Universal Audit Data Converter (UADC)<\/h1>\n\nTable of Contents<\/div>\n\n- 1. Overview<\/a><\/li>\n
- 2. Developments in Audit Data Standards<\/a>\n
\n- 2.1. Importance of Audit Data Standardization<\/a><\/li>\n
- 2.2. AICPA’s Audit Data Standard<\/a><\/li>\n
- 2.3. China’s GB\/T 24589<\/a><\/li>\n
- 2.4. ISO\/TC 295 Audit data services<\/a><\/li>\n<\/ul>\n<\/li>\n
- 3. Role of UADC<\/a>\n
\n- 3.1. Application to ESG Report Auditing<\/a><\/li>\n<\/ul>\n<\/li>\n
- 4. Improvements in Auditing with UADC<\/a>\n
\n- 4.1. Efficiency and Speed<\/a><\/li>\n
- 4.2. Consistency and Quality Improvement<\/a><\/li>\n
- 4.3. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 5. Generic Data Conversion Processing<\/a><\/li>\n
- 6. Syntax Binding<\/a>\n
\n- 6.1. Japanese Core Invoice Gateway<\/a><\/li>\n
- 6.2. XBRL Taxonomy<\/a><\/li>\n
- 6.3. Adapting to System Changes<\/a><\/li>\n<\/ul>\n<\/li>\n
- 7. Semantic Binding<\/a>\n
\n- 7.1. Its Role<\/a><\/li>\n
- 7.2. Flexibility and Adaptability<\/a><\/li>\n
- 7.3. Contributing to Efficiency and Speed<\/a><\/li>\n
- 7.4. Wickham’s Tidy Data Concept<\/a><\/li>\n
- 7.5. xBRL-CSV and Hierarchical Tidy Data<\/a><\/li>\n
- 7.6. In the Case of Journal Entry Information<\/a><\/li>\n<\/ul>\n<\/li>\n
- 8. Advantages of Adopting xBRL-CSV<\/a>\n
\n- 8.1. Reduction in JOIN Operations<\/a><\/li>\n
- 8.2. No Need for Database Maintenance<\/a><\/li>\n
- 8.3. Simplification and Speeding Up of Program Processing<\/a><\/li>\n
- 8.4. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 9. Liberation from Vendor Dependence<\/a>\n
\n- 9.1. Effects<\/a><\/li>\n
- 9.2. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 10. Long-Term Data Storage Issues<\/a>\n
\n- 10.1. Effects<\/a><\/li>\n
- 10.2. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 11. Accounting Data Standard Interface<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n\n\n\n\n
Presentation notes are available at:
\nhttps:\/\/www.sambuichi.jp\/wu_wei\/index.html?note=\/2023\/11\/dfda6aee-7c62-4e7a-88fc-055c8e2283fc<\/a><\/p>\n<\/div>\n\nNobuyuki SAMBUICHI
\nISO\/TC295 Audit data services<\/a>
\nConvener at SG1 Semantic model
\nCo-project leader at ISO\/AWI 21926 Semantic data model for audit data services<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n1. Overview<\/h2>\n\n\nStandardization of audit data is essential for a transparent and efficient audit process. Significant developments in this field include the AICPA’s “Audit Data Standard” and China’s GB\/T 24589. These standards aim to enhance the quality and consistency of audit data. The AICPA’s Audit Data Standard[1<\/a>]<\/sup> provides standards for data auditing, and China’s GB\/T 24589 mandates data collection from all domestic accounting software and analysis by regulatory agencies. ISO\/TC 295 Audit data services[2<\/a>]<\/sup> established ISO 21378:2019 Audit data collection[3<\/a>]<\/sup>.<\/p>\n<\/div>\n\nThe Universal Audit Data Converter (UADC) corresponds to these standardized data formats, converting and integrating various formats of audit data into a unified standard format, facilitating rapid and efficient analysis and auditing.<\/p>\n<\/div>\n
\nUADC also streamlines and standardizes the conversion of audit data between various data formats such as CSV, XML, JSON, through its generic data conversion processing. It extends the syntax binding defined in European Standard EN 16931 and enables data conversion based on corresponding definitions, realizing the Japanese version of the Core Invoice Gateway. In semantic binding, it maps flat CSV columns to the hierarchical semantic definition elements of data, maintaining each corresponding definition for flexible adaptation to software upgrades and system changes.<\/p>\n<\/div>\n
\nUADC adopts xBRL-CSV as its standard CSV format, allowing effective representation of data with hierarchical structures in a single file. This reduces JOIN operations and administrative burdens in relational databases, simplifying and speeding up data processing.<\/p>\n<\/div>\n
