Agree the definitions
What counts as a customer, an order, revenue recognised. Written down and signed off before anything is modelled.
Reporting the business can act on.
When reporting stops being trusted, the cause is data spread across too many places with no agreed version of the truth. We build the layer that resolves it.
Where trust in the numbers goes.
Two teams present revenue for the same quarter and the figures differ by four percent. Both are defensible. They come from different systems, filtered on different dates, using definitions that were never written down.
The usual response is another dashboard, which adds a fourth number to the argument. The problem is not presentation. It is that revenue has no single definition and no single place it is calculated.
A data platform fixes the layer underneath: one model, one set of definitions, one place reporting reads from, and a record of where every figure came from.
What we build.
A warehouse sized to the actual data, pipelines that load it on a schedule the business can rely on, and a modelling layer where the definitions live. Reporting tools sit on top of that and stay interchangeable, so the platform outlives whichever BI product is current.
Includes
- Warehouse and lake architecture
- ETL and ELT pipeline development
- Dimensional modelling and metric definitions
- Business intelligence and reporting layers
- Governance, lineage, and access control
- Historical loading and data quality monitoring
How the work runs.
Architect for the volume
Warehouse, storage, and load pattern chosen against the data you have and the growth you expect, rather than against a reference architecture.
Build the pipelines
Loads that are repeatable and restartable, so a failed run is fixed by running it again.
Publish and govern
Access by role, lineage from figure back to source, and monitoring that catches a load which ran but brought nothing.
Questions we get asked.
Do we need a warehouse, or would a better dashboard do?
If the numbers are trusted and the problem is presentation, a better dashboard is cheaper and faster. If two reports disagree, the problem sits underneath the dashboard and a reporting tool cannot resolve it.
Which warehouse should we use?
It depends on volume, on what your team already runs, and on how predictable the spend needs to be. Postgres carries more businesses than people expect. BigQuery, Snowflake, and Databricks each suit particular shapes of workload, and we set out the running cost of each before the decision.
How long before we see reporting from it?
The first useful reports usually land four to eight weeks in, on a narrow slice such as sales or finance. Loading the rest of the estate continues after that.
What about historical data?
Historical loading is scoped as its own piece of work, because the older the data the more cleaning it needs. How far back to go is a business decision, and we cost it both ways.
Start with the problem.
Describe what is slowing the operation down. We will come back with the approach, the scope, and what it costs.
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