A
Shared meaning
Create consistent definitions for important business concepts across teams, systems, reports, and data products.
Enterprise Data Architecture
Logical Data Architecture Service for Clear, Governed Enterprise Data Design
Dataconsultant helps data, technology, governance, and business teams define how core data concepts should be organised across the enterprise. The service maps domains, entities, relationships, ownership, semantics, and exchange boundaries so platform, integration, migration, analytics, and governance decisions can be made against a coherent business-aligned model.
- Business-domain and stakeholder alignment
- Vendor-neutral logical modelling
- Governance, privacy, and control considerations
- Implementation-ready architecture guidance
Illustrative architecture viewEnterprise information structure
Business alignedBusiness domainCustomer
Shared enterprise conceptsParty · Account · Agreement
Business domainProduct
Business domainSupplier
Common reference structuresLocation · Organisation · Channel
Business domainFinance
Customerowns / usesAccount
Productgoverned byAgreement
TransactionreferencesParty + Location
DomainsBoundaries and ownership
EntitiesMeaning and relationships
ExchangeCanonical integration views
Quick definition
What is logical data architecture?
Logical data architecture is the business-facing blueprint of an organisation’s data. It defines major data domains, concepts, entities, relationships, ownership, semantics, and exchange boundaries without tying them prematurely to a particular database, cloud platform, application, or vendor. It provides the stable structure needed to coordinate governance, integration, migration, analytics, and physical solution design.
Service offering
A practical architecture service from discovery to adoption
The engagement combines business analysis, logical modelling, governance alignment, architecture decision support, and implementation planning.
01
Current-state interpretation
Review business capabilities, data domains, source systems, models, integration patterns, terminology, ownership, quality concerns, and known constraints.
02
Target logical architecture
Define domain boundaries, core entities, relationships, canonical concepts, shared reference structures, and the intended flow of information.
03
Governance and decision rights
Clarify data ownership, stewardship, architecture authority, modelling standards, change control, and exception handling.
04
Implementation guidance
Translate the logical design into priorities, transition states, modelling backlogs, platform implications, integration requirements, and quality controls.
Key value propositions
Why organisations invest in logical data architecture
B
Reduced duplication
Expose overlapping entities, conflicting models, and unnecessary point-to-point data structures before more technology is added.
C
Better governance
Connect data ownership, policy, quality, privacy, and security responsibilities to clearly defined domains and entities.
D
Safer delivery
Give migration, integration, analytics, AI, and platform teams a stable target model for detailed physical implementation.
Problems addressed
Common signs that logical architecture work is needed
Conflicting definitions
Teams use different meanings for customer, product, account, supplier, revenue, location, or transaction.
Fragmented system models
Applications and platforms represent the same business concepts differently, increasing reconciliation and integration effort.
Unclear ownership
Responsibility for shared data is disputed or divided between business, technology, governance, and operational teams.
Architecture without business context
Physical schemas and pipelines are being designed before the organisation agrees what data means and how domains relate.
Migration uncertainty
Modernisation programmes lack a target information structure for mapping legacy data into future platforms.
Weak analytics foundations
Reports, metrics, and AI initiatives depend on inconsistent entities, joins, hierarchies, and reference data.
Clarify the information structure before committing to physical design
Discuss your domains, system landscape, modelling concerns, and transformation priorities.
Who the service is for
Suitable for organisations that need a coherent enterprise view of data
Good fit
- Enterprise data architecture or platform modernisation is underway.
- Multiple systems represent the same concepts differently.
- Data governance needs clearer domains, ownership, and decision rights.
- Migration, integration, master data, analytics, or AI delivery requires a stable target model.
- Business and technology teams need a common language for data.
May not be the right fit
- The requirement is limited to a small physical database schema with no wider business impact.
- Stakeholders cannot participate in validating terminology, relationships, or ownership.
- The organisation only wants a software configuration without architecture decisions.
- Legal, certification, audit, or cybersecurity assurance is expected without the relevant specialist scope.
Common use cases
Where logical data architecture creates practical value
Cloud and platform transformation
Define stable business structures before mapping them to warehouses, lakehouses, operational stores, event platforms, or data products.
Canonical data exchange
Establish common concepts and message boundaries for APIs, events, integration hubs, partner exchange, and application interoperability.
Domain ownership model
Connect domains and entities to accountable owners, stewards, policies, quality expectations, and issue resolution.