\nFurthermore, UADC frees users from the constraints of specific vendors and platforms, solving long-term data storage issues. This allows companies to utilize past data in line with current business needs, improving data management efficiency and reducing risk.<\/p>\n<\/div>\n
\nUniversal Audit Data Converter (UADC)<\/div>\n![\"2311Picture1\"](\"https:\/\/www.sambuichi.jp\/wp-content\/uploads\/2023\/11\/2311Picture1.png\")
<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n2. Developments in Audit Data Standards<\/h2>\n\n\n2.1. Importance of Audit Data Standardization<\/h3>\n\nStandardization of audit data is crucial for achieving efficient and transparent audit processes. Key developments in this field include the Audit Data Standard released by AICPA in 2015 and the accounting data collection standard GB\/T 24589 Financial Information Technology \u2500 Data Interface of Accounting Software by China SAC\/TC 341 in 2010. These standards aim to enhance the consistency and quality of audit data.<\/p>\n<\/div>\n<\/div>\n
\n2.2. AICPA’s Audit Data Standard<\/h3>\n\nAICPA’s Audit Data Standard provides standards for the collection and reporting of audit data, supporting data audits. ISO 21378:2019 is greatly influenced by the expertise provided by ANSI in the USA for AICPA’s ADS.<\/p>\n<\/div>\n<\/div>\n
\n2.3. China’s GB\/T 24589<\/h3>\n\nChina’s GB\/T 24589 enables integrated collection of accounting data at the China National Audit Office and annual accounting detailed data analysis at data centers, contributing to the efficiency of audit processes.<\/p>\n<\/div>\n<\/div>\n
\n2.4. ISO\/TC 295 Audit data services<\/h3>\n\nBased on these backgrounds, ISO\/TC 295 Audit data services was established in 2015 as a PC (Project Committee) on China SAC’s proposal and formulated ISO 21378:2019 Audit data collection. In 2021, Study Group 1 Semantic model started its investigation under ISO\/TC 295. From 2022 to 2023, demonstration projects (POCs) such as the development of the Core Invoice Gateway and a specialized browser for journal entry information were conducted with the cooperation of XBRL Japan. These projects aim to further strengthen the methodology of collecting, converting, and analyzing audit data, aiming for further efficiency in the audit process. UADC was developed against this background of POC. From 2023, based on the proposal of SG1, the formulation of the semantic model standard specification in ISO\/AWI 21926footnote:[Semantic data model for audit data services https:\/\/www.iso.org\/standard\/<\/a><\/p>\n<\/div>\n\n86895.html] has started.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n3. Role of UADC<\/h2>\n\n\nUsing UADC enables the conversion and integration of audit data of various formats into a unified standard format, making data analysis and auditing more rapid and efficient.<\/p>\n<\/div>\n
\n3.1. Application to ESG Report Auditing<\/h3>\n\nThe role of UADC is particularly important in the auditing of Environmental, Social, and Governance (ESG) reports. Raw data from manufacturing equipment and factory control computers often have proprietary formats and are challenging to output in standard formats. By using UADC, this raw data can be converted into a unified data format, providing consistent information needed for auditing ESG reports. This improves the accuracy and transparency of ESG reporting, providing more reliable information to stakeholders.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n4. Improvements in Auditing with UADC<\/h2>\n\n\n4.1. Efficiency and Speed<\/h3>\n\nAudited companies can quickly respond to accounting audits using detailed data without spending time conforming to specific data formats required by audit authorities or firms. This not only saves time in the audit process but also opens the way to deeper analysis and more accurate audit results.<\/p>\n<\/div>\n<\/div>\n
