Translate business meaning, application requirements and workload realities into conceptual, logical and physical data models that engineering teams can implement, test, evolve and operate with confidence.
Final scope, duration and commercial terms are confirmed after discovery and review of the required domains, systems, workloads, controls and deliverables.
Connect business concepts and requirements to entities, relationships and implementation choices.
Make identifiers, constraints, validation and sensitive-data considerations explicit before build.
Design indexes, partitions and access structures against real workload needs and growth assumptions.
Leave teams with models, standards, decisions and implementation notes they can maintain.
Data modeling and database design can range from a focused review to a multi-domain implementation programme. DataConsultant pricing is therefore scope-led rather than presented as a fixed public package on this page.
Review an existing model, schema or database design before migration, scale-up, integration or major application change.
Best for: Architecture assurance, technical debt, design risk or pre-modernisation decisions.
Translate business concepts, data requirements and workload needs into conceptual, logical and implementation-ready physical designs.
Best for: New applications, data products, analytical platforms and shared enterprise datasets.
Refactor legacy models and database structures while planning coexistence, mappings, validation and transition constraints.
Best for: Cloud migration, platform replacement, consolidation and legacy database redesign.
Provide specialist review, modelling standards and database design guidance alongside internal engineering and architecture teams.
Best for: Multi-team programmes that need repeatable models, standards and design governance.
The service is useful when inconsistent meaning, weak integrity, legacy structures or workload changes are beginning to slow engineering, migration, integration or analytics.
Customer, product, account, order or asset definitions diverge across applications and reporting, creating reconciliation and integration friction.
Missing or inconsistent identifiers, constraints and relationship rules make duplicates, orphan records and ambiguous joins harder to control.
Access paths, indexes, partitions or schema shape no longer match real transaction, reporting or data-product workloads.
Tightly coupled structures, duplicated logic or undocumented dependencies make cloud migration, consolidation or platform replacement risky.
Each interface reinvents structures and definitions because no shared canonical model or stable producer-consumer contract exists.
Engineers can see tables or collections but not the business definitions, assumptions, trade-offs, ownership or change rules behind them.
Use a focused model and database design assessment to identify structural risks, missing integrity rules, performance design issues and high-priority remediation before they become programme dependencies.
Data modeling turns business meaning and data requirements into explicit structures. Database design takes those structures into implementation by selecting data types, keys, constraints, indexes, partitions, storage patterns and technology-specific choices that support the expected workload.
The engagement links business semantics to engineering decisions so that models can support applications, integrations, data platforms, analytics and operational ownership without treating the database as an isolated technical component.
Select only the modelling depth and database design activities required for the decision, build stage or modernisation programme.
Define business entities, relationships, domains and core concepts without locking the design to a particular database technology.
Translate business meaning into structured attributes, identifiers, relationships, cardinality, optionality and integrity rules.
Convert the logical model into deployable structures that reflect platform capabilities, workload patterns and operational constraints.
Design facts, dimensions, grains, conformed structures and analytical relationships for reporting and decision-support workloads.
Design maintainable relational schemas with explicit integrity, transaction and query requirements.
Use non-relational patterns when access paths, hierarchy, scale, relationship traversal or flexibility justify them.
Create shared structures and definitions that improve interoperability across applications, integrations, analytics and data products.
Improve legacy designs and introduce modelling standards, review criteria and controlled evolution for future change.
Outputs are tailored to scope. The goal is to leave a traceable design package that can support build, review, migration, testing and future maintenance.
Business concepts, critical data, use cases, access patterns, volumes, lifecycle, controls and non-functional requirements.
Business-level entities, relationships, domains and scope boundaries for stakeholder validation.
Attributes, identifiers, relationships, cardinalities, normalization choices and integrity rules.
Implementation-ready tables, collections or graph structures aligned to the selected database technology.
Definitions, data types, ownership context, naming conventions and model documentation.
Primary and foreign keys, uniqueness, validation rules, nullability and referential integrity design.
Performance-oriented structures based on workload evidence, access patterns, concurrency and expected growth.
Classification, access, retention, deletion, auditability and sensitive-data considerations that affect the schema.
Source-to-target mappings, transformation notes, reconciliation requirements and coexistence considerations when migration is in scope.
