Data Advisory Service
Define data direction, priorities, operating models, investment choices and practical transformation roadmaps.
Typical need: We need a clear data strategy and an agreed direction for investment.
Data, AI and governance consulting
Data and AI consulting services organised around the decisions your business needs to make
Dataconsultant helps organisations define direction, improve data foundations, establish governance, strengthen analytics, prepare data for AI, assess risk and capability, modernise platforms and operate critical data and AI services. The emphasis is practical: clear decisions, accountable ownership, usable deliverables and a path from advisory work to implementation.
Business priorities before technology choices
Governance, privacy, security and control considered early
Platform-aware guidance without a single-vendor bias
Implementation, managed support and knowledge transfer available
The most useful starting point is the business decision or operational problem—not a pre-selected consulting package.
DirectionMake priorities explicit
Connect investment, capability and technology choices to measurable business outcomes.
ControlClarify accountability
Define ownership, decision rights, controls, evidence and governance responsibilities.
DeliveryReduce execution ambiguity
Translate target states into sequenced work, dependencies, deliverables and acceptance criteria.
ContinuitySupport sustainable operation
Combine implementation, managed support, measurement and knowledge transfer where required.
1
Start with the decision or problem, then choose the service
Buyers often know the problem before they know the consulting category. This route keeps the page aligned with how executives and delivery teams actually seek help.
We need a data strategy
Data Advisory Service
View relevant service
Our data platforms are fragmented
Data Engineering Service
View relevant service
Ownership and accountability are unclear
Data Governance Service
View relevant service
Reporting does not support decisions
Data Analytics Service
View relevant service
We need reliable data for AI
AI Data Service
View relevant service
We need an independent review
Assessments and Audits Service
View relevant service
We need continuous operational support
Managed Data and AI Services
View relevant service
We are selecting or modernising a platform
Platform Consulting Service
View relevant service
We need to build internal capability
Academy Service
View relevant service
We need sector-specific support
Industries Service
View relevant service
2
A complete service portfolio across the data and AI lifecycle
Each service is presented first by the business need it addresses, then by its capabilities, expected outputs and intended outcome. This keeps technical depth available without forcing every detail into the first scan.
Data Engineering Service
Design and improve dependable data platforms, pipelines, integration layers, storage models and engineering practices.
Typical need: Our data platforms are fragmented, difficult to operate or unable to scale.
Capabilities, deliverables and outcome
Data Governance Service
Establish ownership, accountability, policies, controls, decision rights, metadata practices and measurable governance operations.
Typical need: Ownership, policy and accountability for data are unclear.
Capabilities, deliverables and outcome
Data Analytics Service
Turn trusted data into useful reporting, analysis, performance measurement, forecasting and decision-support capabilities.
Typical need: Reporting exists, but it does not consistently support business decisions.
Capabilities, deliverables and outcome
AI Data Service
Prepare, govern, evaluate and manage data used for machine learning, generative AI and other artificial intelligence systems.
Typical need: We need reliable, governed and traceable data for AI adoption.
Capabilities, deliverables and outcome
Assessments and Audits Service
Provide structured reviews of data, AI, governance, quality, engineering, platform, risk and operational capabilities.
Typical need: We need an independent current-state review and prioritised remediation plan.
Capabilities, deliverables and outcome
Managed Data and AI Services
Provide ongoing operational support, monitoring, governance administration, engineering assistance and continuous improvement.
Typical need: We need sustained operational support rather than a one-off project.
Capabilities, deliverables and outcome
Platform Consulting Service
Support platform evaluation, selection, architecture, implementation planning, optimisation and governance.
Typical need: We are selecting, modernising or optimising a data or AI platform.
Capabilities, deliverables and outcome
Academy Service
Develop practical internal capability through role-based training, workshops, leadership education and applied learning programmes.
Typical need: We need to build data, governance, analytics or AI capability internally.
Capabilities, deliverables and outcome
Industries Service
Apply data and AI consulting approaches to sector-specific operating environments, risks, regulations and stakeholder expectations.
Typical need: We need support adapted to our sector, regulatory environment and business model.
Capabilities, deliverables and outcome
3
Connected capabilities, not isolated workstreams
Most material data and AI decisions span more than one discipline. The capability landscape shows how strategic, technical, governance, operational and people concerns can be combined around a common business outcome.