\n4.2. Consistency and Quality Improvement<\/h3>\n\nUsing standardized data formats maintains the quality and consistency of data, enhancing the reliability of audit results. The ability to convert data from different systems and formats into a unified format makes it easier to integrate data from various sources.<\/p>\n<\/div>\n<\/div>\n
\n4.3. Summary<\/h3>\n\nThe conversion and standardization of audit data by UADC provide significant value in accounting audits and ESG report auditing. Audited companies can achieve quicker and more efficient audit processes, further enhancing the quality and transparency of ESG reporting. This improves the quality and speed of auditing and data analysis, providing more valuable insights for companies.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n5. Generic Data Conversion Processing<\/h2>\n\n\nUADC’s generic data conversion processing was developed to streamline and standardize the maintenance and development of data conversion programs that required modifications for new formats each time the source or target interface format changed. By specifying the destination of XML elements using XPath and our proprietary semantic path definitions for CSV columns, we have generalized the program coding for conversion processing. This targets various data formats such as CSV, XML, JSON.<\/p>\n<\/div>\n<\/div>\n<\/div>\n
\n6. Syntax Binding<\/h2>\n\n\nUADC’s syntax binding is a crucial function that enables consistent conversion of audit data between different data formats. In particular, in the European Standard EN16931 (standard for electronic invoicing based on VAT regulations), the concept of syntax binding is defined by CEN\/TS 16931-3. This clearly defines the correspondence between Core Invoice Usage Specification (CIUS) and the XML schema syntax of UBL 2.1 and UN\/CEFACT CII D16B.<\/p>\n<\/div>\n
\n6.1. Japanese Core Invoice Gateway<\/h3>\n\nIn the Japanese Core Invoice Gateway, we have refined the concept of syntax binding defined in EN16931 by introducing XPath. This allows the mutual conversion of electronic invoices (JP PINT) defined using UBL 2.1 in Open Peppol and those compliant with UN\/CEFACT CII for SME common EDI according to binding definitions. In the development process, we developed a new logical model for the Japanese Core Invoice and defined syntax binding with it.<\/p>\n<\/div>\n
\nCore Invoice Gateway<\/div>\n![\"2311Picture2\"](\"https:\/\/www.sambuichi.jp\/wp-content\/uploads\/2023\/11\/2311Picture2.png\")
<\/span><\/p>\n<\/div>\n\nProgram page:
\nhttps:\/\/www.sambuichi.jp\/core-japan\/<\/a><\/p>\n<\/div>\n<\/div>\n\n6.2. XBRL Taxonomy<\/h3>\n\nIn the Japanese Core Invoice Gateway, we have registered the syntax binding in the XBRL taxonomy and used xBRL-CSV files as intermediary files. The data conversion of JP PINT to standard CSV and standard CSV to common EDI electronic invoices is performed by a generic Java conversion program.<\/p>\n<\/div>\n<\/div>\n
\n6.3. Adapting to System Changes<\/h3>\n\nWith such a configuration, even if the interface definitions of the source and destination change, it is possible to respond by limiting to changes in the syntax binding definitions, without requiring modifications to the data conversion program. This improves the flexibility and sustainability of the data management process, contributing to the efficiency of audit operations.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n7. Semantic Binding<\/h2>\n\n\n7.1. Its Role<\/h3>\n\nAlthough the input and output of CSV files have been easily programmable and thus not considered much of a problem, there have often been many cases where it was questionable whether the content of these processes was clearly documented as specifications, leading to difficulties in maintenance. Semantic binding significantly reduces the burden of program maintenance by defining the mapping of transformation processes as a correspondence dictionary within the taxonomy definition.
