Master data modeling method, device, and storage medium

Abstract

The application discloses a master data modeling method and device and a storage medium, and relates to the technical field of big data.The method comprises the following steps: acquiring data objects with the same granularity as master data from a data warehouse based on preset data standards of the master data; performing standardization processing on the data objects to obtain standard data objects; performing master data quality inspection on the standard data objects, and taking a data table constructed based on the standard data objects as a master data model when the master data quality inspection is passed.The application can improve the adaptation degree of the master data model and actual business demands, and improve the master data management efficiency.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a master data modeling method, device and storage medium. Background Technology

[0002] As enterprises accelerate their digital transformation, the master data in enterprise-level data warehouses needs to support cross-system and cross-departmental business collaboration and data analysis. The quality of its construction directly determines the reusability of data assets and decision support capabilities.

[0003] Currently, the construction of data warehouse master data generally follows the same construction model as business data construction, led by the IT department. First, existing enterprise data is divided into various data subjects as the basis for the logical model. Then, existing data is populated into each subject, and finally, master data modeling is performed using third normal form or dimensional modeling. This master data modeling approach simply summarizes data from each partition, which easily leads to a disconnect between master data construction and actual business needs. The constructed master data model is difficult to adapt to the actual business requirements of various departments, resulting in a mismatch between the master data model and actual business requirements.

[0004] Therefore, how to improve the fit between the master data model and actual business needs in order to improve the efficiency of master data management is an urgent problem to be solved. Summary of the Invention

[0005] The main purpose of this application is to provide a master data modeling method, device and storage medium, which aims to improve the adaptability of the master data model to actual business requirements, so as to improve the efficiency of master data management.

[0006] To achieve the above objectives, this application provides a master data modeling method, the master data modeling method comprising: Based on the preset data standard of the master data, data objects with the same granularity as the master data are obtained from the data warehouse; The data objects are standardized to obtain standard data objects. A master data quality check is performed on each of the aforementioned standard data objects, and when the master data quality check passes, the data table constructed based on each of the aforementioned standard data objects is used as the master data model.

[0007] In one embodiment, the step of standardizing each of the data objects to obtain a standard data object includes: Determine the business entity, data source, granularity, and analysis dimension of each data object; Normalize data objects that have the same business entity but different data sources to obtain the first data object; Data objects with different business entities but the same granularity are merged to obtain a second data object; Data objects with the same analytical dimensions but different business entities are associated to obtain associated third data objects. The first data object, the second data object, and each of the associated third data objects are used as standard data objects.

[0008] In one embodiment, the step of performing master data quality checks on each of the standard data objects includes: Based on the aforementioned standard data objects, construct a data table; Perform a master data quality check on the data records in the data table to obtain the quality check results.

[0009] In one embodiment, the data table includes a first data table and a second data table, and the step of constructing the data table based on each of the standard data objects includes: For each customer, identify the target data object associated with that customer from among the aforementioned standard data objects; Based on the standard data objects belonging to user basic information in each of the target data objects, the first data table is constructed according to the customer granularity. Based on the standard data objects belonging to user identification information in each of the target data objects, at least one second data table is constructed according to the identification granularity.

[0010] In one embodiment, the step of performing a master data quality check on the data records in the data table to obtain the quality check result includes: A technical check is performed on the data records in the data table to obtain the first check result; A data standard check is performed on the information items of each data record in the data table to obtain a second check result; A third check result is obtained by performing a business-class check on multiple data records in the data table that are associated with preset business rules. The quality inspection result is determined based on the first inspection result, the second inspection result, and the third inspection result.

[0011] In one embodiment, the step of performing a technical inspection on the data records in the data table to obtain a first inspection result includes: Perform a uniqueness check on the data records in the data table to determine whether there are duplicate records, and obtain a uniqueness check sub-result; Determine the number of first data records in the data table at the current moment, and the number of second data records in the data table at a historical moment; The consistency check sub-result is obtained by comparing the number of the first data records with the number of the second data records. Based on the uniqueness check sub-result and the consistency check sub-result, the first check result is determined.

[0012] In one embodiment, the step of performing a data standard check on the information items of each data record in the data table to obtain a second check result includes: Check whether each information item of each data record conforms to the preset encoding rules to obtain the encoding compliance sub-result, wherein the encoding rules include limitations on the encoding length and the characters that make up the encoding; Check whether the code value of each information item in each data record belongs to the preset code value list to obtain the code value compliance sub-result; Check whether each information item of each data record meets the preset data supervision requirements to obtain the supervision compliance sub-result; The second inspection result is determined based on the coding compliance sub-result, the code value compliance sub-result, and the regulatory compliance sub-result.

[0013] In one embodiment, the step of performing a business-type check on multiple data records in the data table that are associated with preset business rules to obtain a third check result includes: Retrieve multiple data records in the data table that are associated with preset business rules; Based on the business scenario to which each of the data records belongs, obtain the corresponding associated verification rules; Each data record is verified according to the aforementioned association verification rules to obtain each business verification sub-result; Based on the results of each of the aforementioned business verification sub-results, the third inspection result is determined.

[0014] Furthermore, to achieve the above objectives, this application also provides a master data modeling apparatus, the master data modeling apparatus comprising: The data acquisition module is used to acquire data objects with the same granularity as the master data from the data warehouse based on the preset data standards of the master data; The standardization module is used to standardize each of the data objects to obtain standard data objects. The construction module is used to perform master data quality checks on each of the standard data objects, and when the master data quality checks pass, the data table built based on each of the standard data objects is used as the master data model.