Core decision
Business priorities and accountable decisions
Business outcomes, risk, constraints and ownership determine which capabilities matter and in what sequence they should be addressed.
Strategy and transformation
Architecture and engineering
Governance, quality and control
Analytics and decision support
AI data and responsible adoption
Platforms and modernisation
Assessment and assurance
Managed operations
Training and capability development
4
How the services respond to common business situations
The examples below translate service names into situations buyers recognise. Actual scope and outcomes depend on the organisation, available evidence, stakeholder participation and agreed responsibilities.
Developing an enterprise data strategy
- Situation
- Business units are investing independently and priorities are not aligned.
- Response
- Align stakeholders, assess capabilities, define a target operating model and sequence initiatives.
- Outcome
- A shared direction and decision-ready transformation roadmap.
Modernising a cloud data platform
- Situation
- Legacy integration and storage patterns limit reliability and scalability.
- Response
- Review architecture, workloads, interfaces, controls and operating practices before designing the target state.
- Outcome
- A practical modernisation plan with clearer dependencies and operational requirements.
Establishing a governance operating model
- Situation
- Policies exist, but ownership and decision rights are not operational.
- Response
- Define governance bodies, ownership roles, stewardship workflows, policies and measures.
- Outcome
- A governance model that can be implemented and monitored.
Improving data quality and ownership
- Situation
- Recurring quality issues have no agreed owners or remediation process.
- Response
- Prioritise critical data, assign accountability, define controls and establish issue workflows.
- Outcome
- Improved visibility and more consistent resolution of priority issues.
Designing executive KPI reporting
- Situation
- Leadership receives inconsistent measures from multiple reporting teams.
- Response
- Clarify decisions, standardise KPI definitions, identify data sources and design reporting governance.
- Outcome
- A coherent KPI framework and reporting blueprint.
Preparing data for AI adoption
- Situation
- AI initiatives are progressing without sufficient dataset quality, traceability or control.
- Response
- Assess data readiness, documentation, provenance, evaluation needs and governance requirements.
- Outcome
- A prioritised plan for more reliable and accountable AI data.
Assessing data and AI controls
- Situation
- Leaders need an independent view of current controls and material gaps.
- Response
- Review evidence, interview stakeholders, test selected controls and prioritise remediation.
- Outcome
- A documented findings set and risk-informed improvement roadmap.
Selecting a data platform
- Situation
- Competing platform options make cost, integration and governance trade-offs difficult to compare.
- Response
- Define requirements, evaluation criteria, constraints and operating implications before comparing options.
- Outcome
- A documented and defensible platform decision process.
Operating governance as a managed service
- Situation
- Governance tasks are defined but internal capacity is limited.
- Response
- Establish service scope, workflows, reporting, issue management and continuous-improvement routines.
- Outcome
- More consistent governance operations and clearer service visibility.
Building organisation-wide data literacy
- Situation
- Teams use data differently and lack a shared understanding of roles and responsibilities.
- Response
- Create role-based learning pathways supported by practical examples and applied workshops.
- Outcome
- A more consistent baseline of data capability across participating teams.
5
A structured path from business alignment to operational transition
The exact sequence changes by engagement, but the operating principle remains consistent: understand the decision, establish evidence, design the target state, prioritise action, support implementation and make ownership measurable.
Business alignment
Understand priorities, stakeholders, constraints and intended outcomes.
Output: Agreed objectives and engagement scope.
Current-state review
Assess data, systems, processes, ownership, controls, platforms and capabilities.
Output: Current-state findings.
Risk and requirement analysis
Identify business, technical, governance, privacy, security, regulatory and operational requirements.
Output: Prioritised requirements and risk register.
Target-state design
Define the future operating model, architecture, controls, service model or capability design.
Output: Target-state design.
Roadmap and prioritisation
Sequence initiatives according to value, dependencies, risk, effort and readiness.
Output: Prioritised roadmap.
Implementation or remediation
Support delivery, engineering, governance activation, configuration or corrective action.
Output: Implemented or remediated capability.
Validation and knowledge transfer
Test outputs, confirm acceptance criteria, document decisions and prepare internal teams.
Output: Validated deliverables and knowledge-transfer materials.