\nSemantic binding in UADC (Uniform Audit Data Collection) is very important for maintaining semantic consistency of data. In this process, data is distributed according to the definition of semantic hierarchies, such as mapping specific CSV columns to auxiliary account codes like cost department codes or supplier codes.<\/p>\n<\/div>\n<\/div>\n
\n7.2. Flexibility and Adaptability<\/h3>\n\nThis approach is flexible and can adapt when the format of the CSV interface changes due to app version updates or when migrating to different software. By changing the semantic binding to the altered interface format and registering it with the XBRL taxonomy, it is possible to respond quickly without waiting for program modifications. This maintains the continuity and consistency of the data management process, improving the efficiency of audit work.<\/p>\n<\/div>\n<\/div>\n
\n7.3. Contributing to Efficiency and Speed<\/h3>\n\nSemantic binding in UADC significantly streamlines the data conversion process for auditing. It facilitates data migration between different software systems, maintaining data integrity and accuracy. Particularly in the fields of auditing and data analysis, this directly translates into saving time and resources, supporting quicker and more accurate decision-making.<\/p>\n<\/div>\n<\/div>\n
\n7.4. Wickham’s Tidy Data Concept<\/h3>\n\nAccording to Wickham’s definition of Tidy Data, data should be organized as follows:
\n– Each variable forms a column: Each variable (measurement or attribute) in the dataset has its own column.
\n– Each observation forms a row: Each row represents a single observation.
\n– Each type of observational unit forms a table: Different observational units are placed in separate tables.<\/p>\n<\/div>\n
\nRelated Article:
\nhttps:\/\/www.sambuichi.jp\/?cat=68<\/a><\/p>\n<\/div>\n\n
- \n
- 1. Overview<\/a><\/li>\n
- 2. Developments in Audit Data Standards<\/a>\n
- \n
- 2.1. Importance of Audit Data Standardization<\/a><\/li>\n
- 2.2. AICPA’s Audit Data Standard<\/a><\/li>\n
- 2.3. China’s GB\/T 24589<\/a><\/li>\n
- 2.4. ISO\/TC 295 Audit data services<\/a><\/li>\n<\/ul>\n<\/li>\n
- 3. Role of UADC<\/a>\n
- \n
- 3.1. Application to ESG Report Auditing<\/a><\/li>\n<\/ul>\n<\/li>\n
- 4. Improvements in Auditing with UADC<\/a>\n
- \n
- 4.1. Efficiency and Speed<\/a><\/li>\n
- 4.2. Consistency and Quality Improvement<\/a><\/li>\n
- 4.3. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 5. Generic Data Conversion Processing<\/a><\/li>\n
- 6. Syntax Binding<\/a>\n
- \n
- 6.1. Japanese Core Invoice Gateway<\/a><\/li>\n
- 6.2. XBRL Taxonomy<\/a><\/li>\n
- 6.3. Adapting to System Changes<\/a><\/li>\n<\/ul>\n<\/li>\n
- 7. Semantic Binding<\/a>\n
- \n
- 7.1. Its Role<\/a><\/li>\n
- 7.2. Flexibility and Adaptability<\/a><\/li>\n
- 7.3. Contributing to Efficiency and Speed<\/a><\/li>\n
- 7.4. Wickham’s Tidy Data Concept<\/a><\/li>\n
- 7.5. xBRL-CSV and Hierarchical Tidy Data<\/a><\/li>\n
- 7.6. In the Case of Journal Entry Information<\/a><\/li>\n<\/ul>\n<\/li>\n
- 8. Advantages of Adopting xBRL-CSV<\/a>\n
- \n
- 8.1. Reduction in JOIN Operations<\/a><\/li>\n
- 8.2. No Need for Database Maintenance<\/a><\/li>\n
- 8.3. Simplification and Speeding Up of Program Processing<\/a><\/li>\n
- 8.4. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 9. Liberation from Vendor Dependence<\/a>\n
- \n
- 9.1. Effects<\/a><\/li>\n
- 9.2. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 10. Long-Term Data Storage Issues<\/a>\n
- \n
- 10.1. Effects<\/a><\/li>\n
- 10.2. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 11. Accounting Data Standard Interface<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n
\n\n\n\nPresentation notes are available at:
\nhttps:\/\/www.sambuichi.jp\/wu_wei\/index.html?note=\/2023\/11\/dfda6aee-7c62-4e7a-88fc-055c8e2283fc<\/a><\/p>\n<\/div>\n\nNobuyuki SAMBUICHI
\nISO\/TC295 Audit data services<\/a>
\nConvener at SG1 Semantic model