[0015] In addition, to achieve the above objectives, this application also proposes an electronic device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the master data modeling method as described above.

[0016] In addition, to achieve the above objectives, this application also provides a storage medium, which is a computer-readable storage medium, on which a program implementing the master data modeling method is stored, and the program implementing the master data modeling method is executed by a processor to implement the steps of the master data modeling method as described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the master data modeling method described above.

[0018] This application provides a master data modeling method. First, based on the preset data standard of master data, data objects with the same granularity as master data are obtained from the data warehouse; each data object is standardized to obtain standard data objects; then, master data quality checks are performed on each standard data object, and when the master data quality check passes, the data table built based on the standard data objects is used as the master data model.

[0019] In summary, this application changes the traditional approach of directly modeling based on existing data by using pre-defined data standards as preliminary inputs for master data modeling. This ensures that data objects retrieved from the data warehouse are based on business definitions, guaranteeing consistency between the retrieved data objects and business semantics from the outset. Furthermore, data objects are standardized to eliminate discrepancies in definitions, and a quality control mechanism based on business standards is established before the model is finalized. Only after passing quality checks are data tables allowed to be designated as master data models. Therefore, this application ensures that the construction of the master data model is always guided and constrained by business standards, avoiding the problem of master data becoming disconnected from actual business operations due to simply aggregating existing data from various partitions in traditional methods. This improves the adaptability of the master data model to actual business requirements. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the first embodiment of the master data modeling method of this application; Figure 2 This is a schematic diagram of the data retrieval process involved in one embodiment of the master data modeling method of this application; Figure 3 This is a schematic diagram of a standardized process involved in an embodiment of the master data modeling method of this application; Figure 4 This is an embodiment of the master data modeling method of this application, illustrating the master-slave representation intent. Figure 5 This is a schematic diagram of the module structure of the master data modeling device in this application; Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the master data modeling method in this application embodiment.

[0023] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0025] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0026] Currently, the construction of data warehouse master data generally follows the same construction model as business data construction, led by the IT department. First, existing enterprise data is divided into various data subjects as the basis for the logical model. Then, existing data is populated into each subject, and finally, master data modeling is performed using third normal form or dimensional modeling. This master data modeling approach simply summarizes data from each partition, which easily leads to a disconnect between master data construction and actual business needs. The constructed master data model is difficult to adapt to the actual business requirements of various departments, resulting in a mismatch between the master data model and actual business requirements.

[0027] Therefore, how to improve the fit between the master data model and actual business needs in order to improve the efficiency of master data management is an urgent problem to be solved.

[0028] The main solution of this application is: based on the preset data standard of the master data, obtain data objects with the same granularity as the master data from the data warehouse; perform standardization processing on each of the data objects to obtain standard data objects; perform master data quality checks on each of the standard data objects, and when the master data quality check passes, use the data table built based on each of the standard data objects as the master data model.

[0029] This application changes the traditional approach of directly modeling based on existing data by using pre-defined data standards as preliminary inputs for master data modeling. This ensures that data objects retrieved from the data warehouse are based on business definitions, guaranteeing consistency between the retrieved data objects and business semantics from the outset. Furthermore, data objects are standardized to eliminate discrepancies in definitions, and a quality control mechanism based on business standards is established before the model is finalized. Only after passing quality checks are data tables allowed to be designated as master data models. Therefore, this application ensures that the construction of the master data model is always guided and constrained by business standards, avoiding the problem of master data becoming disconnected from actual business operations due to simply aggregating existing data from various partitions, as is common in traditional methods. This improves the adaptability of the master data model to actual business requirements.

[0030] It should be noted that the execution subject of the master data modeling method in various embodiments of this application can be a master data modeling system, or a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of realizing the above functions, etc. This embodiment does not specifically limit it in this way. The following uses a master data modeling system as the execution subject as an example to describe this embodiment and the following embodiments.

[0031] Based on this, this application proposes a master data modeling method according to the first embodiment, please refer to... Figure 1 The master data modeling method includes steps S10 to S30: Step S10: Based on the preset data standard of the master data, obtain data objects with the same granularity as the master data from the data warehouse.

[0032] To address the issue that traditional technologies often rely solely on the IT department to lead master data development, resulting in a lack of clarity regarding master data construction between the governance team and business stakeholders, this embodiment of the application proposes that the governance team and business stakeholders jointly formulate unified specifications for each information item in the master data (i.e., the aforementioned preset data standards). These preset data standards include at least data definitions, encoding standards, code value standards, and regulatory standards.

[0033] A data warehouse is an enterprise-level data platform formed by centrally storing data from various source business systems through an ETL process. Internally, it is divided into several data partitions according to different functions, with the near-source model layer retaining a data structure largely consistent with the source systems. Granularity refers to the smallest business unit level represented by each record in a data object, such as customer granularity, account granularity, and product granularity. A data object is a data structure in a data warehouse used to describe a specific business entity; it can be a data table or a data view.

[0034] Using the data definitions in the preset data standards as the matching benchmark, the field semantics of each data object are compared one by one; data objects with the same granularity level as the master data target are selected from the near-source model layer of the data warehouse; the selected data objects with matching field semantics are used as the raw input for subsequent standardization processing.

