Data Platforms & Analytics
Every organisation past a certain size has dashboards nobody believes. The fix is rarely a new tool. It is definitions people agree on, pipelines that fail loudly, and a model someone is accountable for.
Trust is the actual deliverable
When two reports disagree about revenue, the cost is not the reconciliation meeting. It is that every number in every report is now provisional, and decisions quietly revert to intuition. Rebuilding that trust is slow, which is why it is worth protecting deliberately.
So we start with definitions. What counts as an active customer, when a booking is recognised, how refunds are handled. These are business decisions rather than technical ones, and getting them written down and agreed is most of the work. The pipeline is what enforces the agreement afterwards.
What we build
Ingestion
Reliable movement from operational systems, SaaS tools and third-party feeds, with schema changes detected before they quietly corrupt a downstream table.
Modelling
A warehouse layered from raw through cleaned to business-defined marts, so analysts work against stable definitions instead of rediscovering the joins each time.
Testing and observability
Freshness, volume, uniqueness and referential checks running on every load. When something breaks you hear it from the pipeline, not from a director looking at a wrong chart.
Reporting
A small set of well-built dashboards against the modelled layer, plus self-service for teams who need to ask their own questions without starting from raw tables.
Governance
Documented lineage, access aligned to your obligations, and retention rules that are enforced rather than aspirational.
Cost control
Warehouse spend is easy to lose control of. We partition sensibly, schedule deliberately, and make consumption visible per team.
How an engagement runs
Find out what people already distrust
We start with the numbers people quietly recalculate in spreadsheets. Those are the definitions that are broken, and they tell you more than any tooling audit.
Agree definitions in writing
Revenue, active user, churn. The argument is the work; the SQL that follows is the easy part. Definitions live in the repository, versioned, with an owner's name on them.
Build one pipeline properly
Tested, monitored, documented lineage, end to end from source to a dashboard someone will use on Monday. One good pipeline is a template; six half-built ones are a liability.
Turn on the alarms
Freshness, row counts, distribution shifts. Alerts that reach a person who can act, rather than a channel nobody reads.
Hand over the definitions, not just the code
The team that owns the business question owns the model. We leave when they can change a definition and see it flow through without asking us.
What you are left with
Modelled warehouse
Layers that are documented, tested on every load, and readable by an analyst.
Definition catalogue
Metrics with owners, in version control, matching what the dashboards actually compute.
Lineage
Source system through to chart, so a wrong number can be traced in minutes rather than days.
Cost ceiling
Warehouse spend understood per model, with the expensive queries identified and capped.
Treating data work as engineering
Transformations belong in version control, get reviewed before merge, and run through the same deployment pipeline as any other code. Analysts work in a development environment rather than editing production. None of this is novel in software delivery, and it remains surprisingly rare in data teams.
Adopting it is what makes a platform maintainable by people who were not there when it was built.
- All transformation logic in version control and code review
- Automated tests on every load, with alerting that reaches a human
- Separate development, staging and production environments
- Documented lineage from source system through to dashboard
When not to hire us
If three people need a weekly number, you do not need a data platform. You need a scheduled query and somewhere to put the result. We will happily set that up and then get out of the way.
And if the disagreement in your organisation is about what the numbers mean politically rather than technically, no pipeline will settle it. That work needs a decision from someone with the authority to make it — before the engineering starts, not after.
Common questions
If reporting queries are slowing your production system, if you need to join data across several systems, or if you need history your operational schema overwrites, then a warehouse earns its place. Below that, it is overhead. Plenty of organisations are well served by a read replica and some discipline, and we will say so.
Related work
Bring us the part that is stuck.
Send the constraint you are working around and we will come back with an approach, a rough shape of the work, and what it would take to hand it over.