Dataconsultant designs sector-aligned conceptual and logical data models for organisations that need consistent business definitions across applications, integration, analytics, reporting, governance, and AI. We combine domain discovery, modelling, control requirements, and implementation mapping to create a practical foundation that teams can extend, govern, and use across technology environments.
An Industry Data Model Service is a reusable representation of the core business entities, relationships, definitions, rules, and data domains common to a sector. It supports organisations that need consistent meaning across operational systems, data platforms, integration, reporting, governance, and AI. Typical decision-makers include data, technology, architecture, transformation, and business-domain leaders. Deliverables usually include conceptual and logical models, a glossary, entity definitions, relationship rules, mappings, governance responsibilities, and implementation guidance. Value depends on expert participation, reliable source information, controlled extensions, and adoption; the model does not by itself fix poor data, replace system design, or provide legal assurance.
The engagement can be structured around assessment, model design, implementation enablement, or a combination of these. Each phase documents assumptions, responsibilities, dependencies, and decision points.
We examine business capabilities, terminology, systems, reports, interfaces, existing models, data issues, and regulatory constraints.
We define entities, attributes, relationships, cardinalities, reference concepts, lifecycle states, constraints, and controlled extensions.
We connect the model to target databases, APIs, events, analytics layers, metadata tools, quality rules, and governance workflows.
Align business and technology teams around stable definitions for customers, products, agreements, transactions, assets, and events.
Give projects a reusable starting point instead of repeatedly reconciling the same concepts and relationships.
Improve canonical mapping across applications, APIs, events, warehouses, lakehouses, and reporting layers.
Allow local variation while protecting enterprise semantics, ownership, quality rules, and compliance requirements.
Teams use the same term for different concepts, or different terms for the same concept, creating reporting and integration disputes.
Every programme designs entities and relationships independently, increasing delivery effort and technical debt.
Point-to-point transformations embed undocumented assumptions and break when products, channels, or systems change.
Ownership, classifications, rules, lineage, and policy obligations cannot be connected cleanly to business concepts.
Share your priority domains, systems, and intended use cases for a practical scope discussion.
The service is relevant to startups, SMBs, enterprises, public-sector organisations, and regulated businesses where several systems or teams must share consistent industry meaning.
Define stable business concepts before mapping legacy structures to a new ERP, CRM, policy, billing, clinical, commerce, or operational platform.
Create consistent entities, dimensions, relationships, and semantic definitions for reporting, feature engineering, retrieval, and model context.
Establish canonical payload meaning, lifecycle events, identifiers, and mappings across domains and systems.
Connect business terms to ownership, classification, quality rules, lineage, controls, and reporting obligations.
Business capability mapping, domain boundaries, terminology reconciliation, entity identification, definitions, synonyms, lifecycle analysis, business-rule capture, and stakeholder alignment.
Entities, attributes, identifiers, relationships, cardinalities, hierarchies, reference data, code sets, temporal behaviour, extensibility, and canonical exchange structures.
Source-to-target mappings, API and event alignment, dimensional translation, physical-design guidance, metadata registration, quality rules, lineage requirements, and migration traceability.
Ownership, review gates, extension rules, versioning, naming standards, classifications, privacy flags, control mapping, model quality checks, and adoption governance.
| Deliverable | Purpose | Typical content | Acceptance focus |
|---|---|---|---|
| Domain and concept map | Define scope and shared business meaning | Domains, capabilities, major concepts, boundaries, and dependencies | Coverage, clarity, ownership, and agreed exclusions |
| Logical industry data model | Describe entities and relationships independently of technology | Entities, attributes, keys, relationships, cardinalities, constraints, and definitions | Business validity, consistency, extensibility, and traceability |
| Business glossary and code-set catalogue | Standardise terminology and controlled values | Definitions, synonyms, owners, classifications, reference concepts, and status values | Approval, uniqueness, usability, and governance readiness |
| System and platform mappings | Connect the model to implementation | Source-target mappings, transformations, APIs, events, analytics, and migration notes | Completeness, exception handling, and technical feasibility |
| Governance and adoption pack | Support sustainable use | Roles, extension rules, review gates, versioning, QA checks, roadmap, and training material | Accountability, operating fit, and adoption plan |
We can scope a focused domain model, enterprise blueprint, or implementation mapping engagement.
