A data standard whole-process landing application method and device

Abstract

The application discloses a data standard whole-process landing application method and device, the method comprises the following steps: S1, combing and formulating a data standard with unified content format according to a data standard framework; S2, serving the data standard, facilitating application integration into a business system, and realizing data standard penetration; S3, converting the data standard into a data standard quality library, monitoring standard landing effect of business data through data quality management, and promoting improvement and perfection of the business system. The device comprises a data standard management module, a data standard model management module, a data service management module and a data quality management module. The application can unify and perfect a data standard formulation framework, better meet data standard whole-process application constraint requirements, better realize data standard informatization, guarantee data consistency, and better detect data standard landing effect.

Description

Technical Field

[0001] This invention belongs to the field of data standard management technology, and in particular relates to a method and apparatus for the full-process implementation of data standards. Background Technology

[0002] A data standard is a specification of the rules governing the meaning and attributes of a particular business within an organization, ensuring that data is understood, used, and exchanged consistently and accurately within the organization.

[0003] Data standards management is a series of activities that regulate the formulation and implementation of data standards.

[0004] The implementation and application of data standards are mainly divided into three stages: pre-standard formulation, in-process application of standards to business, and post-standard verification of business data.

[0005] Traditionally, data standards are easy to formulate but difficult to implement. After being formulated and published, data standard documents typically only provide "query and guidance." However, with current research and technological advancements, establishing a data standard framework beforehand unifies the specifications for business data standard formulation. Building a data standard dictionary guides database field design, and utilizing natural language processing technology to quickly analyze the similarities between existing database fields and the data standard dictionary to establish mappings effectively standardizes data standard formulation and business system model design. Furthermore, by binding data standards to business attribute fields, the quality inspection function of the data platform enables data standards to monitor the quality of business data, promoting the application and improvement of data standards in post-processing data quality.

[0006] Currently, during the system design phase, using data standards to guide and standardize business data architecture can improve the compliance of various systems with data standards and ensure data consistency between systems to a certain extent. However, ensuring that business systems generate data correctly within the data standard value range in real time during business operations still faces challenges. Furthermore, data quality detection based on data standards that deviates from the standard is a passive, post-hoc remedial method, because if data has already been generated incorrectly, it may affect data analysis.

[0007] Updating and maintaining data standards involves not only updating the standard documents themselves, but also ensuring that data in relevant business systems is generated according to the new standards and that historical data is updated.

[0008] The ultimate goal of data standards is to achieve data consistency across various business systems. How to proactively improve the implementation of data standards, ensure that business data conforms to the standards during the data generation process, and achieve data connectivity; and how to accurately identify the scope of application of data standards and automatically update them into the system during the iterative update process of data standards, ensuring that data standards are always automatically generated according to the latest standards, are technical problems that urgently need to be solved by those skilled in the art. Summary of the Invention

[0009] The purpose of this invention is to solve the above-mentioned problems in the prior art and provide a method and apparatus for the full-process implementation of data standards. By unifying and improving the data standard formulation framework, the informatization and service-oriented nature of data standards, and the detection of the implementation effect of data standards, the invention can better realize the implementation of data standards and ensure data consistency.

[0010] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for the full-process implementation and application of data standards, characterized by the following steps: S1. Establish a unified data standard framework with consistent content format to standardize the development of data standards for various business operations within the enterprise; S2. Standardize data into services, forming data standard service interfaces to facilitate the integration of business systems and achieve data standardization. S3. Transform data standards into data quality rules, monitor business data through quality management, promptly identify business data that does not meet the standards, and promote the improvement and perfection of business systems.

[0011] Furthermore, the detailed steps of step S1 are as follows: S11. Based on business development needs, determine enterprise data standard template files with unified content formats from business, technology, management, quality and security related dimensions, form a unified framework for enterprise data standards, and guide the formulation of enterprise data standards; S12. Each business unit shall formulate its own data standards based on the data standard template, and enter the relevant attributes that are 1:1 related to the business standards into the metadata model of the data standards through the data standard project initiation form. S13. The relevant attributes related to the 1:N relationship of the standard document, such as reference data and quality attributes in the business standard, shall be uniformly entered into the data standard reference data model and quality rule model through the relevant data standard forms. S14. Initiate the online review of data standards. After the data standard administrator approves the data standard, publish the data standard and put it on the data standard list so that various business personnel can search, view, download and use it.

