Data Architecture & Modeling.
Design the data architecture your organization actually needs — logical, physical, and cloud-ready — so the platforms you build on perform reliably and scale without technical debt.
Data Management Services
The Situation — When Clients Come to Us
Organizations come to us for data architecture when:
- Data infrastructure has been assembled ad hoc — system by system, project by project — and now lacks the coherence to support reliable analytics, integration, or governance
- A major platform decision is approaching — cloud migration, data warehouse modernization, data lake adoption, or data mesh — and the architecture design needs to be right before the technology commitment is made
- Current data models don’t support the reporting, analytics, or operational system requirements the business now has — because they were designed for a smaller, simpler organization than the one that exists today
- An ERP, CRM, or enterprise platform implementation requires data modeling and architecture design work before source system integration can be properly specified
What We Do — Our Approach
// Phase 1
Architecture Assessment
We assess the current data architecture — systems, data flows, data models, integration patterns, storage approaches, and performance characteristics — against current and projected requirements. We identify the structural issues creating performance bottlenecks, integration failures, and scalability constraints.
// Phase 2
Conceptual & Logical Data Modeling
We develop the conceptual data model — the authoritative map of the organization’s key data entities, relationships, and business rules — and translate it into logical data models for the priority data domains, independent of any specific technology implementation.
// Phase 3
Target Architecture Design
We design the target data architecture — including storage approach (data warehouse, data lake, data lakehouse, data mesh, or hybrid), platform selection advisory, integration architecture, and the governance and security controls embedded in the design. We provide vendor-neutral platform evaluation and selection recommendation.
// Phase 4
Physical Data Model Design
We develop physical data models for the target environment — schema designs, indexing strategies, partitioning approaches, and performance optimization specifications — calibrated to the chosen platform and the organization’s analytical and operational workload requirements.
// Phase 5
Implementation Guidance
We provide the architecture and modeling documentation package that enables the client team or technology partner to implement the target architecture — including design authority support during implementation to ensure fidelity to the architecture.
What You Get — Deliverables
- Data architecture assessment report with gap analysis
- Conceptual data model — entities, relationships, and business rules
- Logical data models for priority data domains
- Target-state data architecture design — storage, platform, integration, governance, and security
- Vendor-neutral platform evaluation and selection recommendation
- Physical data model designs for the target environment
- Data architecture standards and design guidelines documentation
- Implementation guidance package for the client or technology partner
- Architecture review and quality acceptance criteria for implementation
The right data architecture is the difference between a platform that performs and one that needs to be rebuilt in two years.
Every Iknow architecture engagement is built around your specific requirements, constraints, and growth plans.