Data & analytics

The starting point we usually find

A business that runs on exports: someone downloads the CSVs on Monday, the spreadsheet does its fragile magic, and by the time the numbers reach a meeting nobody can quite say which system they came from or whether “active customer” means what it meant in last month’s deck. Finance has one truth, operations another, the board a third. Nobody is lying; the sources just never agreed. If that is recognisable, you are the normal case - it is where almost every analytics engagement here begins.

One number, agreed

Most data problems are trust problems. The figures exist, but they disagree, and everyone keeps a private spreadsheet they believe more. Fixing that is less about tools than about care: one model of the business, definitions everyone can read, and pipelines that keep the whole thing current without a human remembering to run anything.

Working back from decisions

We start with the decisions you need to make and work backwards to the data that supports them - hoarding data for its own sake is how warehouses turn into swamps. Sources get consolidated, the model gets documented, and quality checks run continuously so bad data is caught before it reaches a chart.

• Consolidation of scattered, inconsistent sources
• Documented models with definitions people can check
• Quality monitored continuously, not spot-checked

Signs it is time

There is a point where working around the data costs more than fixing it, and most businesses pass it long before they act. The signals are reliable: meetings that open with ten minutes of whose-number-is-right; a person whose actual job has quietly become copying data between systems; decisions delayed a week because the report takes a week; a data team that spends its life answering tickets instead of finding anything out. Each one is a tax, paid monthly. The engagement exists to stop paying it.

• Meetings that start by reconciling versions of the same number
• Someone’s real job is moving data between systems by hand
• Decisions waiting days for reports that should take minutes
• A data team buried in requests instead of doing analysis

How an engagement runs

It opens with a short, fixed-fee discovery: we sit with the people who make the reports and the people who argue with them, trace where each number is born, and write down the definitions everyone thought they shared. Out of that comes a prioritised plan - which sources to consolidate first, which metrics to govern first, what the warehouse needs to look like. Delivery is then incremental: the first governed metrics land within weeks and visibly agree across every view, and trust compounds from there. Nobody waits a year for a big reveal.

• A fixed-fee discovery with a two-week turnaround
• Definitions written down before pipelines are built
• First governed metrics live within weeks
• Incremental delivery, so trust is earned as we go

We work with the spreadsheets, not against them

The reflex in this industry is to treat spreadsheets as the enemy. We treat them as the specification: fifteen years of business rules live in those cells, along with the only numbers anyone actually trusts. For one UK insurer we built the warehouse underneath the finance team’s existing workbooks - same files, same tabs, clean reconciled data arriving overnight instead of being pasted in by hand from nine systems. Nobody was retrained, and within a year the owners had quietly retired half the workbooks themselves, because the warehouse had earned it.

Self-serve, so the data team can breathe

Dashboards and reporting are built for people to answer their own questions, which shortens the queue for the data team and gets decisions made while they still matter. And because the foundation is properly modelled, it is the same foundation you will need if machine learning or AI is on the horizon.

The same foundation AI will need

There is a second return on this work that did not exist a few years ago. Every AI ambition - the assistant, the automation, the model tuned on your history - stands on exactly the foundation described here: consolidated sources, documented definitions, pipelines someone would notice breaking. Clients who do this work first find the AI conversation gets shorter and cheaper; clients who skip it tend to meet us again a year later, one stalled pilot wiser. If AI is on your roadmap, this is not a detour - it is the first mile of the same road.

What you get, and what it costs

The deliverables are concrete: a warehouse modelled for your domain, pipelines that are monitored and safely re-runnable, dashboards designed for self-serve, and a definitions catalogue people can actually read - all documented, in your accounts and your name. Discovery is a fixed fee with a two-week turnaround; build phases are scoped projects from £8,000; ongoing platform work runs as an embedded team from £4,500 a month per engineer. Tooling is chosen for your stack and budget, with no reseller stake behind the recommendation.