect the value of data assets.

[0018] The technical solution provided by this application may have the following beneficial effects:

[0019] This application can confirm the ownership of data assets, such as the right to hold data assets, the right to process and use data assets, and the right to operate data assets.

[0020] This application considers the path to realizing the financial value of data assets, supports the financing and trading of data assets, and helps enterprises use data assets for financial innovation.

[0021] This application can capitalize data assets and reduce the difficulty of cost aggregation. Cost aggregation is the process of identifying, classifying, measuring and summarizing various costs involved in the entire life cycle of data assets from generation, processing to application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of a data asset business processing system of this application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0024] The inclusion of data assets in the balance sheet also faces challenges such as difficulty in confirmation, evaluation, and measurement. Enterprises need to strengthen data compliance and governance, establish relevant accounting systems, accelerate the training of professional talents, and explore financialization paths.

[0025] In this context, enterprises need to fully understand the technical background of data asset inclusion and grasp the development of the digital economy to ensure the effective management and maximum utilization of data assets.

[0026] The business processing system for data assets provides a centralized, standardized and automated environment for managing and evaluating data assets.

[0027] The business processing system of data assets can support the collection, processing, storage and analysis of data assets, ensure the quality and security of data assets, and provide tools for data asset value assessment and measurement to help enterprises overcome the technical difficulties in entering data assets into the balance sheet.

[0028] Specifically, refer to Figure 1 , which shows a schematic diagram of a data asset business processing system of the present application, the system includes:

[0029] Data asset quality control module, data asset compliance confirmation module, data asset value assessment module, data asset accounting measurement module and data asset finance module.

[0030] The data asset quality control module is used to detect the quality of data assets and correct data assets that do not meet quality requirements.

[0031] It is also used to determine the lineage path of data assets.

[0032] It is also used to provide a complete view of data assets from the source of the data assets to the various applications that apply the data assets based on the lineage path of the data assets.

[0033] It is also used to track the flow or changes of data assets and evaluate the impact of data asset changes on downstream systems of data assets.

[0034] It is also used to adopt the PDCA (Plan-Do-Check-Act) cycle to continuously improve the quality of data assets.

[0035] The data asset compliance confirmation module is used to clarify the ownership of data assets, including the right to hold data assets, the right to process and use data assets, and the right to operate data assets.

[0036] The right to process and use data assets and the right to operate data assets are two core rights.

[0037] The right to process and use data assets focuses on technology development, protects the rights and interests of data asset processors in legally processing data assets, and promotes "data availability".

[0038] The data asset management rights focus on market operations, guarantee the market benefits of data asset operators, and promote the "use of data".

[0039] It is also used to classify and grade data assets.

[0040] It also includes an external expert knowledge base, which brings together expert resources in multiple fields such as law, data protection, and industry standards.

[0041] It also supports the integration and sharing of data assets across departments and teams, ensuring data consistency and integrity between different systems and teams, improving the availability and collaborative efficiency of data assets, and continuously improving the quality of data assets to help enhance the value of data assets, providing a continuously optimized quality assurance for data assets to be included in the table.

[0042] The data asset quality control module includes: an intelligent data asset quality monitoring unit, a data lineage and impact analysis unit, and a data asset quality improvement unit.

[0043] The intelligent data asset quality monitoring unit is used to automatically identify quality issues of data assets, such as duplicate records, inconsistencies or incompleteness, using machine learning algorithms, and automatically correct them.

[0044] This not only improves the accuracy and reliability of data assets, but also provides a solid foundation for subsequent data asset analysis and application.

[0045] Duplicate records can be detected using hash algorithms, similarity algorithms, or classification algorithms.

[0046] Detecting inconsistencies can use rule engines, decision trees, or anomaly detection algorithms, etc.

[0047] Incompleteness detection can be done using statistical methods, KNN imputation methods, or model-based imputation methods.

[0048] Correcting data quality includes merging, deletion, rule-based correction, and filling in missing values.

