Shared Information Structure
Make domains, entities, relationships and cross-domain dependencies visible in one governed design layer.
Enterprise Data Architecture
Logical Data Architecture Consulting That Turns Business Meaning Into a Governed Enterprise Blueprint
DataConsultant helps organisations define a technology-agnostic logical data architecture across business domains, entities, attributes, identifiers, relationships, shared definitions and information rules. The work creates a traceable design layer between business meaning and physical implementation so architecture, governance, integration, analytics and AI teams can make consistent downstream decisions.
Business domains and subject areas made explicit
Logical entities, relationships and identifiers defined
Governance, quality and ownership requirements embedded
Traceable handover into physical design and delivery
Scope, timeline and commercial terms are confirmed after reviewing domain coverage, existing models, stakeholder access, architecture constraints, governance needs and required handover depth.
Business Meaning Preserved
Connect architecture terms to domain language, ownership and business rules before technology detail takes over.
Implementation Traceability
Give platform, integration and modelling teams a stable reference for downstream physical design decisions.
Controls by Design
Embed ownership, quality, classification and lifecycle requirements into the logical information structure.
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Move From Conflicting Data Definitions to a Reusable Logical Architecture Baseline
The objective is not to create another diagram. It is to establish a stable enterprise information layer that clarifies what data means, how concepts relate and which rules should survive changes in applications, databases and platforms.
Current State — Fragmented
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Different teams use different definitions for the same business concept.
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Application schemas are treated as the enterprise data model.
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Entity boundaries and identifiers vary by system or project.
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Cross-domain relationships are hidden in interfaces and reports.
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Governance rules are documented separately from architecture.
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Physical redesign repeatedly reopens basic business-definition debates.
Target State — Governed
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Business terms are connected to approved logical entities and attributes.
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Domains and subject areas have documented ownership boundaries.
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Relationships, cardinality, keys and shared identifiers are explicit.
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Reference and master-data concepts are treated consistently across domains.
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Quality, classification, lineage and lifecycle needs are linked to concepts.
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Physical models and interfaces can trace back to an approved logical baseline.
Need One Logical Baseline Before Multiple Teams Build Their Own Version of the Truth?
Use a focused architecture review to identify the domains, definitions, entity boundaries and decision gaps that must be resolved before downstream design accelerates.
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What Logical Data Architecture Defines — and What It Deliberately Leaves to Physical Design
Logical data architecture describes the information structure the enterprise needs independently of a specific database implementation. It organises business data into domains and subject areas, defines logical entities and attributes, records relationships and identifiers, connects shared definitions to ownership and rules, and creates traceability into physical models, data products, interfaces and analytical structures.
It should be detailed enough to guide consistent design, but not so implementation-specific that a database, cloud platform or application schema becomes the definition of the business itself.
Defines business information structureDomains, concepts, entities, attributes, relationships, keys, rules and shared definitions.
Creates architecture guardrailsNaming, modelling, reuse, ownership, reference-data and cross-domain consistency principles.
Connects governance to designCritical concepts, accountability, quality expectations, classification and lifecycle needs.
Supports implementation without dictating itPhysical schemas, indexes, partitions and vendor-specific optimisations remain downstream decisions.
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Logical Data Architecture Scope: From Domain Boundaries to Implementation Traceability
Scope is tailored to the decisions required. A focused engagement may cover one domain; a broader enterprise engagement can align shared concepts and rules across multiple business areas and transformation programmes.
Business vocabulary alignment
Reconcile critical terms, definitions, synonyms and contextual differences with domain experts and governance owners.
Domain & subject-area architecture
Define logical boundaries for customer, product, finance, supplier, workforce, operations and other information areas in scope.
Logical entities & attributes
Identify stable business entities, descriptive attributes, identifiers, classifications and reusable information concepts.
Relationships & cardinality
Document how entities associate, depend on one another and participate in business events, hierarchies and shared processes.
Identifiers & reference data
Clarify business keys, shared identifiers, code sets, hierarchies and reference-data responsibilities before physical implementation.
Business rules & constraints
Capture important validity rules, mandatory relationships, lifecycle conditions and semantics that downstream designs must preserve.
Governance & quality requirements
Connect ownership, criticality, quality dimensions, classification, retention, privacy and control expectations to logical concepts.