[0035] For example, in a retail banking scenario, the default data standard defines "customer" as a natural person holding a valid identity document. Using this definition as the matching benchmark, data objects containing fields such as name, document type, document number, and date of birth are selected from the customer information table, credit card customer table, and wealth management customer table in the near-source model layer of the data warehouse. These data objects all satisfy the definition of "customer" granularity. In a bank-wide institutional data construction scenario, the default data standard defines "institution" as a business unit with independent operating qualifications and an institution code. Using this definition as the matching benchmark, data objects containing fields such as institution code, institution name, institution level, and superior institution code are selected from the institution tables of various business systems in the near-source model layer of the data warehouse. These data objects all satisfy the definition of "institution" granularity.

[0036] like Figure 2 The diagram illustrates the data retrieval process. The data warehouse contains multiple data partitions, such as PDM (Physical Data Model Layer), SUM (Common Processing Layer), BMART (Basic Data Mart Layer), and AMART (Application Data Mart Layer). Each data partition contains data objects related to "retail customers," which originate from different business systems or serve different application needs.

[0037] The identification of master data information items is led by the data governance team, based on preset data standards as input, combined with high-sharing analysis of information items at the same granularity as "retail customers" in each data partition within the data warehouse. Specifically, the data governance team analyzes the field meaning, usage frequency, and cross-system sharing degree of information items in each data partition according to the data definitions in the preset data standards. Information items that meet the data standard definitions and have a high degree of sharing are included in the master data information item set, while information items with low sharing degree or lacking clear business definitions (such as...) are excluded. Figure 2Items marked as "to be analyzed" or "none" such as "Customer Tag 1" and "Customer Manager" are temporarily excluded to achieve standardized confirmation of master data information items.

[0038] Step S20: Standardize each of the data objects to obtain standard data objects.

[0039] Since each data object originates from different business systems, their field naming, encoding rules, and data formats may differ. The purpose of standardization is to eliminate these differences, transforming the originally scattered and heterogeneous data objects into data objects with unified business definitions, unified field definitions, and unified encoding standards, i.e., standard data objects.

[0040] Determine the business entity type and source system described by each data object; for data objects with the same subject but different sources, merge them based on the common subject to eliminate duplication; for data objects with different subjects but the same granularity, merge them based on the common granularity to integrate fields; for data objects with the same analysis dimension but different subjects, establish a coding mapping relationship for association; output the results after merging, combining and association as standard data objects.

[0041] In this embodiment, step S20 may include: Step S201: Determine the business subject, data source, granularity, and analysis dimension of each data object.

[0042] The business entity is the core business entity type described by the data object, such as customer, institution, account, product, etc., used to identify the entity category to which the data object belongs at the business level. The data source is the name of the original business system from which the data object originates, such as core system, credit card system, anti-money laundering system, etc., used to trace the source of the data. Granularity is the smallest business unit level represented by each record in the data object; for example, natural person granularity means each record corresponds to one natural person, household registration granularity means each record corresponds to one household registration, and institution granularity means each record corresponds to one institution, used to measure the coarseness of the data. The analytical dimension is the observation perspective to which the data object belongs in business analysis, such as customer dimension, institution dimension, product dimension, etc., used to identify the perspective to which the data object belongs when associated with other entities.

[0043] The metadata and field semantics of each data object are read one by one; the business entity type described by the data object is identified according to the table name, field composition and business meaning, which is taken as the business subject; the business system to which the data object belongs is determined according to the source system identifier of the data object, which is taken as the data source; the granularity is determined according to the business unit level corresponding to each record in the data object; the analysis dimension is determined according to the perspective of the data object in cross-business analysis; the identification results of the four dimensions are used as the classification basis for subsequent standardization processing.

[0044] Step S202: Normalize the data objects that have the same business entity but different data sources to obtain the first data object.

[0045] Normalization is the process of merging data records belonging to the same natural entity from multiple data objects originating from different business systems into a single unique record, based on the same business entity. It can be understood as follows: since the same business entity may have different record formats in different systems (such as naming differences, overlapping fields, consistent granularity but overlapping content), the purpose of normalization is to eliminate duplicate descriptions of the same entity across systems, forming a single, authoritative entity record, based on the business entity.

[0046] The system filters out data objects that have the same business entity but different data sources; it performs record matching based on key identifier fields (such as ID number, customer number, etc.) shared by the data objects; it determines multiple successfully matched records as records describing the same natural entity, and merges them into a complete record according to preset field priority or field concatenation rules; it uses the business entity as the naming basis for the merged data object to obtain the unified data object (hereinafter referred to as the first data object for distinction).

[0047] For example, Figure 3 For a standardized process diagram, such as Figure 3 As shown in (a), the business entity of both the retail customer data object (source: retail system) and the credit card customer data object (source: credit card system) is "customer". Using document type and document number as matching keys, customer records belonging to the same natural person in the two data objects are merged, and the first data object obtained is the retail natural person.

[0048] Step S203: Merge the data objects with different business entities but the same granularity to obtain the second data object.

[0049] Merging is the process of combining multiple data objects with the same granularity but different business entities into a single data object according to unified field definitions and encoding rules. Since data objects under different business entities may have some common field attributes at the same granularity level, the purpose of merging is to integrate information of the same granularity scattered across different entities, eliminate unnecessary duplicate storage, and form a unified data view at that granularity level.

[0050] The process involves filtering out data objects with different business entities but the same granularity; identifying common field attributes (such as code, name, status, date, etc.) among the data objects; using this granularity as the naming basis for the merged data object; integrating the common fields of the data objects into the same data object according to unified field definitions and coding rules, while retaining the unique fields of each data object as extended attributes, resulting in the merged data object (hereinafter referred to as the second data object for distinction).

