Data Sharing Community
Welcome to the Portal of the CDQ Data Sharing CommunityThe CDQ Data Sharing Community is a trusted network of user companies to manage business partner data collaboratively. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
What's new? (RSS)New data source integrated: Hochschulrektorenkonferenz (DE.HRK) (17 April 2025)SummaryWe are pleased to announce the integration of a new data source into the Business Partner Lookup service: Hochschulrektorenkonferenz (DE.HRK). DetailsThe Hochschulrektorenkonferenz provides official information about all German universities. Each record may include the following details:
Enhanced Weekly Mail Summaries Powered by AI (16 April 2025)We’re excited to announce improvements to our weekly emails, which now include AI-generated summaries for charts based on customer data. These summaries help you quickly understand what has changed and identify areas where data quality can be enhanced. Key Enhancements
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Business Partner Status Available in the Tax Guard Identifier Qualification Results (14 April 2025)Business Partner status is now provided together with identifier qualification results in Tax Guard. An additional response object called businessPartnerStatus indicates the Business Partner status: type, value and reference to the Wiki. Moreover, the value is present in the First Time Right application (as a result of the Qualify button execution) in case the status is different than UNKNOWN or ACTIVE. Many data sources (like VIES, AT.FON or BZST) do not provide the status of a Business Partner. Thus, in such cases we return UNKNOWN value. All the possible status values can be found here: https://meta.cdq.com/Business_partner/status/type. ... further resultsData modelAn important prerequisite for collaborative data management is a common understanding of the shared data. For the CDQ Data Sharing Community, this common understanding is specified by the CDQ Data Model. The concepts of this model are defined and documented in this wiki which can be used as a business vocabulary. Moreover, the wiki provides a machine-readable interface to reuse this metadata by using semantic annotations. ![]() Data maintenance proceduresA procedure is a common standard or "how-to" for a specific data management task. Within the CDQ Data Sharing Community, companies agree on such procedures to ensure similar rules and guidelines for similar tasks. For several countries, the CDQ Wiki provides such information, e.g. data quality rules, trusted information sources, legal forms, or tax numbers. Try
or select another country from the list. |
Data sources
Metadata and Standards: Metadata-driven Data QualityData 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.
Data Quality RulesTransformation of human-documented data requirements into executable data quality rules is mostly a manual IT effort. Changing requirements cause IT efforts again and again. Some checks, e.g. tax number validity (not just format!), require external services. Other checks, e.g. validity of legal forms, require managed reference data (e.g. legal forms by country, plus abbreviations). Continuous data quality assurance (i.e. batch analyses) and real-time checks in workflows often use different rule sets. Data requirements and related reference data are collected and updated collaboratively by the Data Sharing Community. Data quality rules are derived from these requirements automatically. All data quality rules are executed behind 1 interface, in real-time. Batch jobs and single-record checks use the same rule set and can be integrated by APIs. For proving that a data quality rule is content-wise correct we maintain supporting document(s) per data quality rule which share the rule's source. This could be:
We manage the URL (if any), a screenshot of the relevant parts (if any) and the source's name (e.g. Community member data standard, European Commission, National ....) See Identifier format invalid (SIREN (France)) as an exemplary rule that was specified and implemented based on information provided by the OECD. |