[0049] Among them, when correcting duplicate records, you can merge duplicate records, for example, merge the field values ​​of duplicate records into one record, for example, take the average value (numeric field) or take the longest string (text field), or delete duplicate records, for example, directly delete the duplicate records and retain one record in the same record.

[0050] When correcting inconsistencies, you can correct inconsistent records based on business rules. For example, you can adjust the value of a date field to conform to chronological order, or predict the correct value based on a machine learning model and replace the inconsistent value.

[0051] When correcting incompleteness, missing values ​​can be filled, for example, using an imputation method (such as statistical methods, KNN imputation, model-based imputation, etc.) to fill missing values, or deleting missing values. For example, if there are many missing values ​​and they have little impact on the analysis results, records containing missing values ​​can be deleted.

[0052] The data lineage and impact analysis unit is used to determine the lineage path (DataLineage Path) of data assets through graph algorithms. It can provide a complete view of data assets from the source of the data assets to the various applications that apply the data assets. It helps to track the flow or changes of data assets and helps to evaluate the impact of data asset changes on the downstream systems of data assets. Therefore, when data assets are entered into the table, the source and change history of the data can be clearly displayed, enhancing the transparency and traceability of data assets.

[0053] The lineage path of a data asset refers to the flow of data from its source to its final location of use, reflecting the complete history of its generation, processing, conversion, and storage. Graph algorithms can effectively determine and analyze the lineage path of data assets. A data asset lineage path can be a directed graph, where nodes represent data asset entities (such as tables, fields, and files), and edges represent the flow relationships between data assets.

[0054] The data asset quality improvement unit is used to improve (e.g., continuously improve) the quality of data assets by adopting PDCA (Plan-Do-Check-Act), including regular data asset quality assessment, problem identification, repair and preventive measures, etc.

[0055] Secondly, it supports the integration and sharing of data assets across departments and teams, ensures the consistency and integrity of data assets between different systems and teams, improves the availability and collaborative efficiency of data assets, and continuously improves the quality of data assets, which helps to enhance the value of data assets and provides a continuously optimized quality assurance for the inclusion of data assets in the table.

[0056] PDCA includes: Plan (setting quality goals and evaluation indicators for data assets), Execution (implementing quality assessments and remediation measures for data assets), Check (identifying quality issues with data assets and evaluating remediation effects), and Action (taking remediation and preventive measures to optimize and improve processes).

[0057] After each quality improvement of data assets, the next improvement plan and improvement program can be adjusted based on the results of the improvement, and then the next data asset quality improvement batch can be entered to gradually improve the quality of data assets.

[0058] The quality assessment of data assets is the starting point of PDCA. The purpose is to fully understand the current quality status of data assets and provide a basis for subsequent improvements. It is used to evaluate the accuracy of data assets (whether data assets are correct and whether data assets are consistent with actual conditions), completeness (whether data assets are missing and whether data assets contain all necessary information), consistency (whether data assets are consistent across different data sources or systems), timeliness (whether data assets are updated in a timely manner and whether data assets reflect the latest information), availability (whether data assets are easy to access and use and whether the format of data assets meets requirements) and uniqueness (whether data assets have duplicate records), etc.

[0059] You can use data asset quality assessment tools (such as Informatica Data Quality, Trifacta, etc.) to automatically scan data assets and generate quality test results. You can also use statistical methods (such as calculating the proportion of missing values, the proportion of outliers, etc.) to evaluate the quality of data assets and obtain quality test results.

[0060] Problem identification is the process of identifying quality issues in data assets.

[0061] Quality issues include: duplicate records (multiple identical or highly similar records in a data asset), inconsistency (logical contradictions between data assets in different systems or fields), incompleteness (missing certain fields or records in a data asset), incorrect data (certain values ​​in a data asset do not conform to business rules or logic), and outdated data (data assets are not updated in a timely manner and cannot reflect the latest situation).

[0062] Data assets can be checked for compliance based on predefined business rule engines. Machine learning algorithms (such as Isolation Forest and One-Class SVM) can be used to identify abnormal data assets, trace the source of problems through data lineage paths, and collect feedback from data asset users about issues they discover during actual use.

