Consulting services
The data foundations the enterprise depends on.
Six connected capabilities — from data strategy and enterprise architecture through to engineering, analytics, AI and governance. Engaged independently, or as a coordinated program across complex environments.
- Strategy
- Architecture
- Analytics
- Engineering
- AI
- Governance
Three connected disciplines around a trusted data and information foundation. Select one to explore.
Capabilities
Explore the capability architecture.
Six capability areas, engaged independently or together. Strategy, architecture and governance set the direction; engineering turns it into working analytics, AI and automation. Select a capability to see the problems it addresses, the core areas of work, and the outcomes it enables.
Define a clear, prioritised and fundable enterprise data and AI agenda aligned to business strategy, transformation priorities and measurable outcomes — covering maturity, operating model, roadmap, investment priorities and where AI genuinely belongs.
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, architecture and engineering initiatives are disconnected from each other.
- AI ambition is set before anyone has established what the data foundations can support.
- 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.
- Prioritise AI and automation ambition against data readiness, value and feasibility.
- 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
- AI strategy and prioritisation
- Capability uplift plan
- Prioritised data initiative portfolio
- Executive briefing pack
Business outcomes
- A shared, prioritised view of where to invest first.
- Data, analytics and AI work connected to business strategy and to each other.
- A roadmap executives can fund and delivery teams can act on.
Design enterprise data and information structures, platform and integration patterns, and architectural standards that provide a trusted foundation for operations, analytics, AI and transformation — system-agnostic, reusable and built to outlast any single platform decision.
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.
- Critical information assets are poorly understood, hard to find and inconsistently classified.
- Projects lack reusable data design patterns and repeat the same work.
- Platform modernisation proceeds without an architecture that makes data reusable.
- 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.
- Design information architecture, taxonomy and metadata models that work across systems.
- Define master and reference data, integration and data product architecture patterns for reuse.
- Shape data platform and lakehouse architecture so it supports analytics and AI workloads.
- Set data architecture standards and lightweight architecture governance.
Typical deliverables
- Enterprise data architecture principles
- Data domain and subject area model
- Conceptual, logical & canonical models
- Information architecture and taxonomy model
- Master / reference data patterns
- Integration data pattern recommendations
- Data platform architecture
- Data product architecture patterns
- 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 supports analytics and AI without being rebuilt for each initiative.
Turn fragmented reporting and inconsistent metrics into trusted business intelligence, semantic models, advanced analytics and decision-support capabilities — so leaders debate the decision rather than the numbers.
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.
- Advanced analytics stays experimental and never informs an actual decision.
- 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.
- Design semantic models and dashboards that make trusted data easy to consume.
- Identify and frame advanced analytics and decision-intelligence use cases by value and feasibility.
- 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 design
- Dashboard design guidance
- Advanced analytics use case portfolio
- 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 actually made.
Engineer modern data platforms, pipelines, analytics workloads, AI solutions, automation and intelligent systems that turn trusted enterprise data into scalable operational capability — from ingestion through to production, with oversight and control designed in.
Common client problems
- Data pipelines are fragile, manual and expensive to change.
- Platform and lakehouse builds deliver storage but not usable, governed data.
- AI and machine learning work stays as proofs of concept and never reaches production.
- Generative and agentic AI is experimented with, but not engineered to a standard the enterprise can rely on.
- Repeatable data engineering effort consumes capacity that should go to new capability.
- Solutions are built without the monitoring, lineage and controls production requires.
What DataVirtue does
- Engineer ingestion, integration and transformation pipelines on modern cloud and lakehouse platforms.
- Build analytics and AI workloads on governed, well-modelled data foundations.
- Engineer machine learning, generative AI and agentic AI solutions from prototype through to production.
- Apply intelligent automation to repeatable data engineering, quality and documentation work.
- Operationalise models and automated systems with monitoring, lineage, oversight and rollback.
Typical deliverables
- Data platform and lakehouse engineering
- Ingestion, integration and pipeline delivery
- Data transformation and modelling implementation
- AI / ML solution engineering
- Generative and agentic AI solution design
- Intelligent automation design
- Model and pipeline operationalisation
- Engineering standards and delivery patterns
Business outcomes
- Trusted data foundations turned into working, operational capability.
- AI, automation and autonomous systems that reach production and stay supportable.
- Less manual effort, and engineering capacity released for new capability.
Design and implement practical information management, data quality, master and reference data, metadata, lifecycle and data-product capabilities that make enterprise data easier to manage, govern and use — 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.
- Records, content and information lifecycle are managed separately from data.
- Data assets are created for one project, never catalogued and never reused.
- Data ownership is unclear in day-to-day operations.
What DataVirtue does
- Assess data management capability and design practical solution patterns.
- Establish data quality, metadata and cataloguing approaches and workflows.
- Implement master and reference data management and issue-remediation processes.
- Define records, retention and information lifecycle management approaches.
- Package high-value data assets as governed data products with owners and data contracts.
- 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
- Information lifecycle and records recommendations
- Data product definitions and contracts
- Data issue and remediation workflow
- Stewardship operating model
Business outcomes
- Repeatable, lower-effort data management practices.
- Quality, metadata, master data and information lifecycle brought under practical control.
- Trusted data assets reused across teams, analytics and AI instead of rebuilt.
Establish pragmatic governance, ownership, stewardship, policies, decision rights and responsible-AI controls that become part of day-to-day delivery rather than remaining governance on paper.
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.
- AI adoption is moving faster than oversight, accountability and control.
- 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.
- Define responsible-AI governance — human oversight, explainability, accountability and risk assessment.
- Align controls to privacy, security, records and regulatory obligations.
- 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
- Responsible AI framework and controls
- AI risk assessment approach
- Issue and escalation model
- Governance adoption roadmap
Business outcomes
- Clear accountability for critical data and AI assets.
- Governance embedded into delivery, not bolted on afterwards.
- AI adopted with oversight, explainability and controls in place early.
How we deliver
A delivery model designed for high-stakes change.
Independent and objective
We are not reselling a platform. Advice is aligned to your outcomes, not a vendor roadmap.
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