Traceability & handover
Map logical concepts to current sources, interfaces, products, analytical structures and physical models with documented decisions and gaps.
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A Logical Architecture Control Model That Keeps Meaning Consistent From Business Term to Implementation
The architecture pack can be structured so each downstream design decision has a clear source of meaning, ownership and review evidence rather than relying on undocumented assumptions.
Business meaning
Business termsApproved definitions, synonyms and contextual usage
Capabilities & processesWhere information is created, changed and consumed
Decision contextQuestions, outcomes and obligations the data must support
OwnershipAccountable domain, owner, steward and review forum
Logical structure
Domains & subject areasLogical boundaries and shared information areas
Entities & attributesStable business concepts and their descriptive properties
RelationshipsAssociations, cardinality, hierarchies and dependencies
Identifiers & rulesBusiness keys, reference sets, constraints and lifecycle rules
Implementation trace
Source mappingsWhere concepts exist today and known quality or ownership gaps
Interface mappingsAPIs, events, files, messages and canonical exchange structures
Data-product mappingsReusable domain products, analytical assets and consumer contracts
Physical-model handoverRequirements and decisions for platform-specific implementation
Metadata & glossary
Data quality
Privacy & classification
Lineage & traceability
Version & change control
Need an Architecture Pack Your Governance, Engineering and Platform Teams Can All Use?
Define the logical artefacts, model depth, review gates and traceability needed so downstream teams can build without reopening the same semantic decisions.
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Business Decision to Architecture Evidence: Make Every Logical Choice Traceable
A useful logical architecture explains why each definition and relationship exists, which stakeholder needs it, how it will be validated and what downstream implementation must preserve.
Which operational, analytical, regulatory or transformation decision must be supported?
Which concepts, measures, events, states and identifiers must be understood consistently?
Which domain is accountable, and which cross-domain relationships require shared governance?
How should entities, attributes, relationships, keys and rules represent that meaning?
What completeness, consistency, quality, control and traceability checks prove the design is usable?
Which physical models, interfaces, data products and analytical assets inherit the approved design?
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Use Logical Data Architecture Where Shared Meaning Must Survive Organisational or Technology Change
The service is most valuable when multiple systems, domains or programmes must agree on the information structure before detailed implementation proceeds.
ERP or CRM transformation
Separate enduring business concepts from application-specific schemas so migration and integration teams share one logical reference.
Enterprise data platform modernisation
Define the information structure and domain boundaries that warehouse, lakehouse or data-product designs need to preserve.
Integration & canonical data design
Create shared concepts and identifiers that reduce semantic drift across APIs, events, files and cross-application exchanges.
Mergers, acquisitions & consolidation
Reconcile overlapping terms, entities and systems to create a common information baseline before consolidation decisions.
Governance, master data & quality programmes
Connect ownership, critical concepts, identifiers, reference data and quality expectations to an architecture teams can implement.
Analytics and AI readiness
Establish consistent business entities and relationships so metrics, features, retrieval content and analytical products share traceable meaning.
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Logical Data Architecture Deliverables Designed for Review, Reuse and Downstream Handover
The final pack is agreed during discovery and scaled to the domains, model depth and delivery stage. Representative outputs include:
DELIVERABLE 01
Domain & subject-area map
Business data domains, logical boundaries, shared concepts, ownership context and cross-domain dependencies.
DELIVERABLE 02
Logical entity catalogue
Approved entities, definitions, attributes, identifiers, classifications and accountable subject-area context.
DELIVERABLE 03
Relationship views
Entity relationships, cardinality, dependencies, hierarchies and important business association rules.
DELIVERABLE 04
Identifier & reference-data principles
Business-key guidance, shared identifiers, code sets, hierarchies, reference ownership and reuse expectations.
DELIVERABLE 05
Business-rule register
Constraints, lifecycle states, mandatory relationships, derivation principles and semantic rules that require validation.
DELIVERABLE 06
Glossary & ownership alignment
Links between business terminology, data ownership, stewardship, model artefacts and review responsibilities.
DELIVERABLE 07
Quality & control requirements
Criticality, quality dimensions, classification, privacy, retention, lineage and assurance requirements linked to concepts.
DELIVERABLE 08
Traceability matrix
Mappings from logical concepts to current systems, interfaces, data products, analytical assets and target physical designs.
