Data, platforms & governance
“My data is scattered and I can’t use it.”
Before you talk about AI, you need usable data. We turn your scattered data into a reliable, well-governed asset, so every project built on it (AI or not) starts on solid foundations.
Turning scattered data into a usable asset.
We cover the whole chain: collection, quality, catalogue and governance, so your teams can finally trust their data.
- Designing pipelines to collect, transform and consolidate data.
- Setting up data catalogues and quality frameworks.
- Governance and lineage: traceability, compliance and access control.
- Data platform architecture (data lake, data warehouse, data mesh).
The kind of projects we deliver.
Consolidating scattered data
Consolidating data from disparate legacy systems into a single repository you can actually use.
Catalogue with automated rules
Setting up a data catalogue with automated quality rules and monitoring of data freshness.
Data mesh architecture
Designing a data mesh architecture for a multi-site group, with governance shared across business teams.
Examples of the kind of projects our teams deliver.
Focused on results
A pragmatic approach built around real results and efficiency, not technology for show.
Scoping first
We define the project before we talk about tools, so we avoid dead ends.
Real-world delivery
We take solutions all the way to production, not just to prototype.
Plain speaking
Complex topics made clear and practical, with no needless jargon and no overpromising.
The questions we hear most often.
Our data is spread across several systems. Where do we start?
With a review of your existing sources, to identify what data is genuinely usable and prioritise use cases. We then consolidate it through suitable pipelines, without waiting for a full overhaul before delivering value.
What is a data mesh, and is it right for every company?
A data mesh is an architecture that decentralises data ownership by business domain, which helps multi-site groups with varied needs. It isn’t always the answer: we only recommend it when the structure of your organisation justifies it.
How do you guarantee regulatory compliance of our data?
Through governance and lineage: flow traceability, access control and quality frameworks, designed in from the start of the platform rather than bolted on afterwards.
Do we need a data catalogue before we start with AI?
It isn’t strictly required, but a catalogue with automated quality rules speeds up every AI project that follows, because you spend less time making data reliable for each new use case.
Do you work alongside our existing data team, or independently?
Either. We join your existing teams where they exist, or work independently on a defined scope with a regular point of contact.
Give your data a solid foundation?
Tell us about your context. We’ll get back to you shortly to identify priorities.
