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 Risk Factor Introduced for Email Domain Guard App (22 November 2024)We’re excited to announce the implementation of a new risk factor in our Email Domain Guard App Details
ImpactFrom now on, if a domain with valid characteristics is not found in the WHOIS database, it will receive a risk score of 65. This change aims to emphasize the potential threat of dealing with domains that cannot be sufficiently analyzed. Expansion of New Zealand Business Number Register Coverage (19 November 2024)We are pleased to announce an update to the New Zealand Business Number Register (NZ.CR) integration in the Business Partner Lookup service. This update expands the scope of available data to include business partners with the following legal forms:
Previously, records for entities with these legal forms were not provided. With this enhancement, users gain access to a more comprehensive dataset for New Zealand, supporting more detailed and accurate business partner assessments. This update is available immediately in the Business Partner Lookup service. New Data Source Integration: BetReg - Swiss Healthcare Establishments Database (18 November 2024)We are pleased to announce the integration of BetReg, a comprehensive database of healthcare-related organizations in Switzerland. This resource supports compliance with Swiss narcotics regulations by listing establishments licensed to handle controlled substances. BetReg, based on Art. 66 para.2 of the Ordinance of 25 May 2011 on Narcotics Control (NarcCO), includes companies authorized by Swissmedic to procure controlled substances and other entities in the healthcare sector. What BetReg offersCDQ now provides records from BetReg, which include:
This integration will allow our customers to access the most current and authoritative information on Swiss healthcare facilities, supporting informed decision-making and regulatory compliance. ... 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. |