\nCo-project leader at ISO\/AWI 21926 Semantic data model for audit data services<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n1. Overview<\/h2>\n
\n\nStandardization of audit data is essential for a transparent and efficient audit process. Significant developments in this field include the AICPA’s “Audit Data Standard” and China’s GB\/T 24589. These standards aim to enhance the quality and consistency of audit data. The AICPA’s Audit Data Standard[1<\/a>]<\/sup> provides standards for data auditing, and China’s GB\/T 24589 mandates data collection from all domestic accounting software and analysis by regulatory agencies. ISO\/TC 295 Audit data services[2<\/a>]<\/sup> established ISO 21378:2019 Audit data collection[3<\/a>]<\/sup>.<\/p>\n<\/div>\n
\nThe Universal Audit Data Converter (UADC) corresponds to these standardized data formats, converting and integrating various formats of audit data into a unified standard format, facilitating rapid and efficient analysis and auditing.<\/p>\n<\/div>\n
\nUADC also streamlines and standardizes the conversion of audit data between various data formats such as CSV, XML, JSON, through its generic data conversion processing. It extends the syntax binding defined in European Standard EN 16931 and enables data conversion based on corresponding definitions, realizing the Japanese version of the Core Invoice Gateway. In semantic binding, it maps flat CSV columns to the hierarchical semantic definition elements of data, maintaining each corresponding definition for flexible adaptation to software upgrades and system changes.<\/p>\n<\/div>\n
\nUADC adopts xBRL-CSV as its standard CSV format, allowing effective representation of data with hierarchical structures in a single file. This reduces JOIN operations and administrative burdens in relational databases, simplifying and speeding up data processing.<\/p>\n<\/div>\n
\nFurthermore, UADC frees users from the constraints of specific vendors and platforms, solving long-term data storage issues. This allows companies to utilize past data in line with current business needs, improving data management efficiency and reducing risk.<\/p>\n<\/div>\n
\nUniversal Audit Data Converter (UADC)<\/div>\n<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n2. Developments in Audit Data Standards<\/h2>\n
\n\n2.1. Importance of Audit Data Standardization<\/h3>\n
\nStandardization of audit data is crucial for achieving efficient and transparent audit processes. Key developments in this field include the Audit Data Standard released by AICPA in 2015 and the accounting data collection standard GB\/T 24589 Financial Information Technology \u2500 Data Interface of Accounting Software by China SAC\/TC 341 in 2010. These standards aim to enhance the consistency and quality of audit data.<\/p>\n<\/div>\n<\/div>\n
\n2.2. AICPA’s Audit Data Standard<\/h3>\n
\nAICPA’s Audit Data Standard provides standards for the collection and reporting of audit data, supporting data audits. ISO 21378:2019 is greatly influenced by the expertise provided by ANSI in the USA for AICPA’s ADS.<\/p>\n<\/div>\n<\/div>\n
\n2.3. China’s GB\/T 24589<\/h3>\n
\nChina’s GB\/T 24589 enables integrated collection of accounting data at the China National Audit Office and annual accounting detailed data analysis at data centers, contributing to the efficiency of audit processes.<\/p>\n<\/div>\n<\/div>\n
\n2.4. ISO\/TC 295 Audit data services<\/h3>\n
\nBased on these backgrounds, ISO\/TC 295 Audit data services was established in 2015 as a PC (Project Committee) on China SAC’s proposal and formulated ISO 21378:2019 Audit data collection. In 2021, Study Group 1 Semantic model started its investigation under ISO\/TC 295. From 2022 to 2023, demonstration projects (POCs) such as the development of the Core Invoice Gateway and a specialized browser for journal entry information were conducted with the cooperation of XBRL Japan. These projects aim to further strengthen the methodology of collecting, converting, and analyzing audit data, aiming for further efficiency in the audit process. UADC was developed against this background of POC. From 2023, based on the proposal of SG1, the formulation of the semantic model standard specification in ISO\/AWI 21926footnote:[Semantic data model for audit data services https:\/\/www.iso.org\/standard\/<\/a><\/p>\n<\/div>\n