[0063] After quality issues are identified, actions can be taken to remediate the quality issues of the data assets.

[0064] For example, for duplicate records, you can merge the duplicate records (merge the field values ​​of the duplicate records into one record), or delete the duplicate records (directly delete the duplicate records and keep one record).

[0065] For inconsistencies, you can directly correct inconsistent records based on business rules, or use machine learning models to predict correct values ​​and replace inconsistent values.

[0066] To address incompleteness, you can fill missing values, for example, using statistical methods (such as mean, median, mode), KNN imputation, or model-based imputation methods. Alternatively, you can delete missing values. For example, if there are many missing values ​​and they have little impact on the analysis, you can delete records containing missing values.

[0067] For erroneous data assets, you can use scripts or tools to delete or correct the erroneous data assets.

[0068] Secondly, data assets can be versioned and historical versions of data assets can be retained for traceability.

[0069] The data asset compliance confirmation module includes: data asset rights analysis unit, data asset classification and hierarchical management unit, and expert think tank system.

[0070] The "data assets" in the data processing and use rights refer to raw data assets that have not been deeply processed or preliminarily processed data assets, including collected user behavior records, sensor data, public data sources, etc., as well as data asset collections that have undergone basic processing such as cleaning, desensitization, and deduplication (such as the original library of user tags after desensitization). It belongs to the upstream link of the data development chain, emphasizes the technical processing rights of data assets (such as the legal processing rights stipulated in the "Data Security Law"), does not involve market-based transactions, and the scope of rights is limited to technical operations within the authorized scope. The "data assets" in the data asset operation rights refer to the final results with commercial value formed through deep processing, analysis, modeling, etc., including data reports (such as the "Media Consumption Behavior Analysis Report"), data interfaces (such as user portrait APIs), data services (such as risk control model deployment), data derivatives (such as industry trend forecasting tools), etc.

[0071] The data asset rights analysis unit is used to clarify the ownership of data assets, including the right to hold data assets, the right to process and use data assets, and the right to operate data assets.

[0072] In this way, the ownership issue of data assets can be resolved, ensuring that the legitimate rights and interests of data assets are protected, and at the same time providing a legal basis for the transaction and circulation of data assets.

[0073] For example, through data asset inventory, determination of the data asset holder, legal compliance and document management, the ownership of data assets can be clarified, and then the data asset holding rights can be clarified.

[0074] For example, through authorization management, data usage agreements, auditing and monitoring, and training and education, the processing and use of data can be standardized, thereby clarifying the rights to process and use data assets.

[0075] For example, through data asset definition, authorization and cooperation, compliance management, and marketing and operation, the legal development and operation of data assets can be ensured, thereby clarifying the operating rights of data assets.

[0076] The data asset classification and grading management unit is used to classify and grade data assets, ensuring that data assets are properly managed according to their importance and sensitivity (personal privacy information and commercial confidential information, etc.). It helps enterprises formulate corresponding compliance strategies based on the different characteristics of data assets and provides a basis for refined management of data assets.

[0077] Data asset classification includes the process of grouping data assets according to their nature, purpose and source, which can help quickly identify the type and purpose of data assets and provide a basis for subsequent classification and management.

[0078] For example, it can be classified according to data type, including: structured data (such as tabular data in databases (relational databases, data warehouses, etc.)), semi-structured data (such as data in JSON or XML format) and unstructured data (such as text files, pictures, audio or video, etc.).

[0079] Alternatively, data can be classified according to their source, including: internally generated data (data generated by the company's internal systems), externally acquired data (data purchased or acquired through cooperation with third parties (such as market research data, public data sets)), and user-generated data (data generated by users when using products or services (such as social media data, user feedback)).

[0080] Alternatively, data can be classified according to their purpose, including: data that records business transaction processes (such as order data, payment data), data used for data analysis and decision support (such as report data, data in data warehouses), data used for data standardization and reference (such as dictionary data, coding tables), and data used to describe the core business entities of the enterprise (such as customer information, product information).

