Data the business can trust and actually use.

Start from decisions, not data

Data projects stall when they start by collecting everything. We start from the decisions people need to make and the questions behind them: which customers are at risk, which jobs lose money, where stock will run out. Each question points to the data needed to answer it. That keeps the first release small and the value visible.

One definition per metric

Most arguments about numbers are arguments about definitions. "Revenue" can mean booked, invoiced or paid; "active customer" can mean several things. We write each important metric down once, with its owner, its calculation and its source, and build it in one place so every report uses the same logic. When the definition changes, it changes everywhere.

The table above is illustrative; your definitions come from your teams.

Pipelines that can be trusted

Pipelines move data from source systems into a model built for analysis. The parts that make them dependable are not glamorous: automated checks for missing, duplicated or out-of-range values; alerts when a source stops sending; a record of where each figure came from; and tests on transformations, like any other code. A dashboard that is quietly wrong is worse than no dashboard, because people act on it.

Do you need a warehouse?

Not always. A small business with two or three sources can often report directly from a well-structured database. A warehouse earns its place when several systems need combining, history must be kept as sources change, or reporting load is slowing the operational systems. We size the architecture to the questions and the volume, and leave room to grow.

Access and governance

Analytical data often combines information from many systems, so it needs its own access rules. People see the data their role requires, personal data is minimised or pseudonymised where the analysis allows, and access is reviewed. Under UK GDPR, security measures should be appropriate to the risk and tested regularly; the ICO's guide to data security sets out what that involves.

Ready for AI

The same foundations make AI work possible: clean, defined, permissioned data with known sources. Teams that skip them find their AI pilots stall on data problems rather than model problems.

Batch or real time

Most reporting is well served by data refreshed every few hours or overnight. Real-time pipelines cost more to build and run, so we reserve them for decisions that genuinely cannot wait, such as fraud checks, stock alerts or live operational dashboards. Asking how quickly someone will act on a number usually answers the question.

Owning it afterwards

Data platforms need someone to look after them. We document each pipeline, metric and data source, set up alerts that reach the right owner, and work alongside your analysts so they can add new sources and reports themselves. The goal is a platform your team runs, not one that depends on us.

An example

A logistics business reports from spreadsheets compiled every Monday. The first release connects the order, delivery and finance systems to a small warehouse, defines six metrics with their owners, adds quality checks and replaces the Monday spreadsheet with a dashboard updated each morning. The second release adds margin by customer, the question the leadership team asked first.