\n86895.html] has started.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n3. Role of UADC<\/h2>\n
\n\nUsing UADC enables the conversion and integration of audit data of various formats into a unified standard format, making data analysis and auditing more rapid and efficient.<\/p>\n<\/div>\n
\n3.1. Application to ESG Report Auditing<\/h3>\n
\nThe role of UADC is particularly important in the auditing of Environmental, Social, and Governance (ESG) reports. Raw data from manufacturing equipment and factory control computers often have proprietary formats and are challenging to output in standard formats. By using UADC, this raw data can be converted into a unified data format, providing consistent information needed for auditing ESG reports. This improves the accuracy and transparency of ESG reporting, providing more reliable information to stakeholders.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n4. Improvements in Auditing with UADC<\/h2>\n
\n\n4.1. Efficiency and Speed<\/h3>\n
\nAudited companies can quickly respond to accounting audits using detailed data without spending time conforming to specific data formats required by audit authorities or firms. This not only saves time in the audit process but also opens the way to deeper analysis and more accurate audit results.<\/p>\n<\/div>\n<\/div>\n
\n4.2. Consistency and Quality Improvement<\/h3>\n
\nUsing standardized data formats maintains the quality and consistency of data, enhancing the reliability of audit results. The ability to convert data from different systems and formats into a unified format makes it easier to integrate data from various sources.<\/p>\n<\/div>\n<\/div>\n
\n4.3. Summary<\/h3>\n
\nThe conversion and standardization of audit data by UADC provide significant value in accounting audits and ESG report auditing. Audited companies can achieve quicker and more efficient audit processes, further enhancing the quality and transparency of ESG reporting. This improves the quality and speed of auditing and data analysis, providing more valuable insights for companies.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n5. Generic Data Conversion Processing<\/h2>\n
\n\nUADC’s generic data conversion processing was developed to streamline and standardize the maintenance and development of data conversion programs that required modifications for new formats each time the source or target interface format changed. By specifying the destination of XML elements using XPath and our proprietary semantic path definitions for CSV columns, we have generalized the program coding for conversion processing. This targets various data formats such as CSV, XML, JSON.<\/p>\n<\/div>\n<\/div>\n<\/div>\n
\n6. Syntax Binding<\/h2>\n
\n\nUADC’s syntax binding is a crucial function that enables consistent conversion of audit data between different data formats. In particular, in the European Standard EN16931 (standard for electronic invoicing based on VAT regulations), the concept of syntax binding is defined by CEN\/TS 16931-3. This clearly defines the correspondence between Core Invoice Usage Specification (CIUS) and the XML schema syntax of UBL 2.1 and UN\/CEFACT CII D16B.<\/p>\n<\/div>\n
\n6.1. Japanese Core Invoice Gateway<\/h3>\n
\nIn the Japanese Core Invoice Gateway, we have refined the concept of syntax binding defined in EN16931 by introducing XPath. This allows the mutual conversion of electronic invoices (JP PINT) defined using UBL 2.1 in Open Peppol and those compliant with UN\/CEFACT CII for SME common EDI according to binding definitions. In the development process, we developed a new logical model for the Japanese Core Invoice and defined syntax binding with it.<\/p>\n<\/div>\n
