Outsource Your Data Modeling
Focus on Business Outcomes — Not Data Models
Outsource data modeling to a specialist team focused on business meaning, governed change, and scalable delivery. Platform‑agnostic, migration‑ready, and built for outcomes.
Your Data Is Your Business Edge
Your data is not generic — and neither is your business advantage.
Every organization creates value in different ways, shaped by its unique processes, decisions, and operating model.
That’s why effective data modeling can’t be templated or driven by tools alone.
We take our combined experience across industries, data domains, and operating models and focus it entirely on what you need — ensuring your data models reflect how your business creates value and help you maximize that value over time, with confidence and control.
Data Modeling Is a Specialist Business Skill — Not a Tooling Task
Effective data modeling isn’t about choosing the right database, warehouse, or query language.
It’s about deeply understanding business context, data content, and meaning — and translating that understanding into managed data models the organization can rely on.That capability is a specialist skill.
Our dedicated data modeling staff focus on this discipline day in and day out:
- Understanding how your business actually operates
- Interpreting source data in its real‑world context
- Defining shared business concepts, entities, and relationships
- Managing how meaning evolves as the business changes
We don’t rotate data modeling between delivery tasks.
We don’t treat models as side artifacts of engineering work.
This is our core focus.
Platform‑Agnostic by Design
We deliberately do not specialize in:
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Specific cloud platforms
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Database engines
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SQL dialects
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Vendor tools or frameworks
These technologies change. Business meaning does not. By staying platform‑agnostic, your data models:
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Outlast technology decisions
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Remain stable as tools evolve
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Can be implemented across warehouses, lake houses, and analytics platforms
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Act as a durable contract between business and delivery teams
We model your business, not your current database.
Change Management and Data Migration — Built In
Data models don’t stand still. They evolve as businesses change, systems are replaced, and data is migrated.
That’s why change management and migration are built into our managed data modeling approach, not treated as one‑off projects.
Managed Change, Not Reactive Fixes
We manage change deliberately:
- Assessing impact before changes are introduced
- Versioning model evolution over time
- Preserving backward compatibility where required
- Making change explicit, traceable, and intentional
This avoids silent drift and downstream breakage.
Data Migration as a Modeling Discipline
Migration is not just technical — it’s semantic. We anchor migrations to business‑focused data models:
- Mapping source and target systems to shared business entities
- Reconciling meaning explicitly
How It Works
Our process integrates directly with your business and technology teams and fits into your existing delivery lifecycle
We Start With Business Outcomes
We interview your business stakeholders and analytics teams to understand:
- The outcomes they are accountable for
- How decisions are made
- Which data truly matters
- Where meaning, definitions, or trust break down
This ensures models are grounded in real business context, not assumptions.
Changes Are Incorporated Into Ongoing Model Design
Insights are translated directly into your managed data models.
Data modeling is treated as an ongoing discipline, not a one‑time deliverable.
Models evolve deliberately as your business evolves.
Models Are Delivered in System‑Ready Formats
We deliver data models in the formats your systems already require, enabling direct integration
This includes:
- SQL (DDL, views, constraints)
- JSON or XML schemas
- CSV‑based structures
- Other structured formats required by your platforms
No forced technology adoption or changes
You Certify Changes Before Production
All changes are:
- Delivered to lower environments first
- Reviewed and certified by your teams in your environment
Only once accepted are changes promoted to production by your teams, ensuring confidence, control, and traceability.
We Cover the Full Data Pipeline
Based on your requirements, we can manage data design across one or all stages of the pipeline, including:- Raw Data Sourcing- External vendors selection and external operational systems connectivity
- Normalization and Transformation Mapping - Mapping source data into governed, business‑aligned models
- Target "Managed" data models Design - Stable logical models feeding warehouses, analytics, and AI
- Business‑Ready Data Products - Designed for reporting, analytics, and operational use
- Data Quality Rules - Embedded validation aligned to business meaning
Features
Business‑Led Data Requirements
Capture what the business is trying to achieve with data, not just structures. Outcomes, decisions, and success criteria are defined explicitly and carried forward into design and delivery.
Governed Domains & Logical Data Models
Design your logical data models optimizing for consistency, reuse, and long‑term stability across domains.
Business‑Facing Data Products
Once data is managed well, data products are designed for how the business wants to use it.
Living Data Dictionary & Reference Hub
The DDA provides a single, accessible, always‑up‑to‑date source of truth for data definitions, relationships, and context. This is not dead documentation; it is the live collaboration surface through which teams design and iterate data together.
Data Quality Defined at Design Time
The headline and subheader tells us what you're offering, and the form header closes the deal.
Data Quality Defined at Design Time
Define data quality rules directly against logical models in one place. Quality expectations are clear, shared, and reusable across pipelines and data products.
Automated Physical Schemas & Pipelines
Approved logical designs are automatically translated into physical schemas and transformation pipelines aligned to your target platforms and standards. Schemas are ready to be committed to code repositories and CI/CD pipelines, seamlessly integrating with existing release management processes.
Version Control & Full Traceability
Every change — from business requirement to schema — is version‑controlled and traceable. See what changed, why it changed, who approved it, and what it impacts downstream.
Schema & Contract Validation for Integrations
The DDA acts as a schema and contract authority, validating integrations before changes reach production. Breaking changes are detected early — not after incidents occur.
Screenshots
Data Standards
Data Products.