[0081] Data asset classification is the process of classifying data assets based on their importance and sensitivity. Classification helps determine the protection level, access rights, and management policies for data assets.

[0082] For example, it can be graded according to sensitivity, including: high sensitivity (involving personal privacy information (such as ID number, bank card number, health information), commercial confidential information (such as financial statements, business plans)), medium sensitivity (involving corporate operating data (such as sales data, customer information), internal management data (such as employee information)) and low sensitivity (public data (such as market conditions), reference data (such as industry standards)).

[0083] Alternatively, it can be graded according to importance, including: critical data (data that is critical to the core business and operations of the enterprise (such as financial data, customer master data)), important data (data that has a significant impact on part of the enterprise's business (such as sales data, market research data)) and general data (data that has less impact on the enterprise's operations (such as log data, backup data)).

[0084] Alternatively, data can be automatically classified based on predefined rules (such as data field name, data format, and data source). For example, data with field names containing "ID number" or "bank card number" will be automatically classified as highly sensitive data.

[0085] Alternatively, content classification can be performed based on the content, for example, by using machine learning algorithms (such as text classification and sensitive information detection) to analyze the data content and automatically classify it. For example, natural language processing technology can be used to identify sensitive information in text.

[0086] The expert think tank system includes an external expert knowledge base, which brings together expert resources in multiple fields such as law, data protection, and industry standards.

[0087] These experts may come from law firms, consulting firms, industry organizations, and other institutions, and possess specialized knowledge and experience in handling data compliance disputes. The system automatically matches and recommends the most appropriate external expert based on the specific content and scope of the compliance dispute. This intelligent matching mechanism ensures that the issue is promptly addressed by the right expert. The system continuously learns and optimizes its compliance assessment models and rules based on feedback from external experts and actual results. This continuous learning and optimization mechanism ensures that the system handles future compliance disputes with greater accuracy and efficiency.

[0088] The data asset value assessment module includes: cost value assessment unit, benefit value assessment unit, market value assessment unit, risk value assessment unit, economic factor assessment unit and data application value assessment unit.

[0089] The cost valuation unit for data assets is used to assess the costs incurred during the collection, storage, processing, mining, protection, or R&D process of data assets. This includes upfront expenses, construction costs, operation and maintenance costs, and indirect costs. The value of data assets is typically determined based on their replacement cost, after deducting any depreciation.

[0090] P cost value = C collection + C storage + C processing + C mining + C R & D + C indirect.

[0091] The data asset income value assessment unit is used to evaluate the income of data assets based on their application value and quality. This involves the impact of different application areas, application scenarios, usage methods, and profit-making methods of data assets on data asset income.

[0092] P benefit value = benefit / (C collection + C storage + C processing + C mining + C research and development + C indirect).

[0093] The market value assessment unit is used to estimate the value of data assets based on the market transaction price of data assets through market methods. This requires collecting and analyzing market transaction data and considering factors such as supply and demand relationships and market trends.

[0094] The risk value assessment unit is used to consider the risks and opportunities that data assets may face in specific application scenarios and adjust the assessment results. Risk assessment helps determine the value fluctuations and potential losses of data assets.

[0095] The risks faced include: quality risks of data assets (inaccurate, incomplete or inconsistent data assets may lead to incorrect decisions, and outdated or untimely updates of data assets may affect business operations), security risks of data assets (leakage of data assets may lead to legal proceedings and reputation loss, and tampering or damage of data assets may affect business continuity), compliance risks of data assets (the collection, storage and use of data assets that do not comply with laws and regulations may result in fines), technical risks of data assets (outdated data asset storage and processing technology may lead to performance degradation, and insufficient data analysis tools may affect the utilization efficiency of data assets) and market risks of data assets (changes in market demand for data assets may lead to a decline in value, and the advantages of competitors' data assets may lead to loss of market share), etc.

