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 a practical data and information agenda aligned to business priorities, regulatory expectations, operating-model reality, and future digital and AI ambition — covering strategy, maturity, operating model, roadmap, and investment priorities.
Common client problems
- Data activity is fragmented across programs and teams, with no single agenda.
- Business priorities are not translated into a clear data and information direction.
- Data investment lacks prioritisation and a defensible business case.
- Governance, analytics, AI, and architecture initiatives are disconnected from each other.
- Executives need a practical roadmap, not a theoretical strategy document.
What DataVirtue does
- Assess 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 and prioritised initiative portfolio tied to business priorities.
- Frame investment priorities and brief executives with an agenda they can act on.
Typical deliverables
- Data and information strategy
- Data maturity assessment
- Target state and roadmap
- Data operating model
- Capability uplift plan
- Prioritised data initiative portfolio
- Executive briefing pack
Business outcomes
- A shared, prioritised view of where to invest first.
- Data and information work connected to business strategy and to each other.
- A roadmap executives can fund and delivery teams can act on.
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Define system-agnostic data domains, models, and standards — conceptual, logical, and canonical — so integration, reporting, analytics, AI, and operational change build on consistent, reusable foundations.
Common client problems
- Data architecture is too system-specific, so the same data is defined differently in each platform.
- Integration and reporting are built on inconsistent, point-to-point structures.
- Projects lack reusable data design patterns and repeat the same work.
- Business capabilities and data domains are not aligned.
What DataVirtue does
- Establish enterprise data architecture principles and a data domain and subject area model.
- Develop conceptual, logical, and canonical models that are technology-neutral.
- Define master and reference data patterns and integration data patterns for reuse.
- Set data architecture standards and lightweight architecture governance.
Typical deliverables
- Enterprise data architecture principles
- Data domain model
- Subject area model
- Conceptual, logical & canonical models
- Master / reference data patterns
- Integration data pattern recommendations
- Data architecture standards
Business outcomes
- Integration and reporting designed once and reused, not rebuilt per system.
- A shared, consistent definition of data across business and technology.
- Architecture that outlasts any single platform decision.
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Rationalise reporting, define trusted KPIs and a semantic layer, and design an analytics operating model so business intelligence and advanced analytics support decisions instead of debate.
Common client problems
- Reports conflict across teams and trust in the numbers is low.
- KPIs are poorly defined or inconsistently calculated.
- Dashboards exist but are not trusted or owned.
- Analytics teams spend too much time reconciling data instead of generating insight.
- Business leaders cannot rely on a single version of key metrics.
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, dashboards, and advanced analytics use cases.
- Design an analytics operating model that connects analytics to decisions.
Typical deliverables
- BI and analytics roadmap
- Reporting rationalisation assessment
- KPI and metric catalogue
- Semantic model recommendations
- Dashboard design guidance
- Analytics operating model
- Trusted reporting framework
Business outcomes
- One agreed definition for each metric that matters.
- Fewer, better reports that leaders actually trust.
- Analytics aligned to how decisions are made.
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Bring structure to information assets — information architecture, taxonomy and metadata, records and content classification, ownership, and lifecycle — so information is findable, usable, and governed.
Common client problems
- Critical information assets are poorly understood and hard to find.
- Records and documents are difficult to classify, retain, and dispose of consistently.
- Data and records management are disconnected.
- Content, metadata, and taxonomy are inconsistent across systems.
What DataVirtue does
- Catalogue information assets and define an information architecture model.
- Develop taxonomy, metadata, and classification models that work across systems.
- Define records, retention, and information lifecycle management approaches.
- Establish information ownership and align it with information governance.
Typical deliverables
- Information asset catalogue
- Information architecture model
- Taxonomy and classification model
- Metadata model
- Records and lifecycle recommendations
- Information ownership model
- Information management roadmap
Business outcomes
- Information that is findable, classified, and trusted.
- Records and content that meet retention and compliance obligations.
- Reduced duplication and clearer ownership of information.
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Find and frame AI, ML, and data science opportunities by value and feasibility — from use-case identification and data requirements through to model lifecycle and delivery advisory.
Common client problems
- AI ideas are not clearly tied to business value.
- Use cases are selected before data readiness is understood.
- Data science work stays experimental and disconnected from operations.
- Model outputs are difficult to explain or operationalise.
- Business teams do not know which AI and ML opportunities are practical.
What DataVirtue does
- Identify and prioritise AI, ML, and data science use cases by value and feasibility.
- Assess the data and feature requirements each use case depends on.
- Advise on model lifecycle, proof-of-concept, and operationalisation.
- Shape a delivery roadmap that moves data science from experiment to operations.
