Capability
Data pipelines & analytics
Dashboards get blamed for a problem that usually sits upstream — the same figure arriving from three systems with three different values. We start by fixing where the numbers come from, not by building a nicer chart on top of bad data.
Signs this is the right fit
You probably recognise at least one of these.
- Two departments quote different figures for the same month, and both can defend theirs.
- A weekly report depends on a spreadsheet only one person knows how to update.
- Nobody makes decisions from the dashboard because nobody quite trusts it.
How we run it
From three conflicting numbers to one everybody trusts.
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01
Sources
Where each figure originates, how often it updates, and which version people currently believe.
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02
Pipeline
Scheduled, monitored movement of that data — no more exports maintained by one person.
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03
Model
Structured around how the business actually thinks about the numbers, not around how the source system happens to store them.
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04
Report
Dashboards built around the decisions they support, then checked against reality.
The last step feeds the first — this runs as a loop, not a one-way handover.
Scope
What this includes.
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Data pipelines
Scheduled, monitored flows that move data reliably instead of by hand.
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Warehousing
One place where the figures live, modelled the way the business asks questions.
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Business intelligence
Dashboards built for decisions rather than for the number of charts on them.
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Data quality
Checks that catch bad records before they reach a report someone acts on.
What you end up with
Handed over, not hinted at.
- Automated pipelines running in your own environment, with alerting when they fail
- A documented data model showing where every figure comes from
- Dashboards tied to specific decisions rather than to available fields
- Quality checks that flag bad records before anyone reports on them
Typical toolkit
What we reach for here.
Tools we use regularly for this kind of work. The right choice still depends on what you already run.
- Python
- PostgreSQL
- dbt
- Airflow
- Metabase
- Data quality tests
Before you ask
The questions that come up every time.
Do we need a data warehouse?
Not always. Below a certain volume, a well-structured database and scheduled jobs do the same work at a fraction of the cost, and we will say when that is your case.
Can you work with the tools we already pay for?
Usually yes. The trouble is rarely the tool itself — it is how data reaches it. Replacing the tool without fixing that just moves the same problem somewhere newer.
What about personal data?
What is stored, where, and for how long gets decided before the first pipeline runs — GDPR is a design constraint here, not a later review step.
Next step
Let's talk specifics.
Describe the project and the timeline you have in mind. We will tell you plainly what is realistic — including if the honest answer is "not yet."