DELIVERABLE 09
Architecture standards & decisions
Naming, modelling, reuse, exceptions, assumptions, unresolved issues and architecture decision records.
DELIVERABLE 10
Implementation handover pack
Acceptance criteria, downstream modelling requirements, open decisions, dependencies and knowledge-transfer materials.
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How the Engagement Moves From Business Vocabulary to an Approved Logical Architecture
The sequence is adapted to the evidence available and the decision deadline, with review points that keep business meaning, architecture quality and downstream usability aligned.
Frame
Confirm domains, decisions, stakeholders, existing artefacts, constraints and acceptance criteria.
Inventory
Review current models, glossaries, applications, interfaces, reports, ownership and known quality issues.
Align Meaning
Resolve critical terminology, domain boundaries, ownership and conflicting definitions with business experts.
Model
Define entities, attributes, relationships, identifiers, rules, hierarchies and shared reference concepts.
Validate
Review completeness, consistency, quality, governance, privacy, integration and analytical implications.
Trace
Map logical concepts to source systems, interfaces, products, reports and planned physical designs.
Approve & Handover
Record decisions, exceptions, open items, acceptance evidence and ownership for ongoing change control.
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Client Inputs and Quality Gates That Keep the Logical Architecture Defensible
Architecture quality depends on access to evidence and accountable reviewers. Missing evidence is recorded as a limitation or open decision rather than silently assumed.
Useful client inputs
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Existing models & glossariesConceptual, logical, physical, dimensional and canonical models; data dictionaries; business terms; reference-data lists.
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Systems & integration evidenceApplication inventories, interface specifications, APIs, events, files, message schemas and lineage information.
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Business rules & reporting definitionsOperational rules, KPI definitions, calculations, hierarchies, classifications and known semantic disagreements.
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Governance & control contextOwnership, stewardship, data quality, privacy, security, retention, residency and audit requirements relevant to scope.
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Accountable reviewersDomain experts, architects, data owners, governance leads and delivery teams able to resolve disputed definitions and accept decisions.
Representative quality gates
| Quality gate | Key check | Evidence |
|---|---|---|
| Scope coverage | Required domains, subject areas and decision contexts are represented. | Scope-to-model coverage matrix |
| Semantic consistency | Critical terms and entities have agreed definitions and no unresolved duplicate meaning. | Glossary alignment and issue log |
| Relationship integrity | Cardinality, dependencies, hierarchies and mandatory associations are validated. | Reviewed relationship views |
| Identifier coherence | Business keys, shared identifiers and reference sets are explicit and reusable. | Identifier and reference-data register |
| Control coverage | Ownership, quality, classification, privacy and lifecycle expectations are linked where relevant. | Control mapping |
| Traceability | Logical concepts map to current sources and target implementation artefacts without unexplained gaps. | Traceability matrix |
| Change control | Decisions, assumptions, exceptions, versions and approval responsibilities are documented. | Decision and change log |
Need to Know Whether Your Existing Models Are Ready to Guide a Major Transformation?
Share the current models, domain scope and delivery objective. DataConsultant can help identify semantic gaps, missing controls and traceability issues before physical redesign or migration begins.
Commercial Approach
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Custom Scope and Pricing for Logical Data Architecture
DataConsultant does not publish a fixed fee for this exact service. Public INR evidence was not sufficiently comparable to present a defensible market range as DataConsultant pricing, so commercial terms are confirmed through a scoped proposal after the required domains, model depth, evidence and handover needs are understood.
Pricing basis: scope is shaped by domain count, existing model quality, stakeholder and workshop effort, relationship complexity, governance depth, traceability requirements, implementation mapping and ongoing assurance needs.
Logical Architecture Assessment
For organisations with existing models that need an independent review of semantic gaps, domain boundaries, consistency and readiness.
CommercialsRequest a Quote
Typical inclusions
- Current artefact review
- Domain and terminology gap analysis
- Logical-model consistency checks
- Traceability and control findings
- Prioritised remediation recommendations
- Architecture readout
Domain Logical Architecture
End-to-end logical architecture for one priority business domain or tightly related set of subject areas.
CommercialsRequest a Quote
Typical inclusions
- Business vocabulary and domain alignment
- Entity, attribute and relationship design
- Identifier and reference-data principles
- Business-rule and governance mapping
- Source and implementation traceability
- Review, approval and handover pack
Enterprise Logical Architecture
For organisations that need common information structures, shared concepts and architecture standards across multiple domains.