[0051] For example, such as Figure 3 As shown in (b), the retail account data object (business entity: retail business, granularity: account) and the wholesale account data object (business entity: wholesale business, granularity: account) both have a granularity of "account". During the merging process, the common fields of the two (account number, account opening date, account status, balance, etc.) are integrated according to a unified standard; the "retail account manager number" unique to the retail account and the "wholesale business approver" unique to the wholesale account are retained as extended attributes; the merged data object is named "account master data" with the "account" granularity.

[0052] Step S204: Perform association processing on data objects with the same analysis dimension but different business entities to obtain the associated third data objects.

[0053] Association processing is a process that addresses data objects belonging to the same analytical dimension but belonging to different business entities. It establishes a mapping relationship between their respective coding systems, enabling cross-entity data association without altering the individual data objects themselves. Since data objects under different business entities may each maintain their own independent coding system, the purpose of association is to establish a mapping bridge, allowing for data penetration queries and cross-referencing between different entities in subsequent business operations.

[0054] Select data objects with the same analysis dimensions but different business entities; obtain the coding fields and coding rules used to identify business entities in each data object; use one coding system as the base coding and establish the correspondence between the coding in other coding systems and the base coding; summarize the correspondence to form a mapping relationship set; attach the mapping relationship set to each data object to obtain the associated third data objects.

[0055] For example, such as Figure 3 As shown in (c), the bank has three sets of institutional coding systems: administrative institution codes maintained by the head office management platform, accounting institution codes maintained by the core system, and credit institution codes maintained by the credit system. All three are analyzed from an "institutional dimension," but their business entities are administrative management, accounting management, and credit management, respectively. During the association process, the administrative institution code is used as the basis to establish a mapping relationship between the accounting institution code, credit institution code, and administrative institution code, forming an "administrative code, accounting code, credit code" mapping table. This mapping table is then associated with the data objects of each institution to achieve unified mapping and cross-query of institutional codes across the bank.

[0056] Step S205: The first data object, the second data object, and each of the associated third data objects are used as standard data objects.

[0057] The first data object obtained from the normalization process, the second data object obtained from the merging process, and the associated third data objects are used as standard data objects.

[0058] Thus, by standardizing data objects, the embodiments of this application integrate originally scattered, heterogeneous, and inconsistent multi-source data objects into standard data objects under unified operating specifications, providing a high-quality data foundation for the subsequent construction of a consistent and authoritative master data model.

[0059] Step S30: Perform master data quality checks on each of the standard data objects, and when the master data quality checks pass, use the data table constructed based on each of the standard data objects as the master data model.

[0060] Master data quality inspection refers to the process of verifying the compliance of data content carried by standard data objects based on preset data standards. This includes technical integrity verification, standard-level format and code value verification, and business-level logical consistency verification. Passing the quality inspection means that the verification results of all inspection items meet the preset conditions and there are no blocking anomalies. The master data model is the final output, standardized and quality-verified collection of core business entity data that can be shared across systems. It is typically stored in the model layer of the data warehouse in the form of data tables.

[0061] A data table structure is constructed based on various standard data objects; quality checks are performed on the data records written to the data table, including uniqueness checks, record number consistency checks, encoding compliance checks, code value compliance checks, and business logic verification; when all checks pass, the data table is determined as the main data model and made available for external use; when there are anomalies in the checks, an exception handling mechanism is triggered, and the data table is not determined as the main data model.

[0062] This application embodiment changes the traditional approach of directly modeling based on existing data by using preset data standards as preliminary inputs for master data modeling. This ensures that data objects retrieved from the data warehouse are based on business definitions, guaranteeing consistency between the retrieved data objects and business semantics from the outset. Furthermore, data objects are standardized to eliminate discrepancies in definitions, and a quality control mechanism based on business standards is established before the model is finalized. Only after passing quality checks is a data table allowed to be designated as the master data model. Therefore, this application embodiment ensures that the construction of the master data model is always guided and constrained by business standards, avoiding the problem of master data becoming disconnected from actual business operations due to simply aggregating existing data from various partitions in traditional methods. This improves the adaptability of the master data model to actual business requirements.

[0063] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, step S30 may include: Step S301: Construct a data table based on each of the aforementioned standard data objects; Step S302: Perform master data quality checks on the data records in the data table to obtain the quality check results.

[0064] A data table is a data carrier used to persistently store standard data objects in a data warehouse, according to preset field definitions and storage structures.

[0065] Based on the field composition of each standard data object, the field list and storage structure of the data table are determined. The data table is created in the data warehouse, and the business data carried by each standard data object is written into the data table one by one. After the data records are written to the data table, the quality inspection process is started. Technical checks, data standard checks, and business checks are performed on each data record in the data table to determine whether each data record meets the preset quality requirements. The judgment results of all inspection items are summarized to generate a quality inspection result, which is used to indicate whether the current data table meets the conditions for being a master data model.

[0066] In this embodiment, the data table includes a first data table and a second data table, and step S301 may include: Step A10: For each customer, determine the target data object associated with the customer in each of the standard data objects; Step A20: Based on the standard data objects belonging to user basic information in each of the target data objects, construct the first data table according to customer granularity; Step A30: Based on the standard data objects belonging to user document information in each of the target data objects, construct at least one second data table according to document granularity.

[0067] A customer is a business entity to be processed in the master data construction process, and each standard data object belongs to a specific customer. A target data object refers to all data records associated with a specific customer, identified through matching association fields among all standard data objects.

