Data Sharing Community

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.   CDQ data model       Support Metadata and Standards · Data Quality Rules · Data Sources  · Procedures

What's new? (RSS) Improved matching with transliteration support in Business Partner Lookup (4 June 2025) Overview We have enhanced the Business Partner Lookup service by introducing support for transliteration-based matching. This new feature improves accuracy when matching business partner names, cities, or streets written in different alphabets — such as Latin and Cyrillic — by applying a transliteration step before similarity scoring is calculated. Details When the feature toggle ENABLE_TRANSLITERATION_MATCHING is enabled, the lookup service will: Automatically detect if the input and reference data use different alphabets. Apply transliteration to normalize both values into a common character set. Calculate the matching score based on the transliterated (normalized) form. Example Use Case Input name: STRIMON SPA AD (Latin alphabet) Reference name: СТРИМОН СПА - АД (Cyrillic alphabet) → Now correctly matched with a high score thanks to transliteration. Availability This feature is available behind a feature toggle (ENABLE_TRANSLITERATION_MATCHING) and can be activated upon request. New Augmentation Monitoring View in Data Clinic App (30 May 2025) We’re excited to introduce a brand-new Augmentation Monitoring view in the Data Clinic APP. Built on the foundation of the Business Partner Update Browser, it preserves nearly every element you know and love while giving you full visibility into how each record was enriched. With this view, you can: Inspect augmentation results side-by-side with the original “before” values from your data mirror Leverage the Update Assessment panel to trace data provenance and see exactly which rules were applied Identify the action taken on each field (added, modified, or deleted) Review how records were classified under our ruleset Compare similarity scores for addresses and names To get started, simply create one or more augmentation monitors and configure your curation settings to match your needs. Expanded coverage of US.SEC data source in Business Partner Lookup (28 May 2025) Summary We have significantly improved our integration with the United States Securities and Exchange Commission (US.SEC) data source. As a result, CDQ’s Business Partner Lookup service now provides access to over 900,000 company records collected from the EDGAR (Electronic Data Gathering, Analysis, and Retrieval) system. Details Previously, only a fraction of available filings was accessible. With this enhancement, our customers can now search and retrieve comprehensive business partner data for virtually all companies registered in the EDGAR database. This includes detailed information on U.S.-based legal entities such as corporations, limited liability companies, and other registered businesses. Each record includes detailed attributes such as: Company name and historical names Registered addresses (business and mailing) CIK (Central Index Key) and EIN (Employer Identification Number) identifiers Legal form SIC code and industry description Phone number and website The data is updated daily, ensuring high data freshness and reliability. This improvement boosts the value of the US.SEC source for due diligence, enrichment, and compliance use cases. ... further results 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. 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 CH (Schweiz, Switzerland, Suisse, Svizzera, Svizra, 瑞士), DE (Deutschland, Germany, Allemagne, 德国), PL (Polen, Poland, Pologne, Polska, 波兰), US (Vereinigte Staaten, United States of America, États-Unis d'Amérique, 美国), or select another country from the list.

Data sources Active data sources Records Data source BR.RF 65,819,242 Data source CDQ.INTEL 53,424,836 Data source VIES 50,000,000 Data source FR.RC 41,615,326 Data source US-CA.BER 8,806,428 Data source GB-EAW.CR 8,798,094 Data source US-FL.BER 6,305,570 Data source JP.CR 5,635,164 ... 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. WORLD (World), AT (Österreich, Austria, Autriche, 奥地利), BE (Belgien, Belgium, Belgique, België, 比利时)), 993 legal forms (e.g. ), and 72 active business partner data sources (e.g. Data source CDQ.POOL, Data source VIES, Data source CH.UIDR). Metadata and Standards: Metadata-driven Data Quality Data quality plays a pivotal role in ensuring compliance with legal, regulatory, and industry standards. One of the core challenges in achieving high data quality is adhering to dynamic data requirements that evolve due to changes in national regulations. These requirements vary by country, making it essential for businesses to track and update compliance criteria continuously. In many countries, official company information is available as Open Data, but the lack of a standardized data model or provision method complicates the process of integrating this data. The Data Sharing Community actively collaborates to identify global data requirements and reference data sources, whether Open Data or commercial. Metadata Short description Administrative area metadata Managed reference data for administrative areas with language-specific terms and short names according to ISO 3166-2. Bank account metadata Managed reference data for bank accounts worldwide. Business identifier metadata Managed reference data for types of identifiers per country. CDQ Data Model Basic data concepts of CDQ Cloud Services. Compliance lists metadata Managed reference data about compliance lists considered in the sanction and watchlist screening services Country metadata Managed reference data for countries with language-specific names and short names according to ISO 3166-2. Data Quality Rules Documentation of data quality rules with explanation and technical constraints to validate business partner data records. Data quality rules function library Data quality rule functions are methods implemented in a programming language for being used in data quality rule implementations. They can be e.g. used in custom data quality rules similar to functions employed by business users in popular spreadsheet applications such as Microsoft Excel. Legal form metadata Managed reference data for legal forms with official and commonly used abbreviations and corresponding country. Locality metadata Managed reference data for localities, such as exonyms. Post code metadata Managed reference data for post codes Postal delivery point metadata Managed reference data for postal delivery points, such as Post Office Boxes used for identification, extraction, harmonization and standardization. Registration authority metadata Managed reference data for issuing bodies of identifiers Street metadata Managed reference data for thoroughfares of type Street (CDQ.POOL) used for harmonization and standardization. Data Quality Rules 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. All data quality rules are executed behind 1 interface, in real-time. Batch jobs and single-record checks use the same rule set and can be integrated by APIs. For proving that a data quality rule is content-wise correct we maintain supporting document(s) per data quality rule which share the rule's source. This could be: a public authority source any other trustful webpage a data standard of a specific community member We manage the URL (if any), a screenshot of the relevant parts (if any) and the source's name (e.g. Community member data standard, European Commission, National ....) See Identifier format invalid (SIREN (France)) as an exemplary rule that was specified and implemented based on information provided by the OECD.