Operational transition and measurement
Establish ownership, monitoring, reporting, improvement cycles and ongoing service arrangements.
Output: Operational handover and measurement framework.
6
Detailed scope, standards, platforms and measures—available when you need them
The original page contains useful procurement and due-diligence detail. It is retained here in a compact evidence library so specialists can inspect it without overwhelming the main buying journey.
Typical deliverables by service
| Service | Typical deliverables | Primary stakeholders | Decision supported |
|---|---|---|---|
| Data advisory | Data strategy; target operating model; prioritised roadmap | Boards, executives, CDOs, business leaders | Where to invest and what to do first |
| Data engineering | Architecture blueprint; integration design; engineering standards | CIOs, CTOs, architects, engineering teams | How to build reliable data foundations |
| Data governance | Governance framework; ownership model; policy and control set | CDOs, governance, risk, compliance, privacy | How accountability and controls should operate |
| Data analytics | Analytics requirements; KPI framework; dashboard blueprint | Executives, finance, operations, analytics leaders | Which measures and insights should guide decisions |
| AI data | AI data readiness assessment; dataset documentation; control model | AI leaders, data teams, risk, privacy, security | Whether data is suitable for responsible AI use |
| Assessment and audit | Maturity assessment; findings report; remediation roadmap | Internal audit, risk, executives, programme leaders | Which gaps and risks require priority action |
| Managed services | Service model; operational reporting; improvement backlog | Operations leaders, CDOs, platform owners | How ongoing support and accountability should work |
| Platform consulting | Options assessment; architecture recommendation; migration roadmap | CIOs, CTOs, procurement, architecture teams | Which platform approach best fits requirements |
| Academy | Learning pathway; curriculum; workshop materials | HR, L&D, executives, technical and operational teams | How internal capability should be developed |
Frameworks and standards considered
Reference points are selected according to jurisdiction, sector, risk and engagement scope; they are not treated as automatic certification claims.
Data and governance
- DAMA-DMBOK
- DCAM
- ISO 8000
- ISO/IEC 38505
Technology and service management
- COBIT
- ITIL
- ISO/IEC 27001
- NIST Cybersecurity Framework
Privacy and responsible AI
- ISO/IEC 27701
- ISO/IEC 42001
- NIST AI Risk Management Framework
- GDPR
- Privacy by design
Risk and control
- Model risk management
- Internal control frameworks
- Data protection principles
- Sector-specific regulatory requirements
Platforms and technologies supported
Technology recommendations remain requirements-led and platform-aware.
Cloud and data platforms
- Microsoft Azure
- Amazon Web Services
- Google Cloud
- Snowflake
- Databricks
- Microsoft Fabric
- BigQuery
- Redshift
- Synapse Analytics
Data integration and orchestration
- Azure Data Factory
- AWS Glue
- Apache Airflow
- dbt
- Kafka
- Informatica
- Talend
- Fivetran
Analytics and visualisation
- Power BI
- Tableau
- Looker
- Qlik
- Excel
- Python
- R
Governance, metadata and quality
- Microsoft Purview
- Collibra
- Alation
- Informatica
- Atlan
- Monte Carlo
- Great Expectations
Engagement models
| Model | Best for | Scope style | Typical outputs | Client involvement | Continuity |
|---|---|---|---|---|---|
| Advisory engagement | Strategic choices or executive guidance | Flexible question-led scope | Recommendations, decision records, roadmap | Regular sponsor access | Low |
| Defined project | A clearly bounded capability or deliverable | Milestone-based scope | Designed and agreed project outputs | Active subject-matter participation | Medium |
| Assessment or audit | Independent current-state review | Evidence-led review scope | Findings, maturity view, remediation plan | Access to people, systems and evidence | Low |
| Implementation support | Delivery or remediation assistance | Workstream or outcome-based scope | Configured, engineered or activated capability | Joint delivery with internal teams | Medium |
| Managed service | Ongoing operational support | Service catalogue and operating model | Monitoring, administration and reporting | Service owner and governance participation | High |
| Training programme | Capability development for defined audiences | Role-based learning scope | Curriculum, workshops and learning materials | Participant and sponsor engagement | Medium |
| Fractional or embedded specialist support | Ongoing specialist input without a full project | Capacity and priority-based scope | Advisory, delivery and decision support | Close collaboration with internal teams | Medium to high |
Outcome and service measures
| Measure | What it indicates | Evidence source | Typical cadence |
|---|---|---|---|
| Strategic alignment | Share of priority initiatives mapped to agreed business outcomes | Approved roadmap and decision records | Quarterly or by governance cycle |
