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)

BP Lookup response extended with a new attribute "goldenRecordUse" (20 October 2021)

New attribute has been added to Business Partner Lookup service (

"goldenRecordUse" informs which records were used when creating a Golden Record. The new information can have either:

  • "PRIMARY" (for the base record according to the primary data source)

values for matches which contribute at least one attribute to the Golden Record.

This feature is only relevant if a Golden Record is created, i.e. if feature SHOW_GOLDENRECORD_STANDARD or SHOW_GOLDENRECORD_QUICK is activated and can help grouping matches which are used for the Golden Record creation.

DNB records can be now downloaded as JSON files (13 September 2021)

A new functionality in Business Partner Lookup app allows to download a single DNB record and save it to JSON file. The file contains Company Profile, Linkage, and Executives data (cmpelk v2).

Lookup requests and batch overlap check jobs are now configurable (31 August 2021)

A new app has been just introduced into our portfolio. The Business Partner Lookup Configurator application allows the user to create custom lookup configurations, which can be used when sending single business partner lookup request or running a batch overlap check job.
Once the configuration is saved, it can be used via API as well as in Batch Overlap Check web application.

Moreover, business partner lookup configuration, similarily to data validation and data curation configurations, can be selected in the API Key Management App and assigned to an API key.

... 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 Exchange API (Services to upload, manipulate, and download businesspartner data in the CDL Cloud.)
  • Analytics Query API (This is an API for fetching metrics from the e2e database.)
  • Bankaccount Data API (Services to validate and to confirm bank account data, and to manage payment fraud cases.)
  • Data Compliance API (Services to search and read compliance information.)
  • Data Curation API (Services to curate and enrich business partner and address data.)
  • Data Matching API (Services to maintain matching definition used as configuration for matching jobs and services to match data with a job.)
  • Data Validation API (Services to validate businesspartners and identifiers.)
  • Machine Learning API (This is an API for using Data Analytics team's machine learning services.)
  • Referencedata API (Services to search and read reference data.)

Data sources

Active data sourcesRecords
Data source VIES50,000,000
Data source BR.RF46,535,779
Data source FR.RC31,791,276
Data source DE.RC6,108,555
Data source GB-EAW.CR5,698,101
Data source JP.CR5,069,054
Data source US-FL.BER3,895,664
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 316 countries (e.g. US (Vereinigte Staaten, United States of America, États-Unis d'Amérique, 美国), BR (Brasilien, Brazil, Brésil, Brasil, 巴西), GR (Griechenland, Elláda, Greece, Grèce, 希腊)), 921 legal forms (e.g. ), and 40 active business partner data sources (e.g. Data source US-IO.BER, 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.