Data Solutions
Most organisations do not lack data — they lack agreement about which number is right. We fix the definitions first, build the pipelines that produce them reliably, and put the result where decisions are actually made.
Why reporting is not trusted.
The pattern is familiar: two teams, two numbers for the same thing, and a meeting spent reconciling instead of deciding.
The same metric, defined differently
Revenue, active customer and churn each mean something slightly different per team. Every dashboard is technically correct and no two agree.
Pipelines nobody can explain
Transformation logic accumulated across scripts, scheduled jobs and spreadsheets, with no lineage from source to number and no test when something changes.
Reports built for the meeting, not the decision
Dashboards designed to be presented rather than acted on, answering questions nobody is actually accountable for.
Governance treated as paperwork
Access, retention and personal data handled by policy documents rather than by the platform, so compliance depends on everyone remembering.
Agree the definitions, then build the plumbing.
Define the metrics
We get the handful of numbers that actually drive decisions written down and owned by a named person, before any pipeline is built. This is the part that stops the arguments.
Model the data
A layered warehouse — raw, cleaned, modelled — so lineage from source to metric is traceable and transformation logic lives in one place.
Test the pipeline
Freshness, volume and referential checks that fail loudly. A silent pipeline error is worse than an outage, because the numbers keep being used.
Put it where decisions happen
Delivered into the tools people already work in, with the definition visible next to the number so nobody has to guess what they are looking at.
What you actually receive.
Every item below is an artefact you keep, not a status report about work in progress.
- A metric dictionary: agreed definitions, each with a named owner
- A layered warehouse model with lineage from source system to metric
- Tested, scheduled pipelines with alerting on freshness and volume
- Dashboards built around decisions rather than around available fields
- Access, retention and personal-data handling enforced by the platform
- Documentation and handover so your team can extend the model
Built on a proven stack.
We select technology for fit and longevity, and integrate with the systems you already run.
- Fits your existing systems
- Secure and scalable by design
- Measured against business outcomes
- Supported after launch
The things clients ask first.
It depends on what you are storing and who queries it. Most mid-sized organisations are served well by a straightforward cloud warehouse; a lakehouse earns its complexity when you have significant unstructured data or machine-learning workloads. We would rather right-size this than sell the larger architecture.
Usually, yes. The value is in the modelled layer underneath, not the visualisation on top. Replacing a BI tool people already know is a cost with little return unless it is genuinely blocking you.
The first metrics land early — the sequencing is deliberately narrow so a small set of agreed numbers reaches production before the model is extended. Anything that promises the whole warehouse before the first useful output is sequenced badly.
Handled in the platform rather than in a policy document: classification at ingest, access enforced by role, retention automated. If you operate under a specific regime, we build to it directly instead of to a generic standard.
Have an idea? Let’s forge it.
Tell us where you want to go. We’ll help you get there with the right technology, delivered by a team that ships.