[0096] The opportunities include: data asset-driven innovation (using data assets to develop new products or services and discover new market opportunities through data analysis), efficiency improvement (optimizing business processes and improving operational efficiency. Improving customer satisfaction and sales conversion rates through precision marketing), competitive advantage (owning high-quality data assets can enhance the competitiveness of an enterprise. The differentiation of data assets can become the core competitiveness of an enterprise), compliance advantage (avoiding legal risks and enhancing corporate reputation through compliance management. The management of compliant data assets can enhance customer trust) and technological progress (using new technologies (such as artificial intelligence and big data analysis) to enhance the value of data assets and improve the efficiency of data asset processing and analysis through technological innovation).

[0097] If there are obvious quality issues with data assets, the data asset valuation result will be lowered. For example, the adjusted value = original valuation value × (1-quality risk factor). The quality risk factor is 0.1 or 0.2, etc.

[0098] If the data presents a security risk, the data asset value assessment result will be lowered. For example, the adjusted value = original assessment value × (1-security risk factor). The security risk factor is 0.15 or 0.2, for example.

[0099] If the data presents compliance risks, the data asset valuation result will be lowered. For example, adjusted value = original valuation × (1 - compliance risk factor). The compliance risk factor is 0.1 or 0.15.

[0100] If the data processing technology is at risk of becoming obsolete, the data asset valuation result will be lowered. For example, the adjusted value = original valuation × (1 - technology risk factor). The technology risk factor is 0.05 or 0.06.

[0101] If the market demand for data assets is unstable, the data asset valuation result will be lowered. For example, the adjusted value = original valuation × (1-market risk coefficient). The market risk coefficient is 0.1 or 0.12.

[0102] If a data asset has significant innovation opportunities, the data asset valuation result will be increased. For example, adjusted value = original valuation × (1 + innovation opportunity coefficient). The innovation opportunity coefficient is 0.2 or 0.18.

[0103] The economic factor evaluation unit is used to analyze economic factors such as the acquisition cost, profitability, transaction price, market application, market size, market share, and long-term competition of data assets, and to determine the extent to which these economic factors affect the value of data assets.

[0104] Determine the evaluation factors and their weights: Through expert scoring or historical data analysis, obtain the weights of each factor in the data asset acquisition cost, profit status, transaction price, market application, market size, market share and competition situation, collect relevant data of each factor in the data asset acquisition cost, profit status, transaction price, market application, market size, market share and competition situation, multiply the relevant data of each factor with its corresponding weight to obtain the corresponding value of each factor, and then sum the corresponding values ​​of each factor to obtain the degree of impact on the value of the data asset.

[0105] The data application value assessment unit is used to evaluate the value of data assets in data analysis, data mining, application development, etc., including the ability of data assets to generate income directly or indirectly as operating assets.

[0106] The data asset accounting measurement module includes: accounting entry directory unit, data asset disclosure unit, and data asset depreciation and amortization unit, etc.

[0107] The accounting entry catalog unit is used to distinguish the types of data assets, such as the types of externally acquired data assets and internally generated data assets, and adopt different measurement methods for data assets according to different types.

[0108] For example, when an accounting element is first recognized, an appropriate measurement attribute is selected to measure the monetary value of the accounting element, for example, initial measurement based on the historical cost of acquiring or developing a data asset.

[0109] Internally generated data assets include data generated by an enterprise during its daily operations. This data is usually related to the enterprise's core business and has high value and sensitivity.

[0110] Internally generated data assets include: transaction data (such as order records, payment information, sales data, etc.), customer data (such as basic customer information, purchase history, preference data, etc.), employee data (such as employee personal information, performance records, training records, etc.), operational data (such as production records, inventory data, logistics information, etc.) and financial data (such as financial statements, cost data, revenue data, etc.).

[0111] Externally acquired data assets include data that an enterprise obtains from external channels, which may come from partners, suppliers, third-party data providers or public data sources.

[0112] Externally acquired data assets include: market research data (such as consumer behavior research, market trend analysis data), industry reports (such as macroeconomic data, industry analysis reports), third-party data services (such as credit assessment data, geographic location data, social media data), public data sets (such as government public data, academic research data) and partner shared data (such as logistics data and sales data shared by supply chain partners).