Legacy consolidation
Create a target model for source-to-target mapping, data cleansing, transformation rules, and decommissioning decisions.
Trusted semantic foundations
Align analytical models, metrics, features, and training data around consistent entities, hierarchies, and relationships.
Domain-oriented delivery
Define reusable business objects, boundaries, contracts, and responsibilities for product-oriented or federated data operating models.
Capabilities
Logical architecture capabilities that can be included
Business information structure
Identify business domains, subdomains, shared concepts, reference structures, and the relationships between them.
Logical modelling
Develop normalised or fit-for-purpose logical entity models, cardinalities, keys, hierarchies, lifecycle states, and business rules.
Governance alignment
Link architecture components to ownership, stewardship, quality, privacy, security, retention, and change-control responsibilities.
Delivery transition
Translate logical structures into implementation principles, mappings, priorities, architecture decisions, and acceptance criteria.
Deliverables
Outputs designed for decision-making and implementation
| Deliverable | Purpose | Typical users |
|---|---|---|
| Enterprise data domain map | Shows major information areas, boundaries, overlaps, shared concepts, and ownership. | Executives, data leaders, architects, governance teams |
| Logical entity relationship models | Defines core entities, attributes at an appropriate level, cardinalities, and relationships. | Data architects, modellers, engineers, analysts |
| Canonical concept definitions | Provides reusable enterprise meanings for common data exchanged across systems and domains. | Integration teams, API teams, platform teams |
| Ownership and stewardship matrix | Connects domains and entities to accountable business and operational roles. | Governance, risk, compliance, business owners |
| Architecture principles and standards | Guides modelling, naming, reuse, quality, privacy, security, and exception decisions. | Architecture review boards and delivery teams |
| Gap assessment and roadmap | Prioritises model development, remediation, platform alignment, and transition activities. | Programme leaders, procurement, delivery managers |
Need deliverables aligned to a specific programme?
Scope the architecture outputs around migration, governance, integration, analytics, AI, or data product delivery.
Service process
How Dataconsultant develops logical data architecture
Business alignment
Confirm drivers, scope, decisions, stakeholders, constraints, and intended downstream use.
Output: agreed architecture charterEvidence review
Assess terminology, models, systems, interfaces, reports, policies, quality issues, and existing architecture.
Output: current-state findingsDomain discovery
Identify domains, subdomains, shared information concepts, ownership, and important business events.
Output: domain and concept mapLogical modelling
Define entities, relationships, hierarchies, states, rules, and canonical structures at the required depth.
Output: reviewed logical modelsControl alignment
Apply governance, quality, privacy, security, residency, retention, and regulatory considerations.
Output: control and ownership matrixAdoption planning
Prioritise gaps, transition states, implementation dependencies, assurance activities, and knowledge transfer.
Output: roadmap and delivery guidanceTechnology, platforms, standards, and frameworks
Vendor-neutral design with practical implementation awareness
Modelling and metadata
Delivery ecosystems
Reference points
Important: Frameworks and standards are applied selectively according to sector, jurisdiction, internal policy, contractual duties, risk appetite, and the required level of assurance. This service does not itself provide legal advice, certification, or statutory audit.
Connect logical architecture to your current technology estate
Review platform constraints, modelling tools, metadata capabilities, and implementation dependencies.
Engagement models
Flexible ways to access logical data architecture expertise
Focused assessment
Evaluate a defined domain, programme, model, or architecture concern and provide findings with priority actions.
Project delivery
Develop an agreed set of domain maps, logical models, governance artefacts, and implementation guidance.
Embedded architect
Add specialist architecture capacity to an internal programme, platform team, or architecture function.
Ongoing assurance
Review models, decisions, changes, exceptions, and delivery alignment across a longer transformation programme.
Practical illustrative examples
How the service may be applied
The examples below are representative scenarios, not claims about named clients or guaranteed results.
Customer data harmonisation
An organisation with CRM, billing, ecommerce, and support platforms defines a shared Party–Customer–Account model, identifies authoritative sources, and separates business meaning from system-specific schemas.
Acquisition integration
A group maps overlapping product, supplier, organisation, and finance entities across acquired businesses, then establishes a target logical structure for phased integration and reporting.
Data product foundation
A federated data programme defines domain boundaries, shared reference entities, data contracts, ownership, and model standards before individual data products are built.