Assumptions, trade-offs, rejected options, review decisions, deployment guidance and knowledge-transfer material.
The process keeps business meaning, engineering constraints, control requirements and implementation decisions connected from discovery through handover.
Confirm business goals, data domains, systems, consumers, constraints, controls and workload requirements.
Create conceptual and logical structures; validate entities, definitions, identifiers, relationships and rules.
Translate the logical model into physical structures aligned to the target database and operating environment.
Review integrity, access patterns, performance design, security, lifecycle, mappings and non-functional requirements.
Support schema implementation, migration mapping, design reviews, testing and change decisions when included in scope.
Provide models, standards, decision records, documentation and knowledge transfer for accountable ownership.
Scope the physical design, mappings, integrity rules, performance considerations, design decisions and handover evidence required by the teams that will implement and operate the database.
Clear fit criteria help keep the work focused on data structures, database decisions and implementation evidence rather than turning the engagement into a generic architecture programme.
A strong design uses the simplest pattern that meets the required consistency, access, scale, relationship, security and operating needs. Different workloads may justify different serving structures.
Useful when integrity, relationships, transactions and well-defined schemas are central.
Useful when business measures, historical analysis and consistent reporting grains are central.
Useful when data shape, access boundaries or scale justify document, key-value or wide-column approaches.
Useful when relationship traversal, connected data or semantic interoperability is the dominant requirement.
The design can work with existing or planned relational, cloud data, warehouse, lakehouse, document and graph technologies. Platform-specific choices are assessed against workload fit, integration, security, resilience, operational skills, lifecycle and cost visibility rather than assumed vendor allegiance.
Database design should expose the controls that affect data structure and access instead of leaving them as undocumented implementation assumptions.
Keys, uniqueness, cardinality, nullability, referential integrity and validation requirements.
Classification, least-privilege implications, restricted attributes, masking or segregation requirements where relevant.
Creation, effective dates, historical treatment, retention, deletion, archival and auditability needs.
Source-to-target traceability, transformation assumptions, canonical mappings and producer-consumer dependencies.
Versioning, compatibility, model review, migration paths and decision records for schema change.
Inputs do not need to be perfect, but the engagement needs enough business and technical evidence to distinguish facts from assumptions. Missing information should become an explicit limitation or action.
The modelling method stays consistent, while entity structures, lifecycle rules, access patterns and control needs vary by business domain and sector.
Account, customer, transaction, policy, product, risk and reference-data structures with strong lineage and integrity expectations.
Patient, provider, study, product and operational data requiring careful semantics, access boundaries and lifecycle treatment.
Customer, product, catalog, inventory, order, promotion and channel models spanning transactions and analytics.
Asset, material, supplier, production, quality, logistics and event structures across operational and analytical systems.
Tenant, user, subscription, product, usage, entitlement and telemetry models that need controlled evolution at scale.
Subscriber, service, network, content, event and interaction models supporting high-volume operational and analytical use.
Citizen, student, programme, service, case and institutional data with definition, access and lifecycle requirements.
Client, engagement, finance, workforce, project, supplier and reference models shared across business functions.
Bring the workload, integration, control and operating requirements into the decision so the physical model and database pattern are justified by evidence rather than vendor preference alone.
The engagement is structured around explicit requirements, traceable decisions, implementation realities and practical ownership rather than producing diagrams in isolation.
Start with the meaning, ownership and use of data before selecting physical structures or database-specific mechanisms.
Assess technology choices against workload, integration, security, resilience, operating skills and lifecycle needs.
Document assumptions, alternatives, trade-offs and review decisions so future teams can understand why the model looks the way it does.
Include integrity, classification, access, lifecycle, lineage and change requirements where they materially affect database design.
Connect models to schema structures, migration mappings, review criteria, testing needs and handover evidence where included in scope.
Leave internal teams with models, standards, definitions and decision records they can own and evolve after the engagement.
Answers to common buyer questions about modeling depth, database patterns, scalability, integrity, platforms, documentation, duration, pricing and collaboration.
Share your contact details and requirement. DataConsultant can review the likely modeling depth, evidence, stakeholders, target technology considerations and appropriate engagement shape.