[0113] The measurement method of data assets is used to quantitatively assess the value of data assets.

[0114] Internally generated data assets are usually closely related to the core business of an enterprise, and their measurement needs to reflect the value of the data assets to the business.

[0115] For example, the cost method is used to measure data assets based on their collection, storage, processing, and maintenance costs. It is suitable for the initial measurement of data assets, especially when the acquisition cost of data assets is high. The value of data assets = collection cost + storage cost + processing cost + maintenance cost.

[0116] For example, the income approach is used to measure data assets based on the expected benefits they bring to the enterprise. It is applicable to situations where data assets make significant contributions to the enterprise's business and the benefits can be reasonably predicted. The value of data assets = (expected benefits × discount rate).

[0117] Externally acquired data assets usually have clear acquisition costs and usage restrictions, and their measurement methods need to reflect the market value and usage benefits of the data.

[0118] For example, the cost method is used to measure data based on the purchase cost, cooperation cost or acquisition cost. It is suitable for the initial measurement of data assets, especially when the acquisition cost of data is clear. The value of data assets = purchase cost + cooperation cost + acquisition cost.

[0119] Another example is the income approach, which measures the expected returns from data assets to the business. This approach is applicable when data assets contribute significantly to the business and the returns can be reasonably predicted. The value of a data asset = (expected returns x discount rate).

[0120] The data asset disclosure unit is used to disclose relevant information about data assets in financial reports, including the description of data assets, measurement basis, valuation method and the impact of data assets on the financial status of the enterprise.

[0121] The description of data assets is an explanation of the basic characteristics and properties of data assets, including the type and source of data assets.

[0122] The types of data assets include: structured data (such as tabular data in a database), semi-structured data (such as data in JSON and XML formats), and unstructured data (such as text files, pictures, audio, video, etc.).

[0123] The sources of data assets include: internally generated data (such as data generated by the company's internal business systems (ERP, CRM, financial systems, etc.)) and externally acquired data (such as data purchased from third-party data providers, data shared by partners, public data sets, etc.).

[0124] The uses of data assets include; transaction data (data used to record business transaction processes), analytical data (data used for data analysis and decision support), reference data (data used for data standardization and reference), and master data (data that describes the core business entities of the enterprise).

[0125] The data asset depreciation and amortization unit is used to allocate the value of data assets, determine their depreciation or amortization over a certain period, reflect the consumption of data asset value, and conduct subsequent value assessment and adjustments for confirmed data assets. This includes assessing the appreciation or depreciation of data assets and performing corresponding accounting treatment in accordance with accounting standards.

[0126] The data asset finance module includes: data asset credit unit, data asset trust unit and data asset securitization unit, etc.

[0127] The Data Asset Credit Unit is used to generate digital asset certificates for data assets owned or controlled by an enterprise through data rights confirmation (determining the right to hold, process, use, and benefit from data assets). This digital certificate can then be used as collateral for pledge financing. The unit supports the financing process of using data assets as collateral, including pledge registration, value assessment, and collateral management. It also provides an interface with financial institutions to achieve automated and intelligent data asset pledge financing.

[0128] Conduct a comprehensive inventory of internal data assets to clarify their sources, types, and uses. Categorize data assets based on factors such as content, usage, and sensitivity, such as core data, important data, and general data. Determine the purpose of data asset ownership confirmation, such as data asset inclusion, data asset compliance management, and data asset transactions or financing. Develop targeted compliance work plans based on business needs.

[0129] Verify the generation or acquisition path of data assets to ensure the legitimacy and traceability of the data. For personal data, verify the identity of the data owner through authentication and authorization. Based on the source and usage scenarios of the data assets, determine the ownership, use, processing, and operation rights of the data assets, typically through explicit provisions in contracts or agreements. Ensure that the source of data assets is compliant and that the company's acquisition of data assets does not violate laws and regulations or infringe on the legitimate rights and interests of third parties. Ensure that the processing of data assets is compliant and adheres to the principles of legality, propriety, and necessity. Register the data intellectual property rights for innovative and original data assets. Register data assets for internal corporate management or financing. List data assets on data exchanges. Obtain notarization of the authenticity and legitimacy of data assets through a notary public. After review by the registration authority, obtain a digital asset certificate. This certificate can serve as the basis for data transactions, financing collateral, data asset inclusion, accounting, and dispute arbitration.