Objective: agree outcomes, scope, domains, use cases, stakeholders, and constraints.
Primary output: engagement charter and evidence plan.
Objective: assess models, systems, terminology, data flows, and known issues.
Primary output: findings, gaps, and modelling priorities.
Objective: define business concepts, boundaries, rules, ownership, and lifecycle.
Primary output: domain map and conceptual model.
Objective: create logical entities, attributes, relationships, constraints, and extensions.
Primary output: reviewed logical industry data model.
Objective: connect the model to platforms, quality, metadata, privacy, and governance.
Primary output: mappings, control requirements, and adoption standards.
Objective: test priority scenarios, resolve issues, transfer knowledge, and plan rollout.
Primary output: accepted model pack and implementation backlog.
The service is vendor-neutral. The exact tools and standards depend on the organisation’s architecture, licences, policies, jurisdictions, and intended model uses.
We can adapt notation, tooling, mappings, and governance to the platforms already in use.
Review a domain, existing model, or programme design and provide findings, risks, and recommendations.
Create a defined conceptual and logical model with glossary, governance, mappings, and handover.
Add experienced modelling capacity to a client-led architecture, platform, migration, or integration programme.
Support model stewardship, extensions, quality reviews, change control, documentation, and capability building.
A multi-system organisation may use an industry model to align customer identity, legal party roles, account relationships, product terms, transaction events, risk classifications, and regulatory reporting mappings. The model would require validation against actual policies, products, jurisdictions, and source data.
A retailer may use the model to reconcile product hierarchies, customer and household concepts, offers, orders, inventory, fulfilment, returns, and channel interactions across commerce, ERP, CRM, analytics, and AI services. Figures and outcomes would be measured after implementation.
A reliable estimate requires scoping. Fixed prices or timelines without understanding domains, evidence, stakeholders, and implementation expectations can be misleading.
Number of domains, entities, attributes, relationships, code sets, jurisdictions, and required conceptual, logical, or physical detail.
Number and quality of existing schemas, systems, interfaces, reports, models, metadata sources, and conflicting definitions.
Availability of subject-matter experts, workshop volume, governance reviews, decision cycles, and regulatory or specialist input.
Mappings, platform-specific design, migration support, tooling, automation, documentation, training, and ongoing governance.
Provide your priority domain, target platforms, intended use, and current documentation for a written proposal.
Models are reviewed with domain experts and implementation teams so they are understandable, usable, and technically feasible.
Recommendations are based on required capabilities and constraints rather than a predetermined platform sale.
Definitions, assumptions, evidence gaps, exceptions, trade-offs, and approvals are recorded for future review.
Deliverables can include standards, walkthroughs, extension rules, review practices, and team enablement.
We will help determine whether you need a focused assessment, a domain model, or broader implementation support.
Define critical entities and attributes, valid values, uniqueness, completeness, relationship integrity, exception handling, ownership, and monitoring expectations.
Identify personal and sensitive concepts, purpose constraints, minimisation, retention, deletion, consent, rights, residency, and sharing considerations.
Connect classification, access boundaries, privileged use, encryption needs, segregation, logging, and third-party access to relevant concepts.
Map sector rules, contractual duties, policy controls, audit evidence, model approvals, change history, and specialist-review requirements.
Dataconsultant’s service does not replace legal advice, statutory audit, formal certification, privacy impact assessment, or specialist cybersecurity testing unless these are explicitly included and delivered by appropriately authorised professionals.
ERP, CRM, billing, policy, clinical, manufacturing, commerce, service, finance, HR, and bespoke operational platforms.
APIs, event streaming, ETL/ELT, data warehouses, lakehouses, master data, metadata, lineage, quality, and orchestration tools.
Semantic layers, BI, metrics, feature stores, knowledge graphs, retrieval systems, model inputs, reporting, and decision-support products.