[0068] User basic information describes the inherent attributes of a customer, such as name, date of birth, gender, nationality, marital status, and contact information. This information is relatively stable throughout the customer's lifecycle, changes infrequently, and corresponds one-to-one with the customer entity. Customer granularity means that each record in the data table corresponds to a unique natural person customer, and is the core granularity level of customer master data. User identification information describes the relevant data of the valid identification documents held by the customer, such as document type, document number, document validity period, issuing authority, and issuance date. A customer may hold multiple documents (ID card, passport, military officer's certificate, etc.), therefore, document granularity is usually finer than customer granularity, and one customer granularity record can correspond to multiple document granularity records.

[0069] The first data table is built based on basic user information and serves as the master table, storing the customer's core basic attribute information. The second data table is built based on user identification information and serves as a slave table, storing extended identification information. It is linked to the first data table through a customer identifier. When constructing the data tables, different tables for different purposes are built according to the business attribute category and granularity level to which each data object belongs.

[0070] Read the customer identifier associated with each standard data object one by one to identify which customer it belongs to; based on the customer's identifier information, filter out all target data objects associated with that customer from all standard data objects; divide each target data object into user basic information class and user document information class according to business attributes; construct a first data table based on the target data objects of the user basic information class according to customer granularity; construct one or more second data tables based on the target data objects of the user document information class according to document granularity, and each second data table is associated with the first data table through the customer identifier.

[0071] For example, such as Figure 4 The diagram illustrates the master-slave representation. Data objects representing basic customer information are written into one table, resulting in the master table (the first table); data objects representing customer identification information are written into another table, resulting in the slave table (the second table). It should be understood that each identification document corresponds to one slave table.

[0072] Thus, this embodiment of the application avoids the field redundancy and storage waste caused by piling all information into the same data table, and allows document information to be maintained and expanded independently at the document granularity. When a customer holds multiple documents, only a new record needs to be added to the second data table without modifying the table structure of the first data table or repeatedly storing basic information, which reduces data redundancy. At the same time, the association query between the two tables is realized through the customer identifier, ensuring data consistency and ease of use.

[0073] In this embodiment, step S302 may include: Step B10: Perform a technical check on the data records in the data table to obtain a first check result; Step B20: Perform a data standard check on the information items of each data record in the data table to obtain a second check result; Step B30: Perform a business category check on multiple data records in the data table that are associated with preset business rules to obtain a third check result; Step B40: Determine the quality inspection result based on the first inspection result, the second inspection result, and the third inspection result.

[0074] A data record in a data table refers to each row of specific data written into the table, with each row corresponding to complete information about a business entity. Technical checks aim to verify the completeness and accuracy of data records, including checking for duplicate records and ensuring the record count matches the source system, used to identify lost or duplicate data during migration. An information item refers to each field in a data record; for example, in a customer record, the customer number, customer name, and identification number are all independent information items.

[0075] Data standard checks are based on preset data standards to verify whether each information item conforms to encoding rules, code value lists, and regulatory requirements, and are used to identify field-level format and value issues. Preset business rules refer to the logical constraints that multiple data records should satisfy in a specific business scenario, such as the data aggregation relationship between master and slave tables, the balance between details and summaries, and the state dependency relationship between fields. Business-related checks examine the logical relationships between multiple data records that are associated with the above business rules, and are used to identify record-level business logic contradictions. The first check result is the summary judgment output of the technical check, the second check result is the summary judgment output of the data standard check, and the third check result is the summary judgment output of the business-related check. The quality check result is the final judgment output obtained by combining the first, second, and third check results.

[0076] After the data records are written to the data table, the quality inspection process is initiated. Technical checks are performed on each data record in the data table, including record uniqueness verification and record count consistency comparison, outputting the first inspection result. Data standard checks are performed on each information item of each data record in the data table, including encoding rule verification, code value compliance verification, and regulatory requirement verification, outputting the second inspection result. For multiple data records in the data table that are associated with preset business rules, business-related checks are performed according to the association verification rules corresponding to each business scenario, outputting the third inspection result. Combining the first, second, and third inspection results, the quality inspection is considered passed when all inspection items pass, and fails when any inspection item fails.

[0077] In this embodiment, step B10 may include: Step B101: Perform a uniqueness check on the data records written into the data table to determine whether there are duplicate records, and obtain a uniqueness check sub-result.

[0078] Uniqueness checking is an operation that checks for duplicate values ​​in the key identifier fields (such as the primary key field) of each data record in a data table. It determines whether a data record is unique by querying whether two or more records have the same primary key value or a composite unique key value. Duplicate records refer to two or more data rows that have exactly the same primary key field value and whose other fields are identical or conflicting.

[0079] Step B102: Determine the number of first data records in the data table at the current moment and the number of second data records in the data table at a historical moment; Step B103: Compare the number of the first data records and the number of the second data records to obtain a consistency check sub-result; The current time refers to the system time point at which this quality check is performed. The first data record count refers to the total number of records obtained by statistically analyzing the data table at this time. The historical time point refers to a point in time prior to the current time. The second data record count refers to the total number of records recorded or captured in a snapshot of the data table at a historical time point. The time interval between the current time point and the historical time point is a preset duration, which is pre-set within a normal fluctuation range based on the company's historical data. When the difference between the first data record count and the second data record count is within the normal fluctuation range, the fluctuation in the record count is considered normal; otherwise, an error message is output to remind relevant personnel to check and maintain the system.

[0080] Step B104: Determine the first check result based on the uniqueness check sub-result and the consistency check sub-result.

[0081] The uniqueness check sub-result is the output of the uniqueness check, the consistency check sub-result is the output of the consistency comparison, and the first check result is the final technical check result obtained by combining the above two sub-results.