\nCore Invoice Gateway<\/div>\n<\/span><\/p>\n<\/div>\n\nProgram page:
\nhttps:\/\/www.sambuichi.jp\/core-japan\/<\/a><\/p>\n<\/div>\n<\/div>\n\n6.2. XBRL Taxonomy<\/h3>\n
\nIn the Japanese Core Invoice Gateway, we have registered the syntax binding in the XBRL taxonomy and used xBRL-CSV files as intermediary files. The data conversion of JP PINT to standard CSV and standard CSV to common EDI electronic invoices is performed by a generic Java conversion program.<\/p>\n<\/div>\n<\/div>\n
\n6.3. Adapting to System Changes<\/h3>\n
\nWith such a configuration, even if the interface definitions of the source and destination change, it is possible to respond by limiting to changes in the syntax binding definitions, without requiring modifications to the data conversion program. This improves the flexibility and sustainability of the data management process, contributing to the efficiency of audit operations.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n7. Semantic Binding<\/h2>\n
\n\n7.1. Its Role<\/h3>\n
\nAlthough the input and output of CSV files have been easily programmable and thus not considered much of a problem, there have often been many cases where it was questionable whether the content of these processes was clearly documented as specifications, leading to difficulties in maintenance. Semantic binding significantly reduces the burden of program maintenance by defining the mapping of transformation processes as a correspondence dictionary within the taxonomy definition.
\nSemantic binding in UADC (Uniform Audit Data Collection) is very important for maintaining semantic consistency of data. In this process, data is distributed according to the definition of semantic hierarchies, such as mapping specific CSV columns to auxiliary account codes like cost department codes or supplier codes.<\/p>\n<\/div>\n<\/div>\n\n7.2. Flexibility and Adaptability<\/h3>\n
\nThis approach is flexible and can adapt when the format of the CSV interface changes due to app version updates or when migrating to different software. By changing the semantic binding to the altered interface format and registering it with the XBRL taxonomy, it is possible to respond quickly without waiting for program modifications. This maintains the continuity and consistency of the data management process, improving the efficiency of audit work.<\/p>\n<\/div>\n<\/div>\n
\n7.3. Contributing to Efficiency and Speed<\/h3>\n
\nSemantic binding in UADC significantly streamlines the data conversion process for auditing. It facilitates data migration between different software systems, maintaining data integrity and accuracy. Particularly in the fields of auditing and data analysis, this directly translates into saving time and resources, supporting quicker and more accurate decision-making.<\/p>\n<\/div>\n<\/div>\n
\n7.4. Wickham’s Tidy Data Concept<\/h3>\n
\nAccording to Wickham’s definition of Tidy Data, data should be organized as follows:
\n– Each variable forms a column: Each variable (measurement or attribute) in the dataset has its own column.
\n– Each observation forms a row: Each row represents a single observation.
\n– Each type of observational unit forms a table: Different observational units are placed in separate tables.<\/p>\n<\/div>\n\nRelated Article:
\nhttps:\/\/www.sambuichi.jp\/?cat=68<\/a><\/p>\n<\/div>\n\n - 10.2. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 9.2. Summary<\/a><\/li>\n<\/ul>\n<\/li>\n
- 8.2. No Need for Database Maintenance<\/a><\/li>\n
- 7.2. Flexibility and Adaptability<\/a><\/li>\n
- 6.2. XBRL Taxonomy<\/a><\/li>\n
- 4.2. Consistency and Quality Improvement<\/a><\/li>\n
- 4. Improvements in Auditing with UADC<\/a>\n
- 2.2. AICPA’s Audit Data Standard<\/a><\/li>\n
- 2. Developments in Audit Data Standards<\/a>\n