CommercialsRequest a Quote
Typical inclusions
- Enterprise domain and subject-area map
- Shared entity and terminology alignment
- Cross-domain relationship design
- Architecture standards and reusable patterns
- Governance and change-control model
- Prioritised domain rollout and handover
Architecture Review & Governance Support
Ongoing specialist support to review changes, new models, vendor designs and implementation alignment against the approved logical baseline.
CommercialsRequest a Quote
Typical inclusions
- Model and design reviews
- Architecture decision support
- Exception and change-control review
- Cross-domain consistency checks
- Implementation traceability assurance
- Knowledge transfer and governance support
Domain complexityNumber of domains, subject areas, entities, shared concepts and cross-domain relationships.
Evidence conditionQuality and consistency of existing models, glossaries, source mappings, lineage and documentation.
Review effortStakeholder count, workshop needs, disputed definitions, architecture boards and approval cycles.
Handover depthTraceability, physical-design mapping, implementation assurance, governance setup and ongoing support.
Commercial note: consulting fees are separate from any third-party modelling, catalogue, metadata, cloud or platform licence costs unless those items are explicitly included in the proposal.
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Use This Service When the Problem Is Shared Information Structure — Not a Narrow Database Configuration Task
Good fit for logical data architecture
- Multiple teams disagree on entity definitions, identifiers or domain boundaries.
- A major platform, ERP, CRM, integration or analytics programme needs a stable logical information layer.
- Business glossary, governance and architecture artefacts need to connect.
- Physical models differ by system but should trace to shared business meaning.
- Cross-domain relationships and reference data need explicit design and ownership.
- Architecture decisions must remain portable across vendor or platform choices.
May require a different or additional service
- You only need a database table, index, partition or performance tuning change.
- The requirement is a single pipeline, API or report with no wider semantic decision.
- A physical data model is already approved and only implementation coding is needed.
- The immediate need is a statutory audit, legal opinion, certification or penetration test.
- No accountable business or data owner can validate definitions and rules.
- The main question is platform procurement rather than enterprise information structure.
Boundary: logical data architecture can establish the semantic and governance requirements for physical implementation, but detailed database engineering, platform configuration, performance tuning and application development are separate implementation activities unless explicitly included in scope.
Not Sure Whether You Need Logical Architecture, Data Modelling or a Broader Target-State Design?
Share the transformation objective and the artefacts you already have. The initial scoping discussion can identify the narrowest useful starting point and the handoffs required.
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Why Consider DataConsultant for Logical Data Architecture
The engagement is designed to connect business semantics, architecture discipline, governance requirements and implementation realities without treating any one vendor schema as the enterprise truth.
Business meaning first
Start with business terms, decisions and domain context before introducing technology-specific structures.
Architecture depth without platform lock-in
Define enough structure to guide delivery while keeping the logical layer portable across database and cloud choices.
Governance integrated into models
Connect ownership, quality, classification, lifecycle and control expectations directly to the information architecture.
Traceability to delivery
Carry approved logical concepts into sources, interfaces, products, analytics and physical design with documented mappings.
Decision records and change control
Document assumptions, trade-offs, exceptions and review ownership so architecture can evolve without losing intent.
Handover built into the scope
Produce artefacts, standards and review guidance that internal teams and existing vendors can continue to use.
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Logical Data Architecture Consulting FAQs
Answers to common enterprise architecture, governance, delivery and procurement questions about this service.
What is logical data architecture?
Logical data architecture is a technology-agnostic blueprint for how an organisation structures business information across domains, entities, attributes, relationships, identifiers, business rules and shared definitions. It creates a stable information structure that can guide detailed data modelling, integration, governance, analytics and physical platform design without assuming a specific database or vendor implementation.
How is logical data architecture different from a logical data model?
A logical data model usually describes entities, attributes, keys and relationships for a defined subject area. Logical data architecture is broader: it places those models within enterprise data domains, ownership boundaries, shared concepts, information flows, standards, governance requirements and cross-domain dependencies. A logical model can therefore be one artefact within a wider logical architecture.
How does logical data architecture differ from physical data architecture?
Logical architecture focuses on business meaning, information structure and relationships independently of a specific database or platform. Physical architecture adds implementation choices such as schemas, tables, storage formats, indexes, partitions, platform services, deployment patterns and performance engineering. The logical layer should provide traceable requirements for those physical choices.