[0082] Read the key identifier field of each data record in the data table, perform grouping and statistics based on this field, and query whether there is an identifier value with a count greater than 1. If it exists, the uniqueness check is deemed to have failed and duplicate record details are recorded. If it does not exist, the uniqueness check is deemed to have passed, and a uniqueness check sub-result is obtained. Obtain the number of the first data record in the data table at the current time and obtain the number of the second data record in the data table at a historical time. Calculate the difference between the number of the first data record and the number of the second data record, and compare the difference with a pre-set normal fluctuation range. If the difference falls within the normal fluctuation range, the consistency check is deemed to have passed. If the difference exceeds the normal fluctuation range, the consistency check is deemed to have failed, and a consistency check sub-result is obtained. Combine the uniqueness check sub-result and the consistency check sub-result. When both pass, the first check result is determined to be passed. When either sub-result fails, the first check result is determined to be failed.

[0083] In this embodiment, step B20 may include: Step B201: Check whether each information item of each data record conforms to the preset encoding rules to obtain the encoding compliance sub-result, wherein the encoding rules include limitations on the encoding length and the characters that make up the encoding.

[0084] A data record is a row of specific data in a data table, with each row corresponding to complete information about a business entity. An information item is each field in a data record; for example, in a customer record, the customer number, customer name, and ID number are all independent information items. Predefined encoding rules are predefined format specifications for specific information items, including limitations on encoding length (such as fixed length or maximum / minimum length) and limitations on the characters that make up the encoding (such as allowing only numbers, only letters, or a combination of letters and numbers).

[0085] Step B202: Check whether the code value of each information item in each data record belongs to the preset code value list to obtain the code value compliance sub-result.

[0086] The preset code value list is a set of legal values ​​pre-defined for a specific information item, used to constrain the value of the information item to be selected from this set.

[0087] Step B203: Check whether each information item of each data record meets the preset data supervision requirements to obtain the supervision compliance sub-result.

[0088] Pre-defined data regulatory requirements are mandatory regulations imposed on data content by external regulatory agencies (such as the State Financial Supervision and Administration Bureau, the People's Bank of China, etc.). These requirements typically involve specific information items that cannot be empty, must conform to a specific format, or must meet specific business meanings.

[0089] Step B204: Based on the coding compliance sub-result, the code value compliance sub-result, and the regulatory compliance sub-result, determine the second inspection result.

[0090] The coding compliance sub-result is the output of the judgment that checks whether the value of each information item conforms to the coding rules; the code value compliance sub-result is the output of the judgment that checks whether the value of each information item belongs to the preset code value list; the regulatory compliance sub-result is the output of the judgment that checks whether each information item meets the regulatory requirements; and the second inspection result is the final judgment of the data standard inspection obtained by combining the above three sub-results.

[0091] Read each information item of each data record in the data table one by one; for information items that need to follow encoding rules, verify whether their values ​​conform to the preset encoding format requirements in terms of length and constituent characters, and obtain the encoding compliance sub-result; for information items that need to follow code value standards, verify whether their values ​​exist in the preset code value list, and obtain the code value compliance sub-result; for information items that need to follow regulatory requirements, verify whether their values ​​meet the preset mandatory specifications, and obtain the regulatory compliance sub-result; combine the above three sub-results, and determine the second inspection result as passed when all sub-results are passed, and determine the second inspection result as failed when any sub-result is failed.

[0092] In this embodiment, step B30 may include: Step B301: Obtain multiple data records in the data table that are associated with preset business rules.

[0093] Preset business rules refer to the logical constraints that multiple data records in a data table should satisfy under specific business scenarios. Multiple data records that are associated with preset business rules refer to several data records that have the above-mentioned logical constraints with each other. These records can be located in the same data table or in different data tables with master-slave or association relationships.

[0094] Step B302: Obtain the corresponding associated verification rules based on the business scenario to which each data record belongs.

[0095] Business scenarios refer to the specific business areas served by the data, such as deposit business, loan business, and credit business. Different business scenarios have different business rules and verification logic. Association verification rules are specific rules that are pre-defined for specific business scenarios and are used to verify whether the logical relationship between multiple data records is valid. For example, verifying whether the sum of the summary value of the master table and the detailed value of the slave table are consistent, or verifying whether the state dependency relationship between various fields is valid.

[0096] Step B303: Verify each data record according to the aforementioned association verification rules to obtain each business verification sub-result; Step B304: Determine the third check result based on the results of each of the aforementioned business verification sub-results.

[0097] The business verification sub-result is a single judgment output obtained after performing verification according to each associated verification rule. The third check result is the final judgment of the business category check obtained by combining the results of each business verification sub-result.

[0098] Identify and retrieve multiple data records in the data table that are associated with preset business rules; determine the business scenario to which each data record belongs, and obtain the corresponding association verification rules according to the business scenario. One business scenario can correspond to one or more association verification rules; verify each data record according to the obtained association verification rules, and determine whether each record meets the corresponding logical constraint relationship to obtain each business verification sub-result; summarize the business verification sub-results, and determine the third check result as passed when all verification sub-results are passed, and determine the third check result as failed when any verification sub-result is failed.

