Data Sharing Community

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CDQ AML Guard now supports dedicated user roles to ensure secure and compliant access to sensitive compliance-related data and functionality.

What Changed

To better protect sensitive information and align with compliance best practices, we have introduced role-based access control in the CDQ AML Guard. This update is designed to ensure that users can only access the functions and data they are authorized to see or act upon.

With this release, access to the AML Guard inside CDQ Cloud Apps is now limited to users who have been explicitly assigned one of three new AML roles.

The Solution

Three roles are now available to support different responsibilities within the AML process:

New Roles in AML Guard:

   AML Guard Viewer
   – View-only access to all AML Guard information
   – Cannot make decisions or modify configurations

   AML Guard User
   – Can view data and take decisions (e.g., classify hits, close cases)
   – Cannot create or modify configurations

   AML Guard Manager
   – Full access: view data, take decisions, and manage all configurations

These roles allow customers to align access levels with internal responsibilities, ensuring only those with a legitimate need can access sensitive screening results and case data.

Why It Matters

Compliance processes frequently involve the handling of sensitive, business-critical information. With this change:

   We enforce the need-to-know-principle: only authorized users access sensitive compliance data

   You can control who can view, decide, or administer AML Guard configurations

   Organizations gain greater security, transparency, and control over their compliance operations

   This supports internal segregation of duties and meets audit and regulatory expectations

What You Need to Do

To ensure continued access to the AML Guard module, please:

   Review your user list in CDQ Cloud Apps

   Assign the appropriate role(s) to each user

   Ensure that users handling compliance cases or administering screening logic have the correct level of access

Users without one of the new AML roles will no longer be able to view or access the AML Guard features.

For assistance or guidance on role assignment, please contact your CDQ representative.

Simplified Data Transformation After Mapping Changes (8 July 2025)

You no longer need to use the API to transform data when there's a change in assigned mapping. Now, you can perform data transformation directly in the APPs interface.

To apply transformations:

Navigate to Data Mirror Management.
Select your data source.
Click on Start Transformation.
Choose the country scope (optional).
- If no country is selected, the system will transform data for all BPs in the mirror where the mapping change is relevant.

This update streamlines your workflow and reduces dependency on API calls.

Monitor Status for Business Partners (27 June 2025)

We’re excited to introduce a new Status feature in the Monitors module of the Data Clinic app. This enhancement provides visibility into the current state of Business Partners (BPs) within Augmentation and Data Quality Profiling monitors, helping users make informed decisions when downloading or reviewing BP data.

Key Benefits:

Avoid consuming incomplete data by easily identifying BPs that are mid-reprocessing.
Monitor BP readiness and take action only when data is up to date.
Streamline workflows by accessing real-time status data directly in the UI or via API.

Status Overview:

Each BP in a monitor is now assigned one of four statuses:

Ready – The BP has up-to-date results and is ready for download.
In Progress – The BP is currently undergoing processing or reprocessing.
Retry – The BP requires processing has failed and needs to be reprocessed.
Blocked – The BP cannot be processed due to an issue that needs to be resolved.

Where to See It:

Configuration Page: Get an overview of how many BPs are in each status category across your monitor.
Data Review (Single Mode): Check the individual status of a selected BP during manual review.
API Access: Status information is also available programmatically via our API responses.

This feature aims to improve data accuracy, transparency, and efficiency when working with monitored BPs.

... 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.

This 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.

Data sources

Active data sources	Records
Data source BR.RF	66,349,272
Data source CDQ.INTEL	53,424,836
Data source VIES	50,000,000
Data source FR.RC	41,793,664
Data source GB-EAW.CR	8,868,373
Data source US-CA.BER	8,842,285
Data source US-FL.BER	6,359,547
Data source JP.CR	5,648,332
... 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.