Evidence and validation
Architecture decisions should be traceable and reviewable
Evidence register
Document source systems, models, policies, reports, interviews, assumptions, and unresolved gaps used to shape the architecture.
Decision records
Record material choices, alternatives, rationale, owners, implications, exceptions, and dependencies.
Stakeholder validation
Use structured reviews with business, data, technology, governance, risk, privacy, security, and delivery stakeholders.
Expected outcomes and KPIs
Measure whether the architecture is being understood and used
Outcomes depend on adoption, implementation quality, stakeholder participation, data quality, platform decisions, and programme governance. Baselines should be agreed before measurement.
Priority domains with approved ownershipCoverage
Core entities with agreed definitionsConsistency
Delivery designs aligned to logical modelsAdoption
Duplicate or conflicting models identifiedRationalisation
Architecture exceptions resolved within processGovernance
Source-to-target mappings traceable to approved conceptsImplementation
Pricing and cost factors
What influences the cost of logical data architecture work
Scope breadth
Number of domains, business units, jurisdictions, systems, interfaces, models, and transformation programmes in scope.
Modelling depth
Whether the work requires concept maps, high-level logical models, detailed entity relationships, canonical objects, or implementation mappings.
Stakeholder complexity
Number of workshops, decision groups, owners, review cycles, suppliers, and cross-functional dependencies.
Control requirements
Privacy, security, residency, retention, regulatory, audit, quality, and risk considerations that must be incorporated.
Evidence quality
Availability and reliability of existing models, inventories, glossaries, lineage, policies, diagrams, and subject-matter expertise.
Delivery model
Focused assessment, defined project, embedded architect, ongoing assurance, onsite needs, and knowledge-transfer expectations.
Request a scope-based estimate
Share the domains, systems, intended deliverables, stakeholders, and programme context for a written proposal.
Why consider Dataconsultant
Architecture guidance that connects business meaning to delivery reality
Dataconsultant approaches logical architecture as a decision-support and implementation discipline, not a diagramming exercise.
Business and technology alignment
Models are validated against business processes, decisions, controls, and delivery needs.
Models are validated against business processes, decisions, controls, and delivery needs.
Evidence-conscious delivery
Assumptions, unresolved questions, limitations, and dependencies are documented.
Assumptions, unresolved questions, limitations, and dependencies are documented.
Governance built into architecture
Ownership, standards, quality, privacy, security, and change control are considered from the start.
Ownership, standards, quality, privacy, security, and change control are considered from the start.
Vendor-neutral recommendations
Logical design remains independent of a particular technology unless platform-specific implementation support is requested.
Logical design remains independent of a particular technology unless platform-specific implementation support is requested.
Security, quality, privacy, and compliance
Control considerations embedded in the architecture
Data quality
Define critical entities, quality dimensions, validation responsibilities, reference structures, and issue ownership.
Privacy
Identify personal or sensitive concepts, purpose limitations, minimisation needs, retention implications, and cross-border concerns.
Security
Support classification, access principles, segregation, trusted exchange, auditability, and protection requirements at a logical level.
Compliance
Trace relevant obligations and internal policies to domains, entities, ownership, records, and architecture decisions.
Legal interpretation, formal compliance opinions, statutory audit, security testing, and certification require appropriately authorised specialists and should be commissioned separately where needed.
Technology ecosystems and delivery environment
Designed to work across mixed enterprise estates
Operational systems
ERP, CRM, ecommerce, finance, supply chain, HR, service, and industry platforms.
Data platforms
Cloud warehouses, lakehouses, data lakes, operational stores, marts, and data product platforms.
Integration estate
APIs, events, messaging, ETL/ELT, iPaaS, file exchange, partner feeds, and batch interfaces.
Governance tooling
Catalogues, glossaries, lineage, quality, master data, access governance, privacy, and observability tools.
Customer perspectives
Representative feedback on logical data architecture support
These testimonials are realistic, representative examples written for this service and are not presented as independently verified customer reviews.
★★★★★
“The workshops gave our business and architecture teams a shared way to discuss customer, account, and product data. The final domain map was clear, practical, and much easier to use than our previous collection of system diagrams.”
★★★★★
“Dataconsultant helped us separate business meaning from the constraints of our legacy applications. That made the migration mapping discussions more structured and gave the delivery teams a stable target to work from.”
★★★★★
“The team handled competing definitions carefully and documented decisions rather than forcing artificial agreement. We now have clearer ownership for shared entities and a sensible process for reviewing model changes.”
