Data Sharing Community
Welcome to the Portal of the CDQ Data Sharing CommunityThe CDQ Data Sharing Community is a trusted network of user companies to manage business partner data collaboratively. | |||||||||||||||||||||||
What's new? (RSS)Matching configuration snapshot added to the duplicate analysis reports (4 May 2021)While experimenting with various matching configuration settings it is useful to have the matching configuration dumped to the report header. The configuration is now available in the metadata tab of the matching and consolidation reports. In future releases it will be presented not just as a configuration dump but per attribute (which comparators, cleaners and similarity scores are applied). Extended tax and business identifier checks (22 April 2021)The identifier data quality checks have been extended by additional data quality rules. We now enable checking the format of D&B's DUNS, the existence of the Norwegian Organisation Number and the Canadian Corporation Number Creating data mirrors - self service for new workspaces enabled (21 April 2021)The Global Settings does now not only allow for creating workspaces, but also to directly create a data mirror for the new workspace. During workspace setup the data mirror can be created and a name assigned to it. Additionally it is possible to activate a weekly summary mail on monitoring jobs that run on this mirror. ... further resultsData SharingBusiness 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 modelAn 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.  Data maintenance proceduresA 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 APIFrom 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.
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Capabilities of CDQ Cloud ServicesData sources
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 protectionCompanies 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. | ||||||||||||||||||||||
