Data Sharing Community

Welcome to the Portal of the CDQ Data Sharing Community

We’re excited to announce the implementation of a new risk factor in our Email Domain Guard App

Details

New Risk Score: 65
Reason for Update: Some newly created domains are not visible in the WHOIS database, which prevents us from establishing their creation date, necessary for assigning domain age risks

Impact

From 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:

Charitable Trusts
Incorporated Societies
Sole Traders

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 offers

CDQ now provides records from BetReg, which include:

Medical Facility Names – Officially registered names of healthcare establishments.
Addresses – Verified location data for accurate referencing.
GLN Identifiers – Unique Global Location Numbers for precise identification.

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 results

Data model

An 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.

This is a graph with borders and nodes that may contain hyperlinks.

Data maintenance procedures

A 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

Active data sources	Records
Data source BR.RF	62,636,987
Data source VIES	50,000,000
Data source CDQ.INTEL	47,131,393
Data source FR.RC	39,721,461
Data source GB-EAW.CR	8,324,797
Data source US-FL.BER	5,938,565
Data source JP.CR	5,545,935
Data source AU.BR	4,768,982
... further results

The CDQ Data Sharing Community uses a collaboratively managed reference data repository. This incorporates the integration of external data sources for enriching or validating business partner and address data. Examples of available data sources are 316 countries (e.g. WORLD (World), AT (Österreich, Austria, Autriche, 奥地利), BE (Belgien, Belgium, Belgique, België, 比利时)), 993 legal forms (e.g. ), and 72 active business partner data sources (e.g. Data source CDQ.POOL, Data source VIES, Data source CH.UIDR).

Metadata and Standards: Metadata-driven Data Quality

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.

Metadata	Short description
Administrative area metadata	Managed reference data for administrative areas with language-specific terms and short names according to ISO 3166-2.
Bank account metadata	Managed reference data for bank accounts worldwide.
Business identifier metadata	Managed reference data for types of identifiers per country.
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.
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.
Locality metadata	Managed reference data for localities, such as exonyms.
Post code metadata	Managed reference data for post codes
Postal delivery point metadata	Managed reference data for postal delivery points, such as Post Office Boxes used for identification, extraction, harmonization and standardization.
Registration authority metadata	Managed reference data for issuing bodies of identifiers
Street metadata	Managed reference data for thoroughfares of type Street (CDQ.POOL) used for harmonization and standardization.

Data Quality Rules

Transformation 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:

a public authority source
any other trustful webpage
a data standard of a specific community member

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.