★★★★★
“The logical model connected our API programme, reporting requirements, and master data work without locking us into one platform. Communication was professional, and revisions were incorporated with clear rationale.”
★★★★★
“We appreciated the emphasis on privacy, retention, and access implications within the model. The architecture was useful to both technical teams and compliance stakeholders, which reduced ambiguity during design reviews.”
★★★★★
“The engagement gave our data product teams consistent entity boundaries and shared reference concepts. The deliverables were well organised, the assumptions were visible, and the knowledge-transfer sessions were practical.”
Frequently asked questions
Logical data architecture questions from buyers and delivery teams
What is logical data architecture?
Logical data architecture defines business data domains, entities, relationships, semantics, ownership, and exchange boundaries independently of a specific physical database or platform. It provides a stable enterprise view that can guide governance, integration, migration, analytics, and solution design.
How is logical data architecture different from conceptual data modelling?
Conceptual modelling usually provides a high-level view of important business concepts and relationships. Logical architecture generally extends this into more structured domain boundaries, entities, relationships, keys, hierarchies, ownership, standards, and implications for integration and implementation.
How is logical architecture different from physical data architecture?
Logical architecture defines what data means and how concepts relate. Physical architecture defines how those structures are implemented in databases, schemas, files, events, APIs, platforms, storage, pipelines, and infrastructure. One logical model may support several physical implementations.
What is included in the service?
Scope can include discovery, current-state review, domain mapping, conceptual and logical models, canonical concepts, ownership, decision rights, modelling standards, integration boundaries, quality and control considerations, gap assessment, roadmaps, design assurance, and knowledge transfer.
Which stakeholders should participate?
Typical participants include business owners, subject-matter experts, data leaders, enterprise and data architects, governance and quality teams, engineers, analysts, integration specialists, privacy, security, risk, compliance, and programme leaders. Participation depends on the domains and decisions in scope.
How long does a logical data architecture engagement take?
There is no reliable fixed duration before discovery. Timing depends on the number of domains and systems, modelling depth, stakeholder availability, evidence quality, regulatory constraints, review cycles, and whether implementation support or detailed mappings are included.
What information is needed from the client?
Useful inputs include process maps, organisation structures, application inventories, existing models, schemas, interfaces, glossaries, reports, lineage, policies, quality findings, migration plans, regulatory obligations, and access to accountable business and technology stakeholders.
Can the service support cloud migration?
Yes. Logical architecture can define the stable target information structure used for migration scoping, source-to-target mapping, transformation rules, data cleansing, platform design, transition states, and decommissioning decisions.
Can the service support APIs and event-driven architecture?
Yes. Canonical concepts, entity boundaries, business events, relationships, identifiers, and ownership can support API resource design, event payloads, data contracts, integration standards, and interoperability across applications and partners.
How are privacy and security considered?
The work can identify sensitive concepts, classifications, access principles, retention needs, residency constraints, trust boundaries, and ownership. It does not replace legal advice, formal security assessment, penetration testing, certification, or statutory compliance review unless separately commissioned.
Does Dataconsultant recommend specific tools?
The logical design is vendor-neutral. Tool and platform implications can be evaluated when relevant, including modelling repositories, metadata catalogues, master data platforms, integration tools, warehouses, lakehouses, and governance systems. Product selection should reflect documented requirements and procurement controls.
Can Dataconsultant work with our internal architects and vendors?
Yes. The engagement can complement internal teams, systems integrators, platform vendors, managed-service providers, and programme partners. Responsibilities, access, deliverables, review authority, dependencies, and escalation routes should be agreed at the start.
How is pricing calculated?
Pricing is affected by domain breadth, modelling depth, system complexity, stakeholder count, workshop needs, review cycles, control requirements, documentation quality, onsite requirements, implementation support, and the selected engagement model. A written estimate can be prepared after initial scoping.
How should architecture quality be measured?
Measures may include approved domain ownership, definition consistency, model reuse, traceability, adoption by delivery teams, reduction in conflicting structures, timely exception resolution, mapping completeness, and stakeholder acceptance. Measures should be baselined and tied to intended decisions.
What are the main limitations of logical data architecture?
A logical architecture does not by itself fix data quality, replace governance, implement platforms, resolve every organisational disagreement, or guarantee programme outcomes. Its value depends on evidence quality, stakeholder decisions, operating-model adoption, physical design, implementation discipline, and ongoing maintenance.