Difference between revisions of "Capability/metadata and standards"
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In many countries, official company information is available as Open Data, but the lack of a standardized data model or provision method complicates the process of integrating this data. The Data Sharing Community actively collaborates to identify global data requirements and reference data sources, whether Open Data or commercial. | In many countries, official company information is available as Open Data, but the lack of a standardized data model or provision method complicates the process of integrating this data. The Data Sharing Community actively collaborates to identify global data requirements and reference data sources, whether Open Data or commercial. | ||
+ | | description = Ensuring data quality is crucial for businesses seeking to maintain compliance with ever-evolving legal, regulatory, and industry standards. In today’s global environment, adhering to dynamic data requirements is a continuous challenge, as these standards frequently change based on national regulations. As regulations vary by country, organizations must track and update their compliance criteria to ensure alignment across all jurisdictions they operate within. | ||
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+ | Many countries offer official company information as Open Data; however, the absence of a standardized data model or provision method complicates the process of integrating this data into business operations. The Metadata and Standards initiative within the Data Sharing Community aims to address these challenges by identifying and aggregating global data requirements and reference data sources—whether Open Data or commercially sourced. Through this collaborative effort, businesses can ensure they have access to accurate, reliable, and standardized data that is critical for compliance and risk mitigation. | ||
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+ | The Metadata and Standards focus on managing a wide variety of metadata types that support compliance, data quality, and standardization. These include: | ||
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+ | * Administrative Area Metadata: This includes managed reference data for administrative areas, using language-specific terms and short names compliant with ISO 3166-2 standards. This ensures consistency in defining regions and administrative units across different countries and languages. | ||
+ | * Business Identifier Metadata: Managed reference data that captures types of business identifiers for each country, helping companies validate and track identifiers such as VAT numbers, business registration numbers, and tax IDs. | ||
+ | * Compliance Lists Metadata: A collection of managed reference data related to compliance lists used in sanction and watchlist screening services. This ensures businesses stay compliant by screening their partners and vendors against global and regional sanctions lists. | ||
+ | * Country Metadata: Managed reference data for countries, featuring language-specific names and short names based on the ISO 3166-2 standard. This helps standardize country references across different datasets. | ||
+ | * Data Quality Rules: Comprehensive documentation of data quality rules, including explanations and technical constraints. These rules validate business partner data to ensure it meets predefined quality and compliance standards. | ||
+ | * Legal Form Metadata: Managed reference data for legal forms, including official abbreviations and country-specific variations. This ensures businesses have the correct and standardized references for company legal structures across different regions. | ||
+ | * City Metadata: Managed reference data for localities, helping businesses validate and harmonize location data to ensure accuracy in address and regional reporting. | ||
+ | * Post Code Metadata: A standardized reference for postal codes across different countries. This metadata ensures that postal codes are accurate and follow country-specific formats. | ||
+ | * Postal Delivery Point Metadata: Managed reference data for postal delivery points such as Post Office Boxes. This data is crucial for identification, extraction, harmonization, and standardization of delivery points in various postal systems. | ||
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+ | By leveraging this structured and standardized metadata, businesses can ensure compliance with complex and diverse regulatory frameworks, improve data quality, and streamline integration across global systems. The Metadata and Standards approach ensures that companies have access to reliable, high-quality reference data, enabling them to stay compliant, reduce risk, and make informed decisions. | ||
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Latest revision as of 12:38, 17 September 2024
Name Name of a concept, e.g. a data model concept. In contrast to terms, the name does not depend on a given context, e.g. a country-specific language. | Metadata and Standards |
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Short description Informal and short human-readable definition of a concept. | Data quality plays a pivotal role in ensuring compliance with legal, regulatory, and industry standards. One of the core challenges in achieving high data quality is adhering to dynamic data requirements that evolve due to changes in national regulations. These requirements vary by country, making it essential for businesses to track and update compliance criteria continuously.
In many countries, official company information is available as Open Data, but the lack of a standardized data model or provision method complicates the process of integrating this data. The Data Sharing Community actively collaborates to identify global data requirements and reference data sources, whether Open Data or commercial. |
Description Informal and comprehensive human-readable definition of a concept. | Ensuring data quality is crucial for businesses seeking to maintain compliance with ever-evolving legal, regulatory, and industry standards. In today’s global environment, adhering to dynamic data requirements is a continuous challenge, as these standards frequently change based on national regulations. As regulations vary by country, organizations must track and update their compliance criteria to ensure alignment across all jurisdictions they operate within.
Many countries offer official company information as Open Data; however, the absence of a standardized data model or provision method complicates the process of integrating this data into business operations. The Metadata and Standards initiative within the Data Sharing Community aims to address these challenges by identifying and aggregating global data requirements and reference data sources—whether Open Data or commercially sourced. Through this collaborative effort, businesses can ensure they have access to accurate, reliable, and standardized data that is critical for compliance and risk mitigation. The Metadata and Standards focus on managing a wide variety of metadata types that support compliance, data quality, and standardization. These include:
By leveraging this structured and standardized metadata, businesses can ensure compliance with complex and diverse regulatory frameworks, improve data quality, and streamline integration across global systems. The Metadata and Standards approach ensures that companies have access to reliable, high-quality reference data, enabling them to stay compliant, reduce risk, and make informed decisions. |
Managed metadata types
Short description | Data concept | |
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Administrative area metadata | Managed reference data for administrative areas with language-specific terms and short names according to ISO 3166-2. | Administrative Area (CDQ.POOL) |
Bank account metadata | Managed reference data for bank accounts worldwide. | Bank (CDQ.POOL) Bank Account (CDQ.POOL) |
Business identifier metadata | Managed reference data for types of identifiers per country. | Identifier (CDQ.POOL) |
CDQ Data Model | Basic data concepts of CDQ Cloud Services. | |
Compliance lists metadata | Managed reference data about compliance lists considered in the sanction and watchlist screening services | |
Country metadata | Managed reference data for countries with language-specific names and short names according to ISO 3166-2. | Country (CDQ.POOL) |
Data Quality Rules | Documentation of data quality rules with explanation and technical constraints to validate business partner data records. | |
Data quality rules function library | Data quality rule functions are methods implemented in a programming language for being used in data quality rule implementations. They can be e.g. used in custom data quality rules similar to functions employed by business users in popular spreadsheet applications such as Microsoft Excel. | |
Legal form metadata | Managed reference data for legal forms with official and commonly used abbreviations and corresponding country. | Legal Form (CDQ.POOL) |
Locality metadata | Managed reference data for localities, such as exonyms. | Locality (CDQ.POOL) |
Post code metadata | Managed reference data for post codes | Post Code (CDQ.POOL) |
Postal delivery point metadata | Managed reference data for postal delivery points, such as Post Office Boxes used for identification, extraction, harmonization and standardization. | Postal Delivery Point (CDQ.POOL) |
Registration authority metadata | Managed reference data for issuing bodies of identifiers | Identifier Issuing Body (CDQ.POOL) |
Street metadata | Managed reference data for thoroughfares of type Street (CDQ.POOL) used for harmonization and standardization. | Thoroughfare (CDQ.POOL) |
Facts and Figures
Administrative Areas | 3,773
|
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Countries | 316
|
Data Quality Rules | 3,103
|
Data Sources (external) | 235
|
Identifiers | 497
|
Legal Forms | 1,002
|
Registration authorities (issuing bodies) | 1,277
|
Terms | 1,674
|