[0012] Furthermore, the detailed steps of step S2 are as follows: S21. After the business data standard is approved, the metadata model and reference data model in the data standard are developed, configured, and released into a data standard service through the data standard service module. S22. Based on the needs of different business scenarios, the metadata model and reference data model of the data standard are transformed into a data dictionary for the business system, and then pushed to the data dictionary of different business systems through data service distribution.

[0013] Furthermore, the detailed steps of step S3 are as follows: S31. Through the data quality management module, the reference data model and quality rule model in the data standard are converted into the data quality rule library; S32. Map and bind the databases, tables and fields collected from the business system into the lake with the quality rules of the data standard to form a data standard quality monitoring scheme. S33. Through the data quality inspection function, the data standard quality monitoring plan is executed on a daily schedule to check the application effect of data standards failure in business systems. S34. Based on the inspection results and the production data standard quality monitoring report, any abnormal data found should be used to promote the correction, improvement and perfection of the business system.

[0014] This invention also discloses a data standard end-to-end application device, characterized in that it includes: The data standard management module designs a data standard model based on the standardized data standard template. It is used to input the contents of various business data standard files into the system for storage and management, and supports the management of data standard creation, review, and release processes. The data standard service management module is used to provide functions for creating data standard services, publishing data standard services, transforming data standard models, and distributing standard data. The data quality management module is used to provide functions such as converting technical and business constraint attributes in data standards into quality rules, mapping and binding data quality rules with business data, quality monitoring and inspection, and data quality reporting, so as to monitor the mapping effect of data standards.

[0015] Furthermore, the data standard model mainly includes the standard metadata model, the standard reference data model, the standard quality rule model, the standard de-identification rule model, the data standard file model, and the data standard application model.

[0016] Furthermore, the data standards management module includes: Data standard creation: Create relevant forms through standards, and upload the content of relevant data standard files to the data standard meta-model, reference data model, standard quality rule model, standard de-identification rule model, and standard file model through the forms.

[0017] Data standard review and release: Provides management functions related to data standard review, release, and obsolescence.

[0018] Data standard query provides functions such as data standard query and download.

[0019] Furthermore, the data standard service management module includes: Data standard creation: By developing, configuring, and publishing data standard metadata and standard data in reference data models, data standard services are formed to facilitate the integration of business systems.

[0020] Data Standards Service: Provides data standard query capabilities through service interfaces, facilitating integration with business systems.

[0021] Data standard format conversion: Different business scenarios have different requirements for metadata and reference data in standards, and in certain scenarios, data standard services may not be able to meet the needs. By converting standard metadata and reference data, a data dictionary format corresponding to the business system can be formed.

[0022] Standard distribution and push service: By proactively distributing data standard dictionaries to the data dictionary tables of corresponding systems through data distribution and push, the service meets the needs of data standard implementation and application in special scenarios.

[0023] Standard service management: Provides functions related to data standard service authorization, push configuration management, etc.

[0024] Furthermore, the data quality management module includes: Data Quality Rule Base: The data in the quality rule model and reference data model of the data standard are transformed into a part of the data quality rule base to form the data standard quality rule base; Data quality rule mapping: Binding data standard quality rules to data fields in business systems to form a data standard quality monitoring solution; Data quality inspection: Daily scheduled scheduling of data standard quality monitoring plan to monitor the quality of business data; Data quality result query: It provides the ability to query abnormal data found by standard quality monitoring, which facilitates business to improve data standards.

[0025] The present invention, by adopting the above-described technical solution, has the following beneficial effects: 1. By taking a holistic approach during the standard-setting phase and adding quality, security, and application-related attributes to the data standard framework, the data standard-setting framework becomes more comprehensive and better meets the application constraints of the entire data standard process.

[0026] 2. By identifying and classifying the content in the data standards to form a data standard storage model, the informatization of data standards is better realized, rather than simply uploading data standard files. At the same time, the data standards are transformed into data standard services and data standard distribution services, which can better integrate the application of data standards into business systems, meet the needs of different scenarios, and ensure data consistency from the source of data generation.

