Data Governance Bodies
Show example — Data Governance BodiesConnect strategic direction, tactical coordination and daily execution. Resolve decisions at the lowest level with the authority and capability to act.
Board Members
Highest escalation point
Decide matters beyond the mandate of the Data Governance Board.
Data Governance Board
Strategic Data Owners
Approve policies, standards and priorities. Resolve cross-domain conflicts, ownership gaps and material exceptions.
Data Governance Forum
Tactical Data Owners
Coordinate definitions, standards, quality expectations and role coverage. Prepare roadmap proposals and Board decisions.
Data Working Groups
Data Stewards, Asset Owners and operational contributors
Maintain metadata, monitor quality and coordinate implementation.
Direction flows down. Evidence, business impact and decision requests escalate up.
Data Strategy, Policy & Standards
Show example — Data Strategy, Policy & StandardsConnect business outcomes to shared requirements and practical daily work.
Data Strategy
Vision, choices, priorities and intended outcomes.
Data Management Policy
Accountability and requirements across the data lifecycle.
Data Management Standards
Consistent requirements, controls and measures.
Procedures & Guidance
Activities, templates and evidence for daily work.
12 standard groups across four foundations
Browse the standard groups by foundation.
Data Governance 3 groups
- DG-01 Data Governance & Ownership ManagementShow example — Data Governance & Ownership Management
- DG-02 Business Definitions & Data Language ManagementShow example — Business Definitions & Data Language Management
- DG-03 Business Processes & Data Services ManagementShow example — Business Processes & Data Services Management
Data Quality 1 groups
- DQ-01 Data Quality & Observability ManagementShow example — Data Quality & Observability Management
Data Operations 6 groups
- DO-01 Data Platforms & BI ManagementData Platforms · Work in progress
- DO-02 Data Operations ManagementShow example — Data Operations Management
- DO-03 Data Architecture ManagementShow example — Data Architecture Management
- DO-04 Data Lineage & Impact ManagementShow example — Data Lineage & Impact Management
- DO-05 Data Products & Business Insights ManagementData Products · Work in progress
- DO-06 AI & Data-Driven Decision Making Management
Data Protection 2 groups
- DP-01 Data Lifecycle ManagementShow example — Data Lifecycle Management
- DP-02 Data Protection & Security ManagementShow example — Data Protection & Security Management
Data Journey
Show example — Data JourneyStart with the business process. Keep meaning, accountability and controls visible as data is created, moved, used and retired.
Data Journey: Source to Target
Show example — Data Journey: Source to TargetA simple view of how data moves from operational sources to business use, with controls throughout the Data Journey.
Set the business context
01 Business Processes
Define the business activity and data needs.
02 Data Dictionary
Agree business terms and definitions.
03 Data Model
Describe entities and relationships.
Move data from source to target
04 Source
Create & modify data in operational systems
05 Analyse & Profile
Identify quality issues and resolve them
06 Ingest
Bring data into the data platform
07 Harmonise
Align and transform for consistent use
08 Target / Consume
Use data in reports, operations, analytics & AI
09 Retain & Dispose
Keep data as required; then archive or delete it.
Ownership, catalogue and controls support the whole journey.
View all nine journey steps
Business Processes
Understand the process and information requirements.
Data Dictionary
Agree business terms and definitions.
Data Model
Define entities, relationships and structures.
Create & Modify
Capture and update data at the source.
Analyse & Profile
Identify quality patterns, issues and gaps.
Ingest
Bring data into the required platforms.
Harmonise
Align, validate and transform for consistent use.
Consume
Use data in operations, reporting, analytics and AI.
Retain & Dispose
Archive or delete under approved requirements.
Data Ownership
Show example — Data OwnershipAssign business accountability, decision rights and daily responsibilities for data.
Business Domain
Strategic Data Owner
Owns domain direction, business priorities and resources. Resolves decisions requiring strategic authority.
Subdomain or agreed local scope
Tactical Data Owner
Turns direction into agreed requirements and actions. Oversees standards and quality expectations.
Data entities and assets
Data Steward
Maintains definitions and metadata, monitors quality and coordinates issue resolution.
Explore supporting roles
Data Maintainer
Keeps source data current and correct. Applies authorised changes and fixes problems at their source.
Data Customer (consumer)
Uses data for an approved purpose, understands quality and use conditions, and reports suspected issues.
Data Asset Owner
Accountable for a dataset, business term, report, KPI or other asset's meaning and suitability within delegated authority.
Each role has a defined scope and authority. A person may hold several roles through explicit appointment.
Regulations & Standards
Regulations and principles that inform the DGU approach to data governance, accountability and controls.
GDPR · Personal data
EU regulation protecting personal data and individuals’ rights, with rules for how organisations process and safeguard personal information.
Read on WikipediaBCBS 239 · Risk data
Basel Committee principles for banks’ risk data aggregation and reporting, supported by strong governance, data quality and architecture.
Read on WikipediaDORA · Digital resilience
EU regulation strengthening digital resilience in financial services through ICT risk management, incident reporting, testing and third-party oversight.
Read on WikipediaSOX · Financial reporting
US legislation improving corporate reporting and accountability, including executive certifications and internal controls over financial reporting.
Read on WikipediaAI Act · Responsible AI
EU regulation applying a risk-based approach to AI, with requirements supporting safety, transparency, trustworthy data and human oversight.
Read on WikipediaNIS2 · Cybersecurity
EU directive strengthening cybersecurity in critical sectors through risk management, incident reporting and management accountability.
Read on WikipediaRegulatory perspectives
ECB perspective
A perspective on the relationship between new technology, underlying data and legacy systems.
Read the context
The supplied screenshot attributes this quotation to Claudia Buch, Chair of the ECB Supervisory Board, on 22 September 2026: “New technologies cannot compensate for poor underlying data or fragmented legacy systems.”
BCBS 239: data lineage
An annotated interpretation connecting attribute-level lineage, data transformations and risk data quality.
Read the context
The supplied screenshot discusses section 3.4 of the ECB’s May 2024 Guide on effective risk data aggregation and risk reporting. Its annotations explain end-to-end lineage at column level, transformation logic and data timeliness, and include a Solidatus connector example.