Data Sharing Community

From CDQ
Revision as of 10:46, 12 July 2021 by Kaihuener (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

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)

In-App tutorials (23 July 2021)

In-App tutorials have been introduced for currently two apps. Tutorials for all other apps will follow soon. These so called app-tours allow for understanding the basic functionalities and required inputs of the CDQ Cloud Apps.

Additional data quality rules available for testing (23 July 2021)

The data quality rulebook was extended by a number of data quality rules. The rules are currently available in HYPERCARE status and can be tested.

Identification of old post code formats


Checkdigit checks for business identifiers


Format checks for identifiers

Business Partner Lookup enriched by a new data source from the USA (23 July 2021)

Our BP Lookup service has been connected to a new data source - Internal Revenue Service, which offers data of over 1,7 million of organizations from the United States of America.

... 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/statusBusiness partner/identifierFraud case/fraudsterBusiness 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.

CDQ API

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.)
  • 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.)
  • 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.RC31,387,469
Data source DE.RC6,108,555
Data source GB-EAW.CR5,453,725
Data source JP.CR5,018,138
Data source US-FL.BER3,838,903
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. AT (Österreich, Austria, Autriche, 奥地利), BE (Belgien, Belgium, Belgique, België, 比利时), DE (Deutschland, Germany, Allemagne, 德国)), 949 legal forms (e.g. ), and 40 active business partner data sources (e.g. Data source EE.CR, Data source DNB, 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.