Data Sharing Community

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Welcome to the Portal of the CDQ Data Sharing Community

The CDQ Data Sharing Community is a trusted network of user companies to manage business partner data collaboratively.

  Collaboration       Fraud protection       CDQ data model       Apps       API       Support

Metadata · Data Quality Rules · Data Sources  · Procedures

What's new? (RSS)

Data Quality Dashboard: We are hearing you. (16 May 2022)

We thank to everyone, who contributed to the review of the Data Quality Dashboard. We used your feedback and made changes to allow better user experience. Here is what is new/changed:

  • Colour-coding of chart Records without Critical Violation per Country is now more clear in telling the story
  • Benchmarking section has two new charts to support the message in existing Data Quality Performance Benchmark table. The overall benchmarking and benchmark broken down by country (top ten countries from data mirror - by total number of records for given country). The green/red bubble represents the organisation view, while the grey bubble are the other organisations in the benchmark set. If your bubble is red, your data quality is worse than the average, and it's green in opposite.
  • We reduce whitespaces and big paddings in charts

Natural person identification improved: Better handling of academic degrees and titles (12 May 2022)

CDQ's natural person identification feature identifies and categorizes given business partner data that represents natural persons. It searches for evidence of personal information in the given data; particularly in the provided name. The identification process was improved to better categorize records that comprise titles in their name, such as Dr., Prof., PhD, Dr. med. etc. This feature improvement significantly reduces the false-positive rate.

Country drop downs reflect actual list of countries (12 May 2022)

A lot of the configuration options (e.g. Generate Data Dump, Validation Configuration, etc) allow to select a specific country (or multiple countries).

However, using this feature was impractical because we were displaying all of the countries in the list, even if we didn't have any records for a particular country.

As of now, the country drop down lists are adjusted accordingly - we only countries if there are any records available.

... further results

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.

AddressBusiness partnerBank accountFraud caseBusiness partner/relationBusiness partner/nameBusiness partner/partner profileBusiness partner/relation/classBusiness partner/statusFraud case/fraudsterBusiness partner/identifierBusiness partner/legal formThis 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.

CDQ Apps

Web applications, also called CDQ Apps, to access certain features for demonstration purposes and to configure certain features which are provided then via APIs directly.


From an integration perspective, CDQ web services are the most important component of the CDQ infrastructure. They provide the technical link between your business applications and the CDQ 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.

Data sources

Active data sourcesRecords
Data source VIES50,000,000
Data source BR.RF46,535,779
Data source FR.RC32,984,712
Data source DE.RC6,811,574
Data source GB-EAW.CR6,155,656
Data source JP.CR5,152,784
Data source US-FL.BER4,313,343
Data source PL.NOBR3,461,846
... 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 317 countries (e.g. WORLD (World), AT (Österreich, Austria, Autriche, 奥地利), BE (Belgien, Belgium, Belgique, België, 比利时)), 942 legal forms (e.g. ), and 57 active business partner data sources (e.g. Data source CDQ.POOL, Data source VIES, Data source CH.UIDR).

Capability/Data Quality Measurement

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, auditor approved. All data quality rules are executed behind 1 interface, in real-time, 1’000+ rules in < 1s. Batch jobs and single-record checks use the same rule set and can be integrated by APIs. If reference data (e.g. correct tax numbers) is available, fix proposals are provided for incorrect records.

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.