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)Updates on Data Quality Rules (19 September 2024)In our recent data quality rules update we have changed 36 rules into RELEASED status and worked on improvements regarding post codes in Vietnam and Iran. Data quality rules now allow to check old and new post code formats in Vietnam (old format is 6 digits length and the new one is 5 digits length). Moreover, we have improved format checks of post codes in Iran. Additionally, we have added and improved rules for checking Business Registration and Tax Identification Numbers in Nigeria. The list of affected rules is the following: Post code rules:
List of 36 rules which status has been changed to RELEASED:Europe:
Asia-Pacific
United States of America:Africa:
Integration of CZ.VAT - Czech Republic VAT Register (Beta) (10 September 2024)We are excited to announce the integration of a new data source into our system: CZ.VAT - Czech Republic VAT Register, provided by the Czech tax authority General Financial Directorate. Key Features:Business Partner Data:Each business partner record includes:
Input Requirements:To search for data in the CZ.VAT data source, users must provide a valid VAT Identification Number (CZ_DIC) as the input data. Current Limitations (Beta Phase):Daily Query Limit:In this beta phase, the number of searches to the CZ.VAT data source is limited to 2000 queries per day. We are actively working on increasing this limit in the near future as we enhance the service capabilities. Inaugural Release Note: Introducing the CDQ Email Domain Guard (30 August 2024)We are pleased to introduce the Email Domain Guard Headless REST API, a comprehensive suite of email verification and domain analysis services Why We Launched Email Domain Guard The need for secure and reliable email communication has never been more critical. Businesses are facing escalating threats from sophisticated phishing attacks, Business Email Compromise (BEC), and fraudulent invoicing scams. These issues not only undermine trust but also expose organizations to significant financial and legal risks. The growing shift towards remote work has further exacerbated these vulnerabilities, with decentralized operations increasing the likelihood of security breaches. Additionally, stringent data protection regulations, such as GDPR, place immense pressure on companies to secure their communications and ensure compliance. Recognizing these challenges, we developed Email Domain Guard to provide businesses with a comprehensive, cutting-edge solution to safeguard their email communications.
Our Email Domain Guard is a state-of-the-art email verification and domain analysis solution designed to elevate the security, accuracy, and reliability of your business communications. By leveraging multi-factor analysis, Email Domain Guard assesses the risk associated with email addresses, providing clear, actionable insights that empower businesses to make informed decisions and proactively manage risks.
Key features include
The launch of our Email Domain Guard comes at a time when businesses are increasingly vulnerable to cyber threats and regulatory pressures. Our solution addresses these needs head-on, providing the tools necessary to secure your communications in an ever-evolving digital landscape.
With our Email Domain Guard, you’re not just adopting an email verification tool—you’re investing in a comprehensive security solution that will protect your business from the growing threats of email fraud and ensure the integrity of your communications. Explore the capabilities of Email Domain Guard today and take the first step towards more secure, reliable, and compliant email operations. For more details on how to implement Email Domain Guard and integrate it into your existing systems, visit our Developer Portal. Guide that will walk you through the process of using the Email Domain Guard API: How to verify e-mail address? Please refer to the API documentation for detailed information: Email Analysis API ... further resultsData 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. |
Data sources
Metadata and Standards: Metadata-driven Data QualityData 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.
Data Quality RulesTransformation 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:
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. |