Independent and objective
We are not reselling a platform. Advice is aligned to your outcomes, not a vendor roadmap.
Services
Defining and delivering the data and information foundations the enterprise depends on.
From data and information strategy through to architecture, governance, and responsible AI — engaged independently or as a coordinated program across complex environments.
20+
Advisory engagements
7+
Strategy-to-execution programs
$400M+
Transformation program experience (cumulative)
Capability areas
Nine services. One coherent data agenda.
Expand any service for the client problems we address, what we do, typical deliverables, and the business outcomes.
Set direction for data and information as managed assets — strategy, maturity, operating model, and a roadmap that survives contact with delivery.
Common client problems
- Data and information investment is reactive and disconnected from business strategy.
- There is no operating model for how data and information are owned and managed.
- Executives cannot see where data risk and value sit across the portfolio.
- Maturity is assumed rather than assessed.
What DataVirtue does
- Assess current data and information maturity against a structured, evidence-based baseline.
- Define a target operating model with clear roles, forums, and decision rights.
- Build a sequenced roadmap tied to business priorities and program milestones.
- Frame investment cases and brief executives in language the board understands.
Typical deliverables
- Data and information maturity assessment
- Data and information strategy
- Target operating model
- Governance and delivery roadmap
- Executive briefing pack
- Prioritised investment case
- AI and data roadmap
Business outcomes
- A shared, prioritised view of where to invest first.
- Data risk and value made visible to executives.
- A roadmap that connects strategy to architecture, governance, and delivery.
Related servicesDiscuss this service
Connect business capability to governed information assets through conceptual, logical, common, and canonical models that integration and governance can rely on.
Common client problems
- Data models reflect systems and vendors, not the business.
- Integration design is point-to-point and brittle.
- The same concept is defined differently in every system.
- Business capabilities and data domains are not connected.
What DataVirtue does
- Define target-state architecture, a baseline assessment, and an architecture roadmap.
- Map business capabilities to data domains and subject areas.
- Develop conceptual, logical, common, and canonical models that are technology-neutral.
- Establish common data definitions and entity catalogues teams can adopt.
Typical deliverables
- Target-state information architecture
- Data domain and subject area model
- Conceptual and logical data models
- Common / canonical data model
- Data entity catalogue
- Business capability to data mapping
- Common data definitions and glossary
Business outcomes
- Integration designed once and reused, rather than rebuilt per system.
- A shared language for data across business and technology.
- Architecture that outlasts any single platform decision.
Related servicesDiscuss this service
Establish ownership, stewardship, policies, and controls using DAMA-aligned practices, and embed them in a data office designed to be practical and sustainable.
Common client problems
- Governance exists on paper but not in delivery.
- Ownership and stewardship of critical data are unclear.
- There is no data office or operating rhythm to sustain governance.
- Metadata, lineage, and master data are inconsistent or undocumented.
What DataVirtue does
- Establish and mature a data governance function and data office.
- Define data ownership, stewardship, forums, and decision rights.
- Set data policies, standards, and quality expectations that can be enforced.
- Establish master data, metadata, lineage, and data lifecycle approaches.
Typical deliverables
- Data governance framework
- Data office operating model
- Ownership and stewardship model
- Governance forums and decision rights
- Data policies and standards
- Master data and metadata approach
- Data management implementation roadmap
Business outcomes
- Clear accountability for critical data assets.
- Governance embedded into delivery, not bolted on afterwards.
- A defensible, auditable basis for data decisions.
Related servicesDiscuss this service
Enterprise information management done properly — policy and standards, content and records architecture, taxonomy and ontology, and classification that improves trust and compliance.
Common client problems
- Critical information is hard to find, classify, and trust.
- Legacy content and records systems no longer meet the need.
- Classification and information governance are inconsistent.
- Information lifecycle and compliance obligations are unclear.
What DataVirtue does
- Review and refresh enterprise information management (EIM) policies and standards.
- Design content and records management architecture and a roadmap.
- Develop taxonomy and ontology models and classification schemes.
- Define an EIM operating model, service catalogue, and policy map.
Typical deliverables
- EIM policies and standards
- EIM policy map
- Content and records management architecture and roadmap
- Taxonomy and ontology model
- Classification scheme
- EIM service catalogue and operating model
- Information lifecycle approach
Business outcomes
- Information that is findable, classified, and governed.
- Records and content that meet compliance obligations.
- Reduced duplication and clearer ownership of information.
Related servicesDiscuss this service
Move from anecdotal quality concerns to measured rules, controls, and remediation that the organisation can own and sustain.
Common client problems
- Data quality issues are felt everywhere but measured nowhere.
- No one owns quality for critical data.
- Controls are manual, inconsistent, and hard to audit.
- Remediation is ad hoc and never finishes.
What DataVirtue does
- Profile and assess data quality against defined dimensions.
- Define a quality rules catalogue and exception model.