| Data ownership coverage | Coverage of critical data elements with named owners | Ownership register | Monthly or quarterly |
| Data-quality issue resolution | Open, ageing and resolved priority issues | Issue workflow and scorecard | Monthly |
| Pipeline reliability | Successful runs, incidents and recovery performance | Monitoring and incident records | Daily or weekly |
| Reporting adoption | Usage of agreed reports and decision products | Analytics usage logs and stakeholder feedback | Monthly |
| Metadata completeness | Coverage of required metadata fields for priority assets | Metadata platform reports | Monthly or quarterly |
| Control implementation | Status of agreed controls and remediation actions | Control register and evidence repository | Monthly or quarterly |
| Platform usage and cost visibility | Usage, unit cost and exception trends | Platform billing and operational reporting | Monthly |
| Incident response | Incident volume, severity, ageing and closure quality | Service management records | Weekly or monthly |
| Training participation | Attendance, completion and applied-learning evidence | Learning records and assessments | Per programme |
| Capability maturity | Movement against agreed maturity criteria | Periodic maturity assessment | Semi-annually or annually |
| Stakeholder satisfaction | Structured feedback against agreed service criteria | Survey and review records | At milestones or quarterly |
How scope and responsibilities are controlled
Engagements should make assumptions, dependencies, exclusions, evidence gaps, acceptance criteria and responsibility boundaries visible. Consulting support does not automatically replace legal advice, statutory audit, certification, penetration testing or specialist regulatory interpretation.
Where implementation or managed support is included, responsibilities, escalation routes, operating measures and handover expectations should be agreed before transition.
7
Check fit first, then define a proportionate commercial scope
A strong consulting relationship starts with fit. The goal is not to force every need into a large programme, but to select the smallest useful intervention that can support the required decision or operational outcome.
DataConsultant may be a good fit when
- You need a clear data, governance, analytics, AI or platform direction tied to business priorities.
- Multiple teams need clearer ownership, decision rights, standards or control responsibilities.
- You need an independent assessment of capability, maturity, risk, controls or platform choices.
- You need design or implementation support that connects architecture, governance and operating practice.
- You need sustained data or AI operational support with defined reporting and improvement cycles.
- You need role-based capability building for executives, specialists or operational teams.
A different or narrower approach may be better when
- The need is a single break/fix task with no advisory, design or operating-model requirement.
- The primary requirement is formal legal advice, statutory certification or specialist penetration testing.
- No accountable sponsor or stakeholder group can provide evidence or make decisions.
- The desired outcome is undefined and there is no agreement on the business problem to solve.
- A product-only procurement decision has already been made and no independent evaluation is required.
- The work sits outside data, AI, analytics, governance, platforms, controls or related capability building.
Commercial scope
Pricing should follow the real work—not a generic package
Service cost varies with scope, evidence, organisational complexity, stakeholder availability, platform landscape, controls, deliverables and the amount of implementation or ongoing support required.
Request a scoped conversation01Scope and complexity
02Number of business units
03Number of systems and data sources
04Stakeholder availability
05Regulatory and control requirements
06Existing documentation quality
07Platform landscape
08Required deliverables
09Implementation involvement
10Managed-service coverage
11Training audience size
12Travel or on-site requirements
Why DataConsultant
Structured advice for executive and specialist stakeholders
Engagements connect business priorities with technical realities, governance obligations, evidence, implementation requirements and operational ownership. The aim is to leave decision-makers with clearer choices and delivery teams with usable next steps.
Discuss your requirement8
Questions about DataConsultant services
These answers provide a practical overview. Final scope, responsibilities, exclusions and outputs are confirmed during consultation and engagement planning.
What data and AI consulting services does Dataconsultant provide?
Dataconsultant provides data advisory, engineering, governance, analytics, AI data, assessments and audits, platform consulting, managed data and AI services, professional training and sector-focused support. Engagements can range from focused advice and independent reviews to defined implementation support and ongoing managed operations.