[0027] 3. Convert the quality-related attributes in the data standard into data standard quality rules and apply them to the monitoring of business system libraries, tables, and fields. This will help identify shortcomings in the implementation of data standards and improve their effectiveness. Attached Figure Description

[0028] The present invention will be further described below with reference to the accompanying drawings: Figure 1 This is a schematic diagram illustrating a method for the full-process implementation of data standards according to the present invention; Figure 2 This is a schematic diagram of the unified data standard framework of the present invention; Figure 3 This is a flowchart illustrating a data standard full-process implementation application device according to the present invention;

[0029] Figure 4 This is a schematic diagram of the functional architecture for the full-process implementation of a data standard according to the present invention. Detailed Implementation

[0030] like Figures 1 to 3 As shown, this invention provides a method for the full-process implementation of data standards, comprising the following steps: S1. Beforehand: Develop and standardize a unified data standard framework for content formatting, and standardize the development of data standards for various business operations within the enterprise.

[0031] S2. In-process: Standardize data into services, form data standard service interfaces, facilitate the integration of business systems, and achieve data standardization.

[0032] S3. Post-event: Transform data standards into data quality rules, monitor business data through quality management, promptly identify business data that does not meet the standards, and promote the improvement and perfection of business systems.

[0033] This invention enables the standardization and unification of all data content and formats within an enterprise, and integrates them with the asset catalog of the business system's data architecture, facilitating the standardization of the business system's data architecture design from the source; it can convert data standards into data standard services, facilitating the integration and application of business systems, and better ensuring that data standards are met during the data generation process; it can transform quality attributes in data standards into quality rules, which can be applied to business data quality monitoring, checking the effectiveness of data standard application, and better promoting the improvement and perfection of business data.

[0034] The unified data standards framework includes: 1. Business-related attributes: Standard name code, Chinese name of standard, English name of standard, main domain of standard, business meaning of standard, reference standard document, standard version number, standard level (standard level: national standard, industry standard, superior standard, enterprise-defined standard).

[0035] 2. Technical attributes: data type, data length, data precision, and unit of measurement.

[0036] 3. Reference data attributes: reference data encoding, reference data value, reference data parent node encoding, and reference data parent node value.

[0037] 4. Standard Management Attributes: Standard management attribute code, management department, standard owner, standard daily administrator, creator, reviser, release date, revision date, standard source system, standard source physical table, management type (centralized, agency).

[0038] 5. Standard quality attributes: standard quality rule code, whether it is unique, whether it can be nullable, value range, value domain type, and reference data code.

[0039] 6. Standard security attributes: security level (confidential, important, general), desensitization rules.

[0040] 7. Application-related attributes: standard code, standard Chinese name, business system of the standard application, physical table corresponding to the referenced business system, corresponding field of the physical table of the referenced business system, and system owner.

[0041] Standard setting principles: 1. National standards > Industry standards > Standards of higher authorities > Enterprise standards.

[0042] 2. All data standards must adhere to a unified standard framework, meet the standard content format, and follow the process of sorting, formulating, reviewing, and publishing.

[0043] 3. Data standard coding and value content design must meet future expansion requirements. At the same time, standard updates must follow the principle of upward compatibility, and old codes and values ​​must be deleted using logical deletion methods.

[0044] 4. Unified standard authoritative source: National and industry standards are uniformly sourced from corresponding authoritative websites, while enterprise standards are sourced from the competent authority's system.

[0045] The detailed steps of step S1 are as follows: S11. Based on business development needs, the data management department or business department, following the unified data standard framework and standard review process, should develop a standardized enterprise data document with consistent content and format, and review and approve it through offline discussions with upstream and downstream departments. The standard document should include standard business, technical, management, quality, and security-related attributes.

[0046] S12. Create a form using the data standard to record and save the business and technology-related attributes of the standard documents in the data standard metamodel, based on the 1:1 relationship between the standard documents.

[0047] S13. Enter the relevant attributes related to the 1:N relationship of the standard document into the data standard management system through the relevant forms in the data standard management.

[0048] (1) Save quality-related attribute records into the data standard quality model through the standard quality rule form.

[0049] (2) Save the reference data attribute records into the standard reference data model through the standard reference data form.

[0050] (3) Save security-related attribute records into the standard data anonymization model using standard security forms.

[0051] (4) Upload the data standard file to the standard file model through the standard file management.

[0052] S14. After the offline data standard is approved, the data standard is published online and put on the data standard list so that various business personnel can search, view, download and use it.