- Design controls and dashboards that make quality visible.
- Build a prioritised remediation roadmap with clear ownership.
Typical deliverables
- Data quality assessment
- Data quality rules catalogue
- Data profiling summary
- Issue and exception model
- Data controls dashboard
- Data remediation roadmap
- Data quality ownership model
- Auditability and traceability approach
Business outcomes
- Quality measured, trended, and owned — not just complained about.
- Controls that are auditable and repeatable.
- A remediation plan with a defined finish line.
Related servicesDiscuss this service
Rationalise reporting, define trusted metrics, and design a semantic layer and operating model so analytics supports decisions, not debate — including self-service and shared platforms.
Common client problems
- Reports disagree and trust in the numbers is low.
- No one owns the definitions behind key metrics.
- Report sprawl makes maintenance and trust impossible.
- Analytics is delivered as outputs, not decision support.
What DataVirtue does
- Rationalise the reporting estate and retire duplication.
- Define KPIs, metrics, and a trusted-metrics framework with clear ownership.
- Advise on semantic models, self-service, and an analytics operating model.
- Design executive dashboards focused on decisions, not vanity metrics.
Typical deliverables
- Reporting rationalisation
- KPI and metric definitions
- Semantic model advice
- BI and analytics roadmap
- Executive dashboard design
- Trusted metrics framework
- Analytics operating model
Business outcomes
- One agreed definition for each metric that matters.
- Fewer, better reports that people actually trust.
- Analytics aligned to how decisions are made.
Related servicesDiscuss this service
Vendor-neutral advisory on modern data platforms — warehouse, lakehouse, and integration patterns — grounded in governance and operating reality.
Common client problems
- Platform decisions are vendor-led rather than capability-led.
- Cloud and hybrid data costs and security are underestimated.
- Integration patterns are inconsistent across the estate.
- There is no operating model for the platform once it lands.
What DataVirtue does
- Assess platform capability needs against business priorities.
- Advise on cloud and hybrid data architecture, warehouse, and lakehouse direction.
- Run independent, structured tool and platform selection (market scan to selection).
- Frame cost, security, governance, and delivery considerations for decision-makers.
Typical deliverables
- Modern data platform roadmap
- Cloud and hybrid data architecture advisory
- Data warehouse or lakehouse advisory
- Integration pattern recommendations
- Cloud data governance model
- Platform capability assessment
- Independent tool selection (market scan, evaluation, recommendation)
- Cost and delivery roadmap
Business outcomes
- A platform direction tied to capability, not hype.
- Cost and security understood before commitment.
- An operating model so the platform is run, not just built.
Related servicesDiscuss this service
Assess AI readiness, prioritise use cases, and put responsible-AI controls and data foundations in place so AI ambition is matched by data trust.
Common client problems
- AI ambition is ahead of data readiness.
- Use cases are pursued without prioritisation or controls.
- There is no governance lens on AI.
- It is unclear which AI use cases are feasible and valuable.
What DataVirtue does
- Assess AI readiness and review the data and information foundations beneath it.
- Prioritise AI use cases by value and feasibility.
- Recommend responsible-AI controls and governance considerations.
- Develop an AI strategy and roadmap aligned to business capability.
Typical deliverables
- AI readiness assessment
- Data foundation review
- Responsible AI controls
- AI use-case prioritisation
- AI strategy and roadmap
- Data governance for AI recommendations
Business outcomes
- A clear-eyed view of what AI your data can actually support.
- Use cases sequenced by value, not novelty.
- Responsible-AI controls in place before scale.
Related servicesDiscuss this service
Domain-driven data architecture for sector-specific needs — from operations and ERP to person, asset, and master data.
Common client problems
- Industry knowledge lives in people, not in models.
- Master and reference data is fragmented across systems.
- Integration is rebuilt for every domain from scratch.
- Capability and data domains are not aligned.
What DataVirtue does
- Map sector capabilities to data domains and subject areas.
- Build industry-aligned canonical and operational data models.
- Define master and reference data structures and a domain glossary.
- Design integration concept models for reuse across the domain.
Typical deliverables
- Data domain map
- Subject area model
- Industry-aligned canonical model
- Capability-to-data mapping
- Operational data model
- Integration concept model
- Master / reference data model
- Domain glossary
Business outcomes
- Domain knowledge captured as reusable, governed assets.
- Master data structured for integration and governance.
- Faster, more consistent delivery within the domain.
Related servicesDiscuss this service
How we deliver
A delivery model designed for high-stakes change.
Strategy connected to delivery
Every artefact is designed to be used in delivery — not to sit in a strategy deck.
Capability that stays
We work alongside your teams so governance and architecture outlast the engagement.
The path through an engagement
- 01Understand the business context
- 02Assess data and information maturity
- 03Define target foundations
- 04Design governance and architecture
- 05Embed controls into delivery
- 06Embed capability and improvement