Modern Data Infrastructure & Governance

Moving to the cloud solves storage and scale. It does not tell you which of the four revenue figures in your warehouse is the one finance signs off, and it tends to make that question harder by making it cheaper to keep all four.

We build the layer underneath that: pipelines that are scheduled and observable, a lake that is organised rather than accumulated, and governance that lives in the platform instead of in a spreadsheet of agreements.

What this includes

• Data lake and warehouse design – raw, staging and serving layers, with rules about what is allowed to move between them
• Ingestion and transformation pipelines: scheduled, monitored, and safe to re-run without duplicating rows
• Lineage, so that for any number on any dashboard there is a path back to the system that produced it
• Access control by role and by column, so analysts get what they need without getting salaries and ID numbers with it
• Data quality checks that run inside the pipeline and fail loudly rather than passing bad rows downstream
• Preparing the estate for AI workloads, which is mostly the unglamorous work of making data findable and consistent

How we run it

Start from the questions, not the sources. We begin with the handful of decisions the business actually makes on data and build back from those. An estate designed to answer everything usually answers nothing on time.

Governance as code. Access rules, retention and quality thresholds live in the repository with everything else, get reviewed, and get deployed. Policy that exists only as a document drifts within a quarter.

One owner per field. Every meaningful field gets a single declared source system and a named owner. This is the least technical part of the work and the part that decides whether the rest of it holds. The wider version of the argument is in the enterprise data paradox.

What you get at the end

• Pipelines running on a schedule, with alerting when they do not
• A catalogue that says what each dataset is, who owns it, and how fresh it is
• Access policies applied in the platform – reviewable and revocable
• A written definition for each metric the business argues about

Where this usually starts

Two versions of the same report disagreeing in a board pack. A migration that finished on time and left nobody able to find anything. Or an AI initiative that stalled the moment it needed clean, labelled, permissioned data, which is most of them.