Our data engineering practice, two years on
Two years ago we made something official that had been quietly true for a while: a significant part of our work had become data engineering. What began as the occasional reporting request had grown into pipelines, warehouses, and dashboards that several of our clients now depend on every day. So we gave it a name, a small dedicated team, and a clear way of working.
From spreadsheets to trustworthy numbers
Most of our data work starts in the same place: a business that is making important decisions from numbers it does not fully trust. The figures come from several different systems, get stitched together by hand in spreadsheets, and nobody can quite explain why two reports disagree. It is a stressful way to run a business.
Our job is to replace that fragility with something dependable. We build pipelines that pull data from each source, clean and combine it in transparent steps, and load it into a warehouse where the important figures are defined once and consistently. When a number looks wrong, we make it possible to trace exactly where it came from, rather than guessing.
Transparency over magic
The principle that guides the data team is the same one that guides the rest of the studio: favour clarity. We would rather write a transformation that an analyst can read and check than one that produces an answer nobody can explain. Every pipeline is tested, and every pipeline is built to fail loudly when something upstream changes, rather than quietly producing nonsense that someone discovers three weeks later.
Two years in, the data team has become one of the busiest parts of the studio, and reporting work now runs alongside our software and cloud engagements as a core service. If your organisation is making decisions from numbers it is not quite sure about, it is exactly the kind of problem we enjoy.
Published by the Arcwell Systems team. Have a question about anything in this article? Get in touch — we're happy to talk.