[0130] Data trust units enable financial institutions to establish property rights trusts for data assets collected and aggregated by data asset holders in accordance with law, based on the entrustment of data subjects. They also provide legal and compliant custody services to facilitate the establishment, management, and distribution of data asset trusts, including the independence of trust property, fiduciary obligations, and protection of beneficiary rights. Legal and financial advisory services are provided for data asset trusts to ensure the compliance and efficiency of trust operations.

[0131] The data asset securitization unit is used to transform data assets into tradable securities products based on the future cash flow of data assets, realize the monetization and circulation of data assets, support the design and issuance of data asset securitization products, provide secondary market trading and circulation services, enhance the liquidity of data asset securities, and use big data analysis technology to assess the risks of data assets, including market risk, credit risk and operational risk.

[0132] To address the key technical challenges in the process of entering data assets into tables, this application adopts advanced DataOps concepts and achieves efficient processing and accurate evaluation of data assets through automated and agile data management practices. The advantages are as follows:

[0133] Data lifecycle management: covers the entire process of data assets from collection, processing, storage to application, ensuring the visualization, traceability, manageability and applicability of data assets.

[0134] Data asset quality control: Combining data warehouse theory and big data characteristics, it achieves accurate calculation of data cost and data lineage. Through graph algorithms, it can accurately determine the data lineage path to ensure the quality and consistency of data assets.

[0135] Graph algorithms include: depth-first search (DFS), breadth-first search (BFS), Dijkstra algorithm, Bellman-Ford algorithm, Floyd-Warshall algorithm, and Prim's algorithm.

[0136] Data asset value assessment: Using the hierarchical analysis method and the data asset value calculator, we comprehensively consider the economic and non-economic factors of data assets to achieve a systematic assessment of the value of data assets.

[0137] The Analytic Hierarchy Process (AHP) is a decision-making method that breaks down complex problems into multiple hierarchical structures and determines the weights of each factor through pairwise comparison. In data asset valuation, AHP can help systematically analyze and weigh economic and non-economic factors.

[0138] A data asset value calculator is used to calculate the specific value of a data asset based on input economic and non-economic factors. It can be a software program, an online tool, or a calculation framework based on formulas and models.

[0139] For example, consider combining data economic and non-economic factors and list all indicators that need to be considered. Categorize these indicators into two broad categories: economic and non-economic. Then, further break down the specific indicators. Compare each indicator pairwise, construct a judgment matrix, and calculate weights. Use a data asset value calculator to input the specific data for each indicator and calculate the overall value based on the weights. Based on the evaluation results, analyze the value of the data asset and make adjustments and optimizations based on actual circumstances.

[0140] The economic factors of data assets include the financial value and economic benefits directly related to data assets. These factors can usually be assessed quantitatively. For example, the market value of data (the transaction price or potential transaction value of data in the market, the scarcity and uniqueness of data), the direct benefits of data (the direct revenue generated by data used for product sales, advertising, etc., and data-driven business growth), the cost of data (the costs of data collection, storage, processing, and maintenance, and data security and compliance costs), the potential benefits of data (the potential benefits from data used for innovation, new product development, etc., and the cost savings from data used to improve operational efficiency), the depreciation and devaluation of data (the potential loss of value of data over time (such as outdated data), and the frequency and timeliness of data updates).

[0141] Non-economic factors of data include factors related to the value of data assets but difficult to measure directly in monetary terms. These factors typically involve data quality, security, and compliance.