[0053] The detailed steps of step S2 are as follows: S21. Through the data service configuration in the data service management module, the reference data model and standard metamodel in the data standard are formed into a data standard service and published. This facilitates business systems to call the interface to query and use the data standard reference data and metadata.

[0054] S22. Based on the needs of different business scenarios, the metadata model and reference data model in the data standard are transformed into a model format to form the storage format of the business system dictionary. Through data service distribution, the data standard reference data is distributed to the data dictionary of different business systems to meet the requirements of different scenarios.

[0055] The detailed steps of step S3 are as follows: S31. Through the data quality management module, the reference data model and quality rule model in the data standard are converted into the data quality rule library.

[0056] S32. Map and bind the databases, tables, and fields collected from the business system into the lake with the quality rules of the data standard to form a data standard quality monitoring scheme.

[0057] S33. Through the data quality inspection function, the data standard quality monitoring plan is executed on a daily basis to check the application effect of data standards in business systems.

[0058] S34. Based on the inspection results and the production data standard quality monitoring report, any abnormal data found should be used to promote the correction, improvement and perfection of the business system.

[0059] like Figure 4 As shown, the present invention also discloses a data standard end-to-end application device, comprising: The data standard management module designs a data standard model based on the standardized data standard template. It is used to input the contents of various business data standard files into the system for storage and management, and also supports the management of the creation, review and release process of data standards.

[0060] The data standard service management module is used to provide functions for creating data standard services, publishing data standard services, converting data standard models, and distributing standard data.

[0061] The data quality management module is used to provide functions such as converting technical and business constraint attributes in data standards into quality rules, mapping and binding data quality rules with business data, quality monitoring and inspection, and data quality reporting, so as to monitor the mapping effect of data standards.

[0062] As an implementation method, the data standard model mainly includes the standard metadata model, standard reference data model, standard quality rule model, standard de-identification rule model, data standard file model, and data standard application model.

[0063] For example, the DDL statement for creating a physical table in a standard metadata model is: CREATE TABLE `data_standards_meta` ( `id` int, `gmt_create` date comment 'creation time', `gmt_modify` date comment 'modification time', `ds_name` varchar(100) comment 'Data Standard Name', `ds_code` varchar(100) comment 'Data Standard Encoding', `ds_name_eng` varchar(100) comment 'Data Standard English Name', `ds_topic ` varchar(100) comment 'Topic domain name', `ds_desc` varchar(100) comment 'Standard name description: business meaning', `ds_level` varchar(100) comment 'Standard Level', `ds_file_name` varchar(100) comment 'Standard filename', `ds_file_version` varchar(100) comment 'Standard file version number', `tech_data_type` varchar(100) comment 'Data type', `tech_data_length` varchar(100) comment 'data length', `tech_data_accuracy` varchar(100) comment 'data accuracy', `tech_data_measure` varchar(100) comment 'Unit of measurement', `ds_owner` varchar(100) comment 'Data Standard Owner', `ds_manager` varchar(100) comment 'Data Standards Administrator', `ds_creater` varchar(100) comment 'Data Standard Uploader', `ds_updater` varchar(100) comment 'Data Standard Updater', `ds_belong_dept` varchar(100) comment 'Department to which data standard belongs', `ds_release_date` varchar(100) comment 'Data Standard Release Date', `ds_update_date` varchar(100) comment 'Data standard revision date', `ds_manage_mode` varchar(100) comment 'Data Standard Management Mode', `ds_source_system` varchar(100) comment 'Data Standard Source System', `ds_source_table` varchar(100) comment 'Data Standard Source Table', `ds_base_data_code` varchar(100) comment 'Standard Reference Data Encoding', `ds_secure_level` varchar(100) comment 'Security level', `ds_secure_desensitize_code` varchar(100) comment 'Reference desensitization rule encoding', `ext_info` varchar(100) ) For example, the DDL statement for creating a physical table in the standard reference data model is: CREATE TABLE `dim_base_data` ( `id` int, `gmt_create` date comment 'creation time', `gmt_modify` date comment 'modification time', `data_base_code` date comment 'Data Standard Reference Data Identifier', `data_base_name` date comment 'Data Standard Reference Data Identifier', `data_code` varchar(100) comment 'Reference data enumeration value encoding', `data_value` varchar(100) comment 'Reference number enumeration value', `parent_code` varchar(100) comment 'Reference data upper-level enumeration value encoding', `parent_value` varchar(100) comment 'Reference data upper-level enumeration value', `versions` varchar(100) comment 'version number', `delete_flag` varchar(100) comment 'Logical deletion flag', `ext_info` varchar(100) ) As one implementation method, the data standards management module includes: The data standard file provides operation forms related to the data standard file, allowing users to upload and store relevant data standard files into the data standard file model.

