SERVICES
Good data models make everything else easier.
We help you design data models that accurately reflect your business — so your DWH is easy to query, easy to maintain, and easy to explain to the people who use it.
A bad data model is expensive. Usually in ways you don’t notice until later.
Data models that don’t reflect business reality create problems that compound over time: BI reports that require complex workarounds, pipelines that are hard to change, and analytical results that nobody fully trusts. Good data modelling is part technical discipline, part business analysis. You need someone who understands both — and who takes the time to get the business rules right before designing the structure.
WHAT WE BUILD
Four modelling capabilities.
01
Conceptual & Logical Modelling
Start with the business. The database comes second.
We work with business stakeholders and data teams together to define what data exists, what it means, and how it relates — before any technical decisions are made. This is where the most important data conversations happen.
02
Physical & Dimensional Modelling
Optimised for the platform and the queries your BI layer actually needs.
We translate logical models into physical designs optimised for the target platform — whether that’s a traditional relational DWH, a cloud-native platform, or a hybrid. Star schemas and snowflake schemas designed for fast, intuitive BI.
03
Data Vault Modelling
When flexibility and auditability matter more than query simplicity.
We’ve built Data Vault models in organisations where source systems change frequently, regulatory auditability is required, or multiple teams need to onboard new data sources independently — without reworking what already exists.
04
Model Review & Remediation
Targeted fixes or broader redesigns — we’ll tell you honestly which it is.
If you have an existing data model that’s causing problems — slow performance, hard-to-maintain ETL, confusing BI logic — we can review it, diagnose the issues, and propose improvements.
HOW WE WORK
We model the business first. The database second.
Data modelling that starts with the database tends to produce technically correct but practically difficult models. We start by understanding the business: what decisions need to be made, what processes generate data, and what questions the data needs to answer. The technical design follows from that — which means it’s much more likely to be right the first time, and much easier to extend later.
STEP 1
Analyse
We spend time understanding the business before designing anything. Entity definitions, business rules, and analytical requirements are captured and agreed first.
STEP 2
Model
We design the logical model with stakeholders, then the physical model for the target platform. Both are documented and reviewed before anything is built.
STEP 3
Validate
We validate the model against real data and real queries. If something doesn’t work in practice, we find out now — not after the ETL is built.
Building a new DWH or struggling with an existing model?
Good data modelling is one of the highest-leverage investments you can make in your data platform. Let’s talk about yours.