[0142] For example, the quality of data assets (data accuracy, completeness, consistency and reliability, data update frequency and timeliness), data security (data confidentiality, integrity and availability, data backup and recovery capabilities), data compliance (whether the data complies with laws and regulations (such as GDPR, CCPA, etc.), whether the use of data complies with industry standards and corporate policies), data asset availability (data usability and accessibility, data format and degree of standardization), data strategic value (the degree to which data supports the company's strategic goals, the importance of data in the company's ecosystem), and data scalability (whether the data can support the company's future growth and expansion needs, the flexibility and adaptability of the data), etc.

[0143] Data asset compliance confirmation: Based on compliance checking tools, ensure the compliance of data asset sources, processing, ownership, and applications, and support enterprises to meet legal and regulatory requirements.

[0144] Accounting measurement of data assets: An accounting entry catalog is constructed (the accounting entry catalog can be constructed based on the "Interim Provisions on Accounting Treatment of Enterprise Data Assets", etc.) to achieve accurate classification and measurement of data assets included in the table, ensuring the accuracy of financial statements.

[0145] Data security and privacy protection: Adopt data encryption technology and privacy protection measures to ensure the security and compliance of data assets and prevent data leakage and unauthorized access.

[0146] Data asset finance: It considers the path to realize the financial value of data assets, supports the financing and trading of data assets, and helps enterprises use data assets for financial innovation.

[0147] This application provides tools for data asset value assessment and measurement to help enterprises overcome the technical difficulties in recording data assets in their balance sheets.

[0148] Combining data warehouse theory and big data characteristics, it achieves accurate calculation of data cost and data lineage, solves data lineage path problems through graph algorithms, and ensures the quality and consistency of data assets.

[0149] By using multi-level analysis methods and a data asset value calculator, we can comprehensively consider the economic and non-economic factors of data assets and achieve a systematic assessment of the value of data assets.

[0150] This application passes compliance checks to ensure the compliance of data asset sources, processing, ownership, and applications, supporting enterprises to meet legal and regulatory requirements.

[0151] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0152] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0153] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0154] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0155] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0156] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0157] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0158] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0159] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0160] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A data asset business processing system, characterized in that: The system comprises: Data asset quality control module, data asset compliance confirmation module, data asset value assessment module, data asset accounting measurement module, and data asset finance module; The data asset quality control module is used to detect the quality of data assets and correct data assets that do not meet quality requirements; The data asset compliance confirmation module is used to clarify the ownership of data assets, including the right to hold data assets, the right to process and use data assets, and the right to operate data assets; The data asset value assessment module is used to evaluate the costs and benefits of data assets during the collection, storage, processing, mining, protection or research and development process; The data asset finance module is used to generate digital asset certificates based on the data assets owned or controlled by the enterprise itself.

2. The system according to claim 1, wherein: The data asset quality control module is also used to determine the lineage path of data assets.

3. The system according to claim 1 or 2, characterized in that The data asset quality control module is also used to provide a complete view of the data assets from the source of the data assets to the various applications that apply the data assets based on the lineage path of the data assets.

4. The system according to claim 1, wherein: The data asset quality control module is also used to track the flow and / or changes of data assets and evaluate the impact of the flow and / or changes of data assets on the downstream systems of data assets.

5. The system according to claim 1, wherein: The data asset quality control module is also used to continuously improve the quality of data assets by adopting the Plan-Do-Check-Act PDCA approach.

6. The system according to claim 1, wherein: The data asset compliance confirmation module is also used to classify and grade data assets.

7. The system according to claim 1, wherein: The data asset compliance confirmation module also includes an expert think tank system, which includes an external expert knowledge base that brings together expert resources in the fields of law, data protection, and industry standards.

8. The system according to claim 1, wherein: The data asset compliance confirmation module is also used to integrate or share data assets across departments or teams to ensure consistency and integrity of data assets between different departments or teams.

9. The system according to claim 1, wherein: The data asset value assessment module is also used to estimate the value of data assets based on the market transaction price of data assets.

10. The system according to claim 1, wherein: The data asset value assessment module is also used to evaluate the impact of the acquisition cost, profitability, transaction price, market application, market size, market share and long-term competition of data assets on the value of data assets.

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