[0064] Standard reference data management: Provides unified management of enumeration values ​​in business attributes within data standard files, enabling the storage of enumeration values ​​into the reference data model through unified encoding.

[0065] Data masking rule management: Provides unified management of data masking rules in security-related attributes of data standard files, and realizes unified storage of data masking rule attributes into the data masking model.

[0066] Quality rule model: Provides unified management of business constraint rules in quality and technical attributes in data standard files, and realizes the unified storage of business constraint rules in the quality rule model.

[0067] Data Standard Creation and Management: Provides a data standard creation form, which uniformly stores the metadata of all 1:1 relationships in the data standard file into the data standard meta-model. The attributes of 1:N relationships are entered into the system by referencing the corresponding codes in the pre-created reference data model, desensitization rule model, and quality rule model.

[0068] Data Standard Review and Release Management: Provides process management for data standard review, release, update, and obsolescence.

[0069] As one implementation method, the data service management module includes: Data Standard Service: Based on the data standard metadata model and reference data model, we develop, configure, and release data standard services to facilitate business systems to integrate data standards through interfaces.

[0070] Standard data distribution service: By transforming the data standard meta-model and reference data model, a data dictionary format corresponding to the business system is formed, and the data standard dictionary is distributed to the data dictionary table of the corresponding system through proactive push to meet the requirements of different business scenarios.

[0071] As one implementation method, the data quality management module includes: Data Quality Rule Base: The data in the quality rule model and reference data model of the data standard are transformed into a part of the data quality rule base to form the data standard quality rule base; Data quality rule mapping: Binding data standard quality rules to data fields in business systems to form a data standard quality monitoring solution; Data quality inspection: Daily scheduled scheduling of data standard quality monitoring plan to monitor the quality of business data; Data Quality Report: This report identifies anomalous data discovered through standard quality monitoring and inspection, pinpointing systems, tables, fields, and records within the business that do not meet data standards. It also drives corrective improvements to the business systems. Furthermore, it enhances the ability to query anomalous data discovered through standard quality monitoring, facilitating data standard compliance improvements within the business.

[0072] The present invention, by adopting the above-described technical solution, has the following beneficial effects: 1. By taking a holistic approach during the standard-setting phase and adding quality, security, and application-related attributes to the data standard framework, the data standard-setting framework becomes more comprehensive and better meets the application constraints of the entire data standard process.

[0073] 2. By identifying and classifying the content in the data standards to form a data standard storage model, the informatization of data standards is better realized, rather than simply uploading data standard files. At the same time, the data standards are transformed into data standard services and data standard distribution services, which can better integrate the application of data standards into business systems, meet the needs of different scenarios, and ensure data consistency from the source of data generation.

[0074] 3. Transform the quality-related attributes and reference data models in the data standards into data standard quality rules, and apply them to the monitoring of business system libraries, tables, and fields. This will help identify shortcomings in the implementation of data standards and improve their effectiveness.

[0075] The above are merely specific embodiments of the present invention, but the technical features of the present invention are not limited thereto. Any simple changes, equivalent substitutions, or modifications made based on the present invention to solve essentially the same technical problems and achieve essentially the same technical effects are all covered within the protection scope of the present invention.

Claims

1. A method for the full-process implementation and application of data standards, characterized in that, Includes the following steps: S1. Establish a unified data standard framework with consistent content format to standardize the development of data standards for various business operations within the enterprise; S2. Standardize data into services, forming data standard service interfaces to facilitate the integration of business systems and achieve data standardization. S3. Transform data standards into data quality rules, monitor business data through quality management, promptly identify business data that does not meet the standards, and promote the improvement and perfection of business systems.

