Data Engineering Service

Data Modeling and Database Design

4.9 out of 5Based on 5,438 reviews

Create data models and database structures that represent business meaning, support reliable applications and improve analytical usability. We develop conceptual, logical, physical, dimensional, relational, NoSQL, graph, canonical, industry and semantic models while supporting database modernisation, design governance and implementation decisions across changing platform requirements and standards.

Business-to-physical traceability
Fit-for-purpose model patterns
Database design standards
Modernisation-ready structures
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Data model design viewIllustrative
Business conceptsDefined responsibility and output
Logical relationshipsDefined responsibility and output
Physical structuresDefined responsibility and output
Consumption modelsDefined responsibility and output

Illustrative model progression

Service Directory

Data Modeling and Database Design services

Select a specialist service to review its scope, delivery considerations and potential outputs.

Conceptual Data Modeling Service

Conceptual Data Modeling Service engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Logical Data Modeling Service

Logical Data Modeling engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Physical Data Modeling Service

Physical Data Modeling Service engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Dimensional Data Modeling Service

Dimensional Data Modeling Service engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Relational Database Design Service

Relational Database Design engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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NoSQL Data Modeling Service

NoSQL Data Modeling Service engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Graph Data Modeling Service

Graph Data Modeling Service engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Canonical Data Model Service

Canonical Data Model Service engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Industry Data Model Service

Industry Data Model Service engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Semantic Model Development Service

Semantic Model Development engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Database Modernization Service

Database Modernization Service engineering to assess, design, implement or improve the capability with dependable architecture, controls and delivery practices.

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Business Value

A structured approach to practical outcomes

Engagements connect business priorities, technical realities, control requirements and the capability of the teams that will own the result.

Outcome alignment

Connect scope to business priorities, service expectations and measurable value.

Decision clarity

Use evidence, options and documented criteria to make complex choices transparent.

Control by design

Address security, privacy, governance, resilience and auditability throughout delivery.

Operational readiness

Prepare ownership, documentation, support and knowledge transfer for sustainable use.

Delivery Approach

How engagements are typically structured

Discover

Clarify outcomes, current state, constraints, stakeholders and available evidence.

Assess and design

Evaluate options, dependencies, risks, controls and target requirements.

Deliver and validate

Produce the agreed outputs using documented standards and acceptance criteria.

Transition and improve

Support ownership, adoption, measurement and prioritised continuous improvement.

FAQs

Data Modeling and Database Design questions

Answers to common search and procurement questions about scope, delivery, timelines, pricing, quality, security and support.

What services are included in Data Modeling and Database Design?

The service area includes the specialist capabilities listed on this page. Scope can cover assessment, strategy, architecture, design, implementation, assurance, optimisation and operating enablement depending on the selected service and business requirement.

When should an organisation engage a data modeling and database design specialist?

External support is useful when teams need independent expertise, additional delivery capacity, cross-functional alignment or a structured approach to complex decisions. The appropriate starting point depends on current maturity, urgency, risk and evidence availability.

How is the right service selected?

Selection begins with the business outcome, current environment, constraints, risk profile and delivery stage. A focused discovery discussion can identify whether one specialist service or a coordinated group of services is the most appropriate starting point.

Can the engagement support cloud, on-premises and hybrid environments?

Yes. Work can address cloud, on-premises, hybrid and multi-cloud environments where relevant. Recommendations consider workload fit, integration, security, residency, skills, operating capacity, commercial constraints and existing investments.

What deliverables are typically provided?

Deliverables vary by service and may include assessments, decision frameworks, architecture artefacts, implementation plans, configured components, standards, test evidence, operating procedures, roadmaps, decision logs and knowledge-transfer materials.

How long does a data modeling and database design engagement take?

There is no reliable fixed duration before scoping. Timing depends on estate complexity, stakeholder access, evidence quality, number of systems or domains, assurance requirements, delivery dependencies and the depth of implementation required.

How is pricing determined?

Pricing is influenced by scope, complexity, delivery model, specialist roles, environment count, integrations, evidence quality, data volumes, controls, documentation, testing and ongoing support. A written estimate should follow initial discovery.

Can specialists work with internal teams and existing vendors?

Yes. Engagements can be structured alongside internal data, architecture, security, cloud, operations and business teams, as well as software vendors and systems integrators. Responsibilities and decision rights should be agreed at the start.

How are security, privacy and governance addressed?

Security, privacy, governance, access, retention, lineage, auditability and resilience requirements are incorporated according to scope and applicable obligations. Specialist legal, audit or certification advice should be commissioned separately where needed.

Can support continue after the initial engagement?

Yes. Follow-on support can include assurance, optimisation, implementation assistance, operating-model transition, documentation, capability building, managed support and periodic health checks under a separately agreed scope.

How is quality validated?

Quality can be validated through peer review, architecture and design checks, standards, automated and manual testing, reconciliation, performance review, security controls, acceptance criteria and documented sign-off responsibilities.

What information is needed to start?

Useful starting information includes business objectives, priority use cases, current architecture, systems and tools, known issues, data classifications, service expectations, timelines, stakeholders, constraints and existing assessments or designs.

How are outcomes measured?

Measures should be linked to the engagement purpose and may include delivery speed, reliability, performance, quality, reuse, adoption, cost, availability, control effectiveness, reduced manual effort and realised business value.

Discuss your data modeling and database design requirement

Share your current situation, intended outcome and delivery constraints to identify an appropriate starting point.

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