[0099] Thus, this application embodiment ensures the integrity and stability of the data from a technical perspective by comparing the uniqueness of records with the consistency of the number of records within a normal fluctuation range. This is achieved through uniqueness checks and comparisons with the number of records within a normal fluctuation range, ensuring that there are no duplicate records in the master data table and that the data volume changes within a reasonable range. Furthermore, by checking encoding rules, code value compliance, and regulatory requirements, field-level standardization verification is performed on each information item of the data record, ensuring that the master data meets preset standards in terms of format, value, and compliance. Finally, by acquiring multiple data records with business rule associations and verifying them according to the association verification rules corresponding to each business scenario, the business logic relationships within the master data and between master and slave tables are ensured. Therefore, these three types of checks constitute a progressive quality inspection system from the three dimensions of technical integrity, field standardization, and business logic consistency, jointly ensuring that the master data model that passes the checks has a high-quality data foundation.

[0100] Furthermore, it should be understood that master data construction is an ongoing project that requires the establishment of a long-term management mechanism covering the entire lifecycle, including pre-event, during-event, and post-event phases, to ensure that the master data model can continuously meet business needs and maintain a high quality level after it is put into operation.

[0101] In the pre-launch phase, attention is focused on the development and design quality of the master data model. During the requirements review phase, the IT side collects master data construction requirements and works with the data governance team to assess the rationality of including them in the master data construction scope. During the design review phase, the IT side works with the data governance team to review core design elements such as master data standards, data definitions, and quality inspection rules. During the development and testing phase, all development changes to master data are managed in accordance with those of important application systems and must pass testing at the test center before being put into production.

[0102] During the process, operational assurance is ensured through a comprehensive quality monitoring system and anomaly handling mechanism. Based on the established data quality inspection system, master data quality is continuously monitored from three dimensions: technical inspection, data standard inspection, and business inspection. Simultaneously, data timeliness is managed according to service level management standards to ensure that master data meets the timeliness requirements of downstream applications. When quality or timeliness anomalies occur, the problem is addressed and resolved as quickly as possible through either an in-process handling mechanism (interrupting the process and waiting for processing) or a post-processing mechanism (continuing operation or post-repair).

[0103] In the post-event phase, continuous monitoring, review, and improvement are conducted. Periodic analysis of downstream usage of the master data is performed, including master-slave table relationships and the frequency of each information item's reference in downstream environments. This information is used to assess the need for adding or removing master data information items. The system automatically reports on master data quality and anomaly handling on a regular basis. Detected issues are reviewed, root causes are analyzed, and improvement measures are implemented. Simultaneously, after the master data model is built, the IT side continuously collects master data-related requirements, which are then jointly evaluated by the governance and business teams, forming a closed-loop iterative mechanism.

[0104] This application also provides a master data modeling apparatus, please refer to... Figure 5 The master data modeling device includes: Data acquisition module 10 is used to acquire data objects with the same granularity as the master data from the data warehouse based on the preset data standard of the master data; Standardization module 20 is used to standardize each of the data objects to obtain standard data objects; The construction module 30 is used to perform master data quality checks on each of the standard data objects, and when the master data quality checks pass, the data table constructed based on each of the standard data objects is used as the master data model.

[0105] Optionally, the standardization module 20 is further configured to: Determine the business entity, data source, granularity, and analysis dimension of each data object; Normalize data objects that have the same business entity but different data sources to obtain the first data object; Data objects with different business entities but the same granularity are merged to obtain a second data object; Data objects with the same analytical dimensions but different business entities are associated to obtain associated third data objects. The first data object, the second data object, and each of the associated third data objects are used as standard data objects.

[0106] Optionally, the building module 30 is further configured to: Based on the aforementioned standard data objects, construct a data table; Perform a master data quality check on the data records in the data table to obtain the quality check results.

[0107] Optionally, the data table includes a first data table and a second data table, and the construction module 30 is further configured to: For each customer, identify the target data object associated with that customer from among the aforementioned standard data objects; Based on the standard data objects belonging to user basic information in each of the target data objects, the first data table is constructed according to the customer granularity. Based on the standard data objects belonging to user identification information in each of the target data objects, at least one second data table is constructed according to the identification granularity.

[0108] Optionally, the building module 30 is further configured to: A technical check is performed on the data records in the data table to obtain the first check result; A data standard check is performed on the information items of each data record in the data table to obtain a second check result; A third check result is obtained by performing a business-class check on multiple data records in the data table that are associated with preset business rules. The quality inspection result is determined based on the first inspection result, the second inspection result, and the third inspection result.

[0109] Optionally, the building module 30 is further configured to: Perform a uniqueness check on the data records in the data table to determine whether there are duplicate records, and obtain a uniqueness check sub-result; Determine the number of first data records in the data table at the current moment, and the number of second data records in the data table at a historical moment; The consistency check sub-result is obtained by comparing the number of the first data records with the number of the second data records. Based on the uniqueness check sub-result and the consistency check sub-result, the first check result is determined.

[0110] Optionally, the building module 30 is further configured to: Check whether each information item of each data record conforms to the preset encoding rules to obtain the encoding compliance sub-result, wherein the encoding rules include limitations on the encoding length and the characters that make up the encoding; Check whether the code value of each information item in each data record belongs to the preset code value list to obtain the code value compliance sub-result; Check whether each information item of each data record meets the preset data supervision requirements to obtain the supervision compliance sub-result; The second inspection result is determined based on the coding compliance sub-result, the code value compliance sub-result, and the regulatory compliance sub-result.

[0111] Optionally, the building module 30 is further configured to: Retrieve multiple data records in the data table that are associated with preset business rules; Based on the business scenario to which each of the data records belongs, obtain the corresponding associated verification rules; Each data record is verified according to the aforementioned association verification rules to obtain each business verification sub-result; Based on the results of each of the aforementioned business verification sub-results, the third inspection result is determined.