2. The method for the full-process implementation and application of data standards according to claim 1, characterized in that: The detailed steps of step S1 are as follows: S11. Based on business development needs, determine enterprise data standard template files with unified content formats from business, technology, management, quality and security related dimensions, form a unified framework for enterprise data standards, and guide the formulation of enterprise data standards; S12. Each business unit shall formulate its own data standards based on the data standard template, and enter the relevant attributes that are 1:1 related to the business standards into the metadata model of the data standards through the data standard project initiation form. S13. The relevant attributes related to the 1:N relationship of the standard document, such as reference data and quality attributes in the business standard, shall be uniformly entered into the data standard reference data model and quality rule model through the relevant data standard forms. S14. Initiate the online review of data standards. After the data standard administrator approves the data standard, publish the data standard and put it on the data standard list so that various business personnel can search, view, download and use it.

3. The method for the full-process implementation and application of data standards according to claim 1, characterized in that: The detailed steps of step S2 are as follows: S21. After the business data standard is approved, the metadata model and reference data model in the data standard are developed, configured, and released into a data standard service through the data standard service module. S22. Based on the needs of different business scenarios, the metadata model and reference data model of the data standard are transformed into a data dictionary for the business system, and then pushed to the data dictionary of different business systems through data service distribution.

4. The method for the full-process implementation and application of data standards according to claim 1, characterized in that: The detailed steps of step S3 are as follows: S31. Through the data quality management module, the reference data model and quality rule model in the data standard are converted into the data quality rule library; S32. Map and bind the databases, tables and fields collected from the business system into the lake with the quality rules of the data standard to form a data standard quality monitoring scheme. S33. Through the data quality inspection function, the data standard quality monitoring plan is executed on a daily schedule to check the application effect of data standards failure in business systems. S34. Based on the inspection results and the production data standard quality monitoring report, any abnormal data found should be used to promote the correction, improvement and perfection of the business system.

5. A data standard end-to-end application device, characterized in that: include, The data standard management module designs a data standard model based on the standardized data standard template. It is used to input the contents of various business data standard files into the system for storage and management, and supports the management of data standard creation, review, and release processes. The data standard service management module is used to provide functions for creating data standard services, publishing data standard services, transforming data standard models, and distributing standard data. The data quality management module is used to provide functions such as converting technical and business constraint attributes in data standards into quality rules, mapping and binding data quality rules with business data, quality monitoring and inspection, and data quality reporting, so as to monitor the mapping effect of data standards.

6. The data standard end-to-end application device according to claim 5, characterized in that: The data standard model mainly includes the standard metadata model, the standard reference data model, the standard quality rule model, the standard de-identification rule model, the data standard file model, and the data standard application model.

7. The data standard end-to-end application device according to claim 5, characterized in that: The data standard management module includes: Data standard creation involves creating relevant forms based on the standards, and uploading and storing the content of the relevant data standard files into the data standard meta-model, reference data model, standard quality rule model, standard de-identification rule model, and standard file model. Data standard review and release, providing management functions related to data standard review, release, and obsolescence; Data standard query provides functions such as data standard query and download.

8. The data standard end-to-end application device according to claim 5, characterized in that: The data standard service management module includes: Data standard creation: By developing, configuring, and publishing data standard metadata and standard data in reference data models, data standard services are formed to facilitate the integration of business systems; Data Standards Service: Provides data standard query capabilities through service interfaces, facilitating integration with business systems; Data standard format conversion: By converting standard metadata and reference data, a data dictionary format corresponding to the business system is formed; Standard distribution and push service: Actively distributes the data standard dictionary to the data dictionary tables of the corresponding system through data distribution and push, so as to meet the needs of data standard implementation and application in special scenarios; Standard service management: Provides functions such as data standard service authorization and push configuration management.

9. The data standard end-to-end application device according to claim 5, characterized in that: The data quality management module includes: Data Quality Rule Base: The data in the quality rule model and reference data model of the data standard are transformed into a part of the data quality rule base to form the data standard quality rule base; Data quality rule mapping: Binding data standard quality rules to the corresponding data fields of the business system to form a data standard quality monitoring solution; Data quality inspection: Daily scheduled scheduling of data standard quality monitoring plan to monitor the quality of business data; Data Quality Report: By identifying abnormal data through standard quality monitoring and inspection, the report identifies systems, tables, fields, and records in the business that do not meet data standards, and promotes the correction and improvement of business systems.

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