The following testimonials are representative service-specific examples and should be replaced with approved customer statements before publication.
“The team helped our architects and business leads agree definitions that had been disputed across several programmes. The model was detailed enough for implementation but remained understandable to product owners and governance teams.”
“We needed a practical foundation for product, customer, order, fulfilment, and returns data. The workshops were structured, decisions were documented, and the final mappings gave our platform teams a clearer path forward.”
“Dataconsultant did not force a generic reference model onto us. They used sector concepts as a starting point, tested them against our operating processes, and clearly separated enterprise standards from controlled local extensions.”
“The engagement improved communication between application, integration, analytics, privacy, and business teams. Revision handling was disciplined, and every material change was traceable to a requirement or stakeholder decision.”
“The model review identified several hidden assumptions in our legacy interfaces. The team explained the implications clearly and provided realistic recommendations rather than suggesting an unnecessary replacement of the entire estate.”
“The handover included modelling standards, extension guidance, ownership, and review checkpoints. That made the output more useful than a static diagram and gave our internal team a practical basis for ongoing governance.”
Share the domain, business problem, systems, and intended uses that your model must support.
An industry data model is a reusable, business-oriented representation of the core entities, relationships, definitions, rules, and data domains common to a specific sector. It helps an organisation create consistent structures for operational systems, analytics, integration, reporting, governance, and AI use cases while preserving room for organisation-specific extensions.
An industry data model describes business concepts and relationships independently of a particular database product. A database schema is a physical implementation optimised for a specific workload and technology. The industry model provides common meaning; logical and physical models translate that meaning into implementable structures.
Common triggers include core-system replacement, data-platform modernisation, mergers, inconsistent reporting, duplicate customer or product definitions, regulatory reporting, enterprise integration, master-data initiatives, analytics expansion, and AI programmes that require reliable semantic context.
Scope may include domain discovery, terminology alignment, current-state model review, canonical entity design, relationship and cardinality definition, code-set and reference-data modelling, governance mapping, logical model development, implementation guidance, traceability, documentation, and knowledge transfer. Final scope is agreed during discovery.
Yes. A useful industry model should provide a stable sector foundation while allowing controlled extensions for products, channels, jurisdictions, operating models, customer segments, policies, and legacy constraints. Extensions should be governed so that local needs do not recreate enterprise inconsistency.
Typical participants include data architects, enterprise architects, business-domain experts, application owners, analytics teams, integration engineers, data governance leads, security and privacy specialists, risk and compliance teams, and accountable data owners. Executive sponsorship is often provided by a CDO, CIO, CTO, COO, transformation leader, or business-domain executive.
There is no reliable fixed duration before discovery. Timing depends on domain breadth, number of systems and jurisdictions, availability of subject-matter experts, model depth, source documentation, review cycles, required mappings, governance maturity, and whether implementation support is included.
Pricing is influenced by the number of domains, entity and relationship complexity, workshop volume, stakeholder count, current-state assessment depth, required mappings, regulatory review, tooling, documentation, implementation support, and engagement model. Dataconsultant can provide a written estimate after initial scoping.
Depending on the environment, the work may use entity-relationship modelling, UML class models, dimensional modelling, canonical data models, ontology concepts, data vault patterns, or platform-specific modelling conventions. Naming, metadata, quality, privacy, and security requirements should align with applicable internal and external standards.
The model can identify sensitive entities and attributes, ownership, classification, lawful-use constraints, retention considerations, access boundaries, residency needs, lineage expectations, and third-party dependencies. It does not replace legal advice, a formal privacy impact assessment, a security assessment, or statutory audit unless separately commissioned.
Yes. Mapping can cover source applications, APIs, event streams, warehouses, lakehouses, master-data platforms, reporting layers, and target databases. The depth of mapping depends on available metadata, data quality, system access, and the agreed implementation scope.
Useful measures may include adoption across priority projects, reduced duplicate definitions, model reuse, mapping coverage, fewer integration exceptions, improved data-quality rule coverage, faster design reviews, clearer ownership, reduced semantic reconciliation effort, and stakeholder acceptance. Baselines and attribution limits should be documented.