[0112] The master data modeling apparatus provided in this application, employing the master data modeling method described in the above embodiments, can solve the technical problem of how to improve the adaptability of the master data model to actual business requirements, thereby enhancing master data management efficiency. Compared with the prior art, the beneficial effects of the master data modeling apparatus provided in this application are the same as those of the master data modeling method described in the above embodiments, and other technical features in the master data modeling apparatus are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0113] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the master data modeling method in Embodiment 1 above.

[0114] The following is for reference. Figure 6 It shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of this application. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0115] like Figure 6As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although the diagrams show electronic devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0116] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0117] The electronic device provided in this application, employing the master data modeling method described in the above embodiments, can solve the technical problem of how to improve the adaptability of the master data model to actual business requirements, thereby enhancing master data management efficiency. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the master data modeling method provided in the above embodiments, and other technical features of this electronic device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0118] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0119] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0120] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the master data modeling method in the above embodiments.

[0121] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0122] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0123] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by an electronic device, the electronic device causes the electronic device to: obtain data objects with the same granularity as the master data from a data warehouse based on a preset data standard for the master data; perform standardization processing on each of the data objects to obtain standard data objects; perform master data quality checks on each of the standard data objects, and when the master data quality checks pass, use the data table constructed based on each of the standard data objects as the master data model.

[0124] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0126] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0127] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described master data modeling method. This addresses the technical problem of how to improve the adaptability of the master data model to actual business requirements, thereby enhancing master data management efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the master data modeling method provided in the above embodiments, and will not be elaborated upon here.

[0128] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the master data modeling method described above.

[0129] The computer program product provided in this application can improve the adaptability of the master data model to actual business requirements, thereby improving the efficiency of master data management. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the master data modeling method provided in the above embodiments, and will not be repeated here.

[0130] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A master data modeling method, characterized in that, The master data modeling method includes: Based on the preset data standard of the master data, data objects with the same granularity as the master data are obtained from the data warehouse; The data objects are standardized to obtain standard data objects. A master data quality check is performed on each of the aforementioned standard data objects, and when the master data quality check passes, the data table constructed based on each of the aforementioned standard data objects is used as the master data model.

2. The master data modeling method as described in claim 1, characterized in that, The step of standardizing each of the data objects to obtain standard data objects includes: Determine the business entity, data source, granularity, and analysis dimension of each data object; Normalize data objects that have the same business entity but different data sources to obtain the first data object; Data objects with different business entities but the same granularity are merged to obtain a second data object; Data objects with the same analytical dimensions but different business entities are associated to obtain associated third data objects. The first data object, the second data object, and each of the associated third data objects are used as standard data objects.

3. The master data modeling method as described in claim 1, characterized in that, The step of performing master data quality checks on each of the aforementioned standard data objects includes: Based on the aforementioned standard data objects, construct a data table; Perform a master data quality check on the data records in the data table to obtain the quality check results.

4. The master data modeling method as described in claim 3, characterized in that, The data table includes a first data table and a second data table. The step of constructing the data table based on each of the standard data objects includes: For each customer, identify the target data object associated with that customer from among the aforementioned standard data objects; Based on the standard data objects belonging to user basic information in each of the target data objects, the first data table is constructed according to the customer granularity. Based on the standard data objects belonging to user identification information in each of the target data objects, at least one second data table is constructed according to the identification granularity.

5. The master data modeling method as described in claim 3, characterized in that, The step of performing master data quality checks on the data records in the data table and obtaining the quality check results includes: A technical check is performed on the data records in the data table to obtain the first check result; A data standard check is performed on the information items of each data record in the data table to obtain a second check result; A third check result is obtained by performing a business-class check on multiple data records in the data table that are associated with preset business rules. The quality inspection result is determined based on the first inspection result, the second inspection result, and the third inspection result.

6. The master data modeling method as described in claim 5, characterized in that, The step of performing a technical inspection on the data records in the data table to obtain a first inspection result includes: Perform a uniqueness check on the data records in the data table to determine whether there are duplicate records, and obtain a uniqueness check sub-result; Determine the number of first data records in the data table at the current moment, and the number of second data records in the data table at a historical moment; The consistency check sub-result is obtained by comparing the number of the first data records with the number of the second data records. Based on the uniqueness check sub-result and the consistency check sub-result, the first check result is determined.

7. The master data modeling method as described in claim 5, characterized in that, The step of performing a data standard check on the information items of each data record in the data table to obtain a second check result includes: Check whether each information item of each data record conforms to the preset encoding rules to obtain the encoding compliance sub-result, wherein the encoding rules include limitations on the encoding length and the characters that make up the encoding; Check whether the code value of each information item in each data record belongs to the preset code value list to obtain the code value compliance sub-result; Check whether each information item of each data record meets the preset data supervision requirements to obtain the supervision compliance sub-result; The second inspection result is determined based on the coding compliance sub-result, the code value compliance sub-result, and the regulatory compliance sub-result.

8. The master data modeling method as described in claim 5, characterized in that, The step of performing a business-type check on multiple data records in the data table that are associated with preset business rules to obtain a third check result includes: Retrieve multiple data records in the data table that are associated with preset business rules; Based on the business scenario to which each of the data records belongs, obtain the corresponding associated verification rules; Each data record is verified according to the aforementioned association verification rules to obtain each business verification sub-result; Based on the results of each of the aforementioned business verification sub-results, the third inspection result is determined.

9. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the master data modeling method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the master data modeling method as described in any one of claims 1 to 8.

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