Data Sharing Community

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Welcome to the Portal of the CDQ Data Sharing Community

Data Sharing

Business partner data management is heavily redundant: Many companies manage data for the same entities such as country names and codes, bill-to, ship-to, and ordering addresses, or legal hierarchies of customers and suppliers. The CDQ collaboration approach is based on a trusted network of user companies that share and collaborativelay maintain this data.
Data Sharing

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.

CDQ Data Model digraph datamodel dot.png

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. By selecting a country, the metadata repository is queried for any kind of information which is available for this country. That means, the for each country the

are searched in the repository. In addition there further country specific information such as address formats is displayed.

CDQ Apps

Most CDQ cloud services are provided by the CDQ API. Beside these technical interfaces, some functionality is also provided by web applications at This website simplifies using some more complex services such as address curation, duplicate matching, or payment fraud protection


For easy development, discovery, and integration visit our supporting documentation

Reference data

Active external sourcesSource id
Alaska Business Entity RegisterUS-AK.BER
Australian Business Register (Australia)AU.BR
Bundesamt für Statistik - UID Register (CH)CH.UIDR
Bundeszentralamt für Steuern (Germany)BZST
CDQ Community Data PoolCDQ.POOL
Canada Corporate RegistryCA.CR
Companies HouseGB-EAW.CR
Country codes and names
... 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. Data sources that explicitly provide information about companies are distinguished from data sources that just provide partial information related to a business partner (e.g. Australian Business Register (Australia)) or just addresses (, Open Street Maps) or additional company data (VAT Information Exchange System (VIES))).

Examples of available reference data are 0 countries (e.g. ), 0 legal forms (e.g. ), and potentially (not all sources are integrated yet) 54 External managed data source (e.g. Companies House, VAT Information Exchange System (VIES), Kamer van Koophandel (Netherlands)).

Data quality rules

Data quality rules are are a special type of business rules that measure and ensure data quality. Currently, 1171 data quality rules are checked to ensure a high level of data quality. Each business rule is documented in a form that is understandable by business users referencing the defined data model concepts in the rule definitions.

The standardization approach of the data quality standard across the community is a collaborative endeavor and managed by CDQ. Rules need to be analyzed and adapted on a permanent basis. Existing rules may become inapplicable, or it may be necessary to introduce new rules. Some business rules specify which data values are valid for specific attributes. For this purpose, reference data is collected and documented on these pages (e.g. countries). Integrating new reference data usually goes along with the adaptation of an existing business rule or the creation of new ones.

Fraud protection

Bank account whitelist

Companies are facing an ever increasing number of digitized frauds, meanwhile on a very professional level. Among other types, falsified invoices are causing significant financial damage, in some cases more than 1 Mio. USD by just one attack. One critical challenge to uncover those fraud attacks is to identify bank accounts (e.g. given by an invoice) which are not owned by the declared business partner (e.g. the supplier of an invoice) but by a third party, i.e. the attacker. The CDQ Data Sharing community is addressing this challenge by sharing information on known fraud cases and on proven bank accounts. The Fraud Case Database comprises known fraud cases, shared by community members. Other members can lookup these cases by bank account data (e.g. IBAN) to automate screening for critical accounts. On the other hand, the Whitelist comprises bank accounts which are declared "save" by community members. You can lookup shared Trust Scores to check a new bank account and to ensure that this account is already used by another member.