Data Sharing Community

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Welcome to the Portal of the CDQ Data Sharing Community

The CDQ Data Sharing Community (formerly Corporate Data League, CDL) is a trusted network of user companies to manage business partner data collaboratively.

  Collaboration       Fraud protection       Data model       Apps       API       Support

Metadata management · Data quality rules · Reference data  · Procedures · Blacklists · Address curation

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 CDL collaboration approach is based on a trusted network of user companies that share and collaborativelay maintain this data.
Stages of Data Sharing

Data model

An important prerequisite for collaborative data management is a common understanding of the shared data. For the CDL, this common understanding is specified by the CDL 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. Currently, the model comprises 32 concepts and 109 properties.

Data model digraph data model business partner address dot.png
  • Address: An address provides information about the physical location of a business partner. It can be specialized in a certain context: e.g. delivery address or invoice address.
  • Name: The business partner name identifies a business partner. There are different types of names.... further results

Data quality rules

Data quality rules are are a special type of business rules that measure and ensure data quality. Currently, 1409 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.


A procedure is a common standard or "how-to" for a specific data management task. Within the Corporate Data League, 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.


The CDQ infrastructure comprises three main pillars:

  • CDL API to provide technical access to all CDQ services,
  • CDL Apps to access these services by web applications, and
  • Wikis to provide metadata management services.

Beside conceptual and technical documentation provided by this repository, the CDQ Team will be glad to provide individual support if you are facing any problems with the CDQ infrastructure. Please use the CDQ Service Desk for all support requests.

Reference data

Active external sourcesActivationCompany records
France Register of Companies2018-03-0511,100,000
Companies House2018-02-0510,216,253
German Commercial Register (Germany)2019-12-105,000,000
Japanese National Tax Agency2019-03-224,717,525
Australian Business Register (Australia)2018-04-152,900,000
... further results
The Corporate Data League uses a collaboratively managed reference data repository for the following purposes:

Examples of available reference data are 258 countries (e.g. Qatar, Sweden, Nigeria), 228 legal forms (e.g. Fundação (PT), Stille vennootschap (CW), General Partnership (US)), and 154 External managed data source (e.g. Australian Business Register (Australia), France Register of Companies, Bundesamt für Statistik - UID Register (CH)).

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 CDL 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 CDL Members. Other CDL 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 CDL Members. You can lookup shared Trust Scores to check a new bank account and to ensure that this account is already used by another CDL Member.

CDL Apps

Most CDL cloud services are provided by web services. 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.


From an integration perspective, CDL web services are the most important component of the CDL infrastructure. They provide the technical link between your business applications and the CDL cloud services. We follow the REST design principle for web services which allows for lightweight interface design and easy integration. Of course, all web services are also available at WSDL interfaces.

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