One approach, from priorities to practice
Use the strategy to set direction, the four foundations to organise the work and the governance dimensions to connect outcomes with the capabilities needed to deliver them.
Five strategic pillars
The priorities: connected data, shared language, quality, transparency and data culture.
Explore topicGovernance dimensions
The outcomes and enabling capabilities that turn the strategy into practice.
Explore topicFour fundamentals
The areas of work: governance, quality, operations and protection.
Explore topicOne Data Vision
DGU connects business goals with the people, processes and technology that manage data through ONE Connected Data and a shared Data Journey.
See the One Data vision
Vision layer / Data vision
To become a data-driven organisation, treat connected data as a strategic asset and manage it responsibly across products, markets and domains.
Strategic layer / Data principles
Reliable, secure and responsibly managed throughout the lifecycle
Discoverable, understandable and reusable across the organisation
Accelerates decisions, product development, AI and analytics
Creates value while preserving the trust of customers, employees and partners
Tactical layer / Delivery enablers
One Data Journey connects business needs to data use and disposal, with shared ownership and controls.
Data Strategy Overview
Address fragmentation through strategic changes that enable scalable, trusted data and analytics.
Data vision
To become a data-driven organisation, treat connected data as a strategic asset and manage it responsibly across products, markets and domains.
Current challenge
Fragmented data landscape
Unified data platform architecture
Integrated data platforms and AI-enabled capabilities
Current challenge
Unclear ownership and governance
Data governance and ownership model
Governance Board and appointed Data Domain Owners
Current challenge
Inconsistent service and process delivery
Service and process model
Clear service roles and repeatable processes across domains
Current challenge
Limited reuse of data assets
Data product framework
A standard framework for reusable data products
Current challenge
Low visibility and trust in data
Data transparency and quality
Standard processes for visible, trustworthy data
Current challenge
Manual data sharing and compliance
Privacy and data usage controls
Defined privacy, sharing and data-use controls
Strategic Data Pillars
The five pillars express data strategy priorities. Four data management foundations organise the work needed to deliver them.
Link assets and business context together
Agree meaning and definitions
Make data fit for purpose
See dependencies and impacts
Build ownership and daily habits
Five strategic data pillars, supported by four data management foundations
Data Governance Dimensions
Connect business goals, governance outcomes and the capabilities that enable the Data Journey.
Explore governance dimensions
Governance dimensions and core capabilities
- Data documentation & cataloguingShow example — Data documentation & cataloguing
- Data LineageShow example — Data Lineage
- Data sourcingShow example — Data sourcing
- Data platformData Platforms · Work in progress
- Data productsData Products · Work in progress
- Data quality managementShow example — Data quality management
- Master data managementMaster Data Management (MDM) · Work in progress
- Data Privacy & SecurityShow example — Data Privacy & Security
- Regulatory compliance managementShow example — Regulatory compliance management
Foundational dimensions
Our four fundamentals
Data governance, data quality, data operations and data protection are the four fundamentals of the DGU approach.
Who decides?
Agree meaning, ownership, decision rights and shared rules.
Catalogue and metadata
Can we trust it?
Define fitness for purpose, check data and resolve root causes.
Quality and observability
How does it work?
Model, connect, transform and trace information across systems.
Architecture, integration and lineage
Can we use it responsibly?
Apply classification, privacy, access, retention and disposal requirements.
Classification and retention