Typical deliverables
- AI/ML use case portfolio
- Data science opportunity assessment
- Feasibility and value assessment
- Data requirements for AI/ML
- Model lifecycle considerations
- Proof-of-concept advisory
- AI/ML delivery roadmap
Business outcomes
- AI and ML effort focused on practical, valuable opportunities.
- Use cases matched to the data that can actually support them.
- A path from concept to operational, explainable models.
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Assess AI readiness and establish responsible-AI governance, controls, and data foundations so AI ambition is matched by accountability, explainability, and trust.
Common client problems
- AI adoption is moving faster than governance and data readiness.
- Teams lack clear accountability for AI use.
- Data quality and lineage are not strong enough for AI.
- Policies exist but are not operationalised, and AI risks are not consistently assessed.
- Leaders need a practical readiness view before scaling AI.
What DataVirtue does
- Assess AI readiness across data, governance, controls, and operating model.
- Define a responsible-AI framework with human oversight, explainability, and accountability.
- Recommend AI risk, privacy, security, and ethical control approaches.
- Strengthen the data foundations and operating model AI depends on.
Typical deliverables
- AI readiness assessment
- Responsible AI framework
- AI governance and control model
- AI risk assessment approach
- Data readiness recommendations
- AI policy and standards recommendations
- AI operating model roadmap
Business outcomes
- A practical, clear-eyed view of readiness before scaling AI.
- Responsible-AI controls and accountability in place early.
- Data foundations strong enough to support trustworthy AI.
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Advisory, design, and implementation support for everyday data management — data quality, metadata and cataloguing, master and reference data, issue management, and stewardship workflows — as repeatable solution patterns, not governance theory.
Common client problems
- Data management practices are manual, inconsistent, and hard to repeat.
- Teams lack repeatable workflows for data quality, metadata, and issue management.
- Master and reference data are poorly controlled.
- Data ownership is unclear in day-to-day operations.
- Organisations need practical solution patterns, not just governance theory.
What DataVirtue does
- Assess data management capability and design practical solution patterns.
- Establish data quality, metadata, and cataloguing approaches and workflows.
- Recommend master and reference data management and issue-remediation processes.
- Define stewardship workflows and a pragmatic implementation roadmap.
Typical deliverables
- Data management capability assessment
- Data management solution design
- Metadata and catalogue approach
- Data quality management process
- Master / reference data recommendations
- Data issue and remediation workflow
- Stewardship operating model
- Implementation roadmap
Business outcomes
- Repeatable, lower-effort data management practices.
- Quality, metadata, and master data brought under practical control.
- Clear ownership and workflows embedded in daily operations.
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Stand up data governance that delivery teams actually use — ownership and stewardship, forums and decision rights, policies and standards, quality and metadata governance, and an adoption path that sticks.
Common client problems
- Governance exists on paper but not in day-to-day delivery.
- Ownership and decision rights are unclear.
- Data issues are not escalated or resolved consistently.
- Standards are not adopted across projects and teams.
- Governance is seen as compliance overhead rather than enablement.
What DataVirtue does
- Establish a data governance framework with ownership and stewardship roles.
- Define governance forums, decision rights, policies, and standards that can be enforced.
- Set data quality, metadata, and lineage governance and an issue-escalation model.
- Design a governance operating model and a realistic adoption path.
Typical deliverables
- Data governance framework
- Ownership and stewardship model
- Governance forums and decision rights
- Data policies and standards
- Data quality governance model
- Issue and escalation model
- Governance adoption roadmap
Business outcomes
- Clear accountability for critical data assets.
- Governance embedded into delivery, not bolted on afterwards.
- Standards that are adopted and seen as enablement, not overhead.
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Apply data product thinking to high-value data assets — definitions, ownership, data contracts, quality and metadata expectations, and consumption patterns — so trusted data is reused instead of rebuilt.
Common client problems
- Data assets are created for one project and never reused.
- Consumers cannot tell which data is trusted.
- Data ownership and quality expectations are unclear.
- Analytics and AI teams repeatedly rebuild the same data.
- Enterprise data platforms lack product thinking and consumption clarity.
What DataVirtue does
- Identify and prioritise data product opportunities tied to business value.
- Define data products with ownership, data contracts, and consumption patterns.
- Set quality and metadata expectations and a data product lifecycle.
- Establish a data product operating model so products are sustained.
Typical deliverables
- Data product opportunity assessment
- Data product catalogue
- Data product definition template
- Ownership and lifecycle model
- Data contract recommendations
- Quality and metadata expectations
- Data product operating model
- Prioritised data product roadmap
Business outcomes
- Trusted data assets reused across teams, analytics, and AI.
- Clear ownership, contracts, and quality expectations for key data.
- Less rebuilding of the same data, and faster, more confident consumption.
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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 direction
- 02Assess foundations
- 03Design the framework
- 04Prioritise practical delivery
- 05Apply AI and automation responsibly
- 06Embed and improve