Which service should our organisation choose first?
The right starting point depends on the decision you need to make. Organisations seeking direction often begin with Data Advisory. Those facing operational or technical gaps may begin with Engineering, Governance, Analytics or a focused Assessment. A consultation can help clarify the most appropriate entry point and avoid unnecessary scope.
Can Dataconsultant assess our current data capabilities?
Yes. The Assessments and Audits Service can review selected data, AI, governance, quality, platform, engineering, risk or operating capabilities. The exact evidence, stakeholders, systems and controls reviewed are agreed in scope. Typical outputs include findings, a maturity view, prioritised risks and a practical remediation roadmap.
Does Dataconsultant support data strategy development?
Yes. The Data Advisory Service can support data strategy, operating-model design, investment prioritisation, capability planning, business-case development and transformation roadmaps. The work is designed to connect business priorities with practical governance, architecture, delivery and measurement considerations.
Can Dataconsultant help establish data governance?
Yes. Support may include governance operating models, ownership and stewardship roles, policies, decision rights, metadata practices, lineage, data-quality governance, controls and governance reporting. The emphasis is on making governance operational rather than producing policy documents that are difficult to apply.
Does the company provide data engineering support?
Yes. The Data Engineering Service can support architecture, data pipelines, integration, cloud data platforms, modelling, DataOps, observability and reliability practices. Scope can focus on design, review, remediation planning or implementation support depending on the organisation’s needs and delivery model.
Can Dataconsultant help prepare data for AI?
Yes. The AI Data Service focuses on the quality, traceability, documentation, governance and evaluation needs of data used by AI systems. Support may include readiness assessments, dataset documentation, training-data controls, model-data traceability, evaluation-data design and responsible AI data practices.
What is included in an assessment or audit?
An assessment normally includes agreed criteria, evidence review, stakeholder interviews, selected control or capability testing, documented findings and prioritised recommendations. The scope can cover governance, quality, engineering, platforms, AI data, controls or operating practices. It does not automatically provide legal certification or guaranteed compliance.
Does Dataconsultant provide managed data and AI services?
Yes. Managed services can include monitoring, governance administration, data operations, platform support, quality management, incident coordination, performance reporting and continuous improvement. The service catalogue, responsibilities, reporting cycle and escalation routes are defined with the client before operational transition.
Can Dataconsultant help us select a data platform?
Yes. Platform Consulting can help define requirements, evaluation criteria, architecture implications, integration needs, governance requirements, migration considerations and cost visibility. Recommendations are based on the organisation’s context and are not presented as tied to a single vendor.
Is training available for executives and operational teams?
Yes. The Academy Service can provide executive education, governance training, data literacy, technical learning, AI awareness and role-based capability pathways. Programmes can be adapted for leaders, governance participants, analysts, engineers, operational teams and other defined audiences.
Does Dataconsultant work with regulated organisations?
Dataconsultant can support organisations operating in regulated environments. Relevant controls, frameworks and regulatory considerations depend on the client’s jurisdiction, sector, risk profile and engagement scope. Consulting support does not replace legal advice, regulatory interpretation or formal certification where those are required.
Which technologies and platforms are supported?
Support can cover widely used cloud, data, integration, analytics, metadata, governance and quality platforms. Examples include Azure, AWS, Google Cloud, Snowflake, Databricks, Microsoft Fabric, Power BI, Tableau, Purview, Collibra, Alation, Airflow and dbt. Recommendations remain requirements-led and platform-aware.
How are service outcomes measured?
Measures are agreed during scope and may include strategic alignment, ownership coverage, issue resolution, pipeline reliability, reporting adoption, metadata completeness, control implementation, platform cost visibility, training participation, maturity and stakeholder satisfaction. Measures are selected according to the engagement and available evidence.
How can we request a consultation?
Use the Request a Consultation button on this page to contact Dataconsultant. It is helpful to provide a short description of the business need, current challenge, relevant stakeholders, target decision and any known constraints. This allows the initial discussion to focus on fit, scope and the most useful next step.
Next step
Bring the business need first. Define the service second.
Share the decision, current challenge, stakeholders, evidence available and known constraints. DataConsultant can help identify a proportionate starting point and the capabilities that should be combined.