When should an organisation create or refresh a logical data architecture?
Common triggers include conflicting definitions across business units, duplicated domain models, ERP or CRM transformation, data-platform modernisation, mergers, enterprise analytics or AI programmes, master-data initiatives, integration renewal, regulatory remediation and repeated difficulty agreeing authoritative data sources or ownership.
What is included in DataConsultant’s logical data architecture service?
Scope can include business vocabulary alignment, domain and subject-area boundaries, logical entity and relationship design, attribute and identifier principles, reference-data treatment, shared concepts, cross-domain dependencies, information-flow requirements, quality and control requirements, model standards, decision records, traceability and implementation handover. Final scope is agreed during discovery.
What deliverables can we expect?
Typical outputs can include a logical architecture blueprint, domain and subject-area map, logical entity catalogue, entity-relationship views, shared identifier and reference-data principles, business-rule register, glossary alignment, source-to-concept traceability, architecture standards, decision log, issues and assumptions register, and a handover pack for detailed physical design or implementation.
Who should participate in the engagement?
Participation typically includes data and enterprise architects, business-domain representatives, data owners and stewards, application and integration architects, data engineers, analytics teams, governance and metadata leads, security and privacy representatives, and programme or product leaders. The exact group depends on the domains and decisions in scope.
Does the service require us to choose a database or cloud platform first?
No. Logical architecture should remain independent of a specific storage technology wherever practical. Existing and planned platforms are still considered because they create constraints and implementation implications, but the logical structure is intended to preserve business meaning and design intent across technology choices.
How are data governance, quality, privacy and security handled?
The logical architecture can record ownership boundaries, critical data concepts, classification needs, quality expectations, lineage requirements, access principles, retention constraints, reference-data responsibilities and control points. It does not replace legal advice, statutory audit, certification, penetration testing or specialist regulatory assessment unless separately commissioned.
What information should we prepare before the engagement?
Useful inputs include current data models, business glossaries, application and integration inventories, canonical messages or APIs, reporting definitions, master and reference data, data-quality issues, architecture diagrams, policies, known ownership gaps, transformation plans and access to domain experts who can validate business meaning and rules.
How long does a logical data architecture engagement take?
The timeline is confirmed after scoping. It depends on the number of domains and subject areas, model depth, stakeholder availability, quality of existing definitions, cross-domain dependencies, required workshops, review cycles, governance obligations and whether implementation mapping or physical-model assurance is included.
How is logical data architecture pricing calculated?
DataConsultant does not publish a fixed fee for this service. Pricing is scope-led and depends on the number of domains, subject areas and systems, existing model quality, workshop and stakeholder count, required modelling depth, traceability requirements, governance and control needs, deliverable pack, implementation support and review or assurance coverage.
Can DataConsultant work with our existing models, standards and tools?
Yes. The engagement can start from existing conceptual, logical or physical models, data catalogues, business glossaries, modelling standards and architecture repositories. Existing artefacts are reviewed for relevance and consistency rather than replaced automatically, and the final handover can be structured around the client’s approved tooling and governance processes.
Can DataConsultant support physical design and implementation after the logical architecture is approved?
Yes. Follow-on support can be scoped for detailed data modelling, database design, integration architecture, target-state architecture, platform design, metadata and lineage enablement, architecture assurance, implementation review and knowledge transfer. Responsibilities and acceptance criteria should be agreed before implementation work begins.
Scope Your Requirement
Tell Us Which Data Definitions, Domains or Architecture Decisions Need to Be Resolved
Use the initial enquiry to describe the transformation context, domains in scope, current modelling artefacts and the decision you need the logical architecture to support.
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Business objectivePlatform modernisation, ERP/CRM change, analytics, AI, M&A, governance or another transformation driver.
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Domain coveragePriority domains, subject areas, entities or shared concepts that need alignment.
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Current artefactsLogical or physical models, glossaries, source maps, integration schemas and known definition gaps.
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Required handoverArchitecture pack, implementation traceability, physical-design guidance, governance or assurance support.
01* Required fields
Request a Logical Data Architecture Scope Review
Share your contact details and requirement. DataConsultant can review the likely scope, required evidence, stakeholder involvement and appropriate next step.