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)BP Lookup response extended with a new attribute "goldenRecordUse" (20 October 2021)New attribute has been added to Business Partner Lookup service (https://www.apimatic.io/apidocs/referencedata/v/3_0#/rest/api-endpoints/business-partners/lookup-business-partners): "goldenRecordUse" informs which records were used when creating a Golden Record. The new information can have either:
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. Data 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
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 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. | ||||||||||||||||||||||
