PIPELINES · MODELS · TRUST
Numbers the whole company agrees on.
Most analytics problems are definition problems. Three teams report three revenue figures because each built its own query. We fix the modelling layer first, then the dashboards are almost incidental.
- Defined
- One metric, one definition, documented
- Tested
- Data quality checks in the pipeline
- Traceable
- Lineage from dashboard to source
THE SHORT VERSION
The modelling layer is the product
A dashboard is a view. The durable asset is the layer underneath: cleaned, conformed tables with tested definitions and documented lineage. Get that right and any BI tool works; get it wrong and no BI tool helps.
We build that layer with tests attached, so a broken upstream change fails the pipeline rather than quietly producing a wrong number on a Monday morning.
The difference is where the effort goes.
Dashboard-first
- Three teams, three definitions of the same metric
- Logic duplicated inside each dashboard, diverging over time
- Silent breakage — the chart still renders, the number is wrong
- Nobody can say where a figure came from
Model-first
- A single modelling layer with one tested definition per metric
- Dashboards as thin views over conformed tables
- Pipeline tests that fail loudly on bad or missing data
- Documented lineage from every figure back to its source
WHAT IT INCLUDES
Scope, in plain terms.
Every engagement is shaped to the problem, but this is the ground it typically covers.
Data pipelines
Batch and streaming ingestion with retries, backfill and idempotency.
Warehouse modelling
Conformed, tested tables with documented metric definitions.
Business intelligence
Dashboards built on the modelling layer, designed around actual questions.
Data quality
Freshness, volume, schema and distribution tests that run continuously.
Event tracking
A governed event schema so product analytics stay comparable over time.
Migration
Moving off legacy reporting without losing the history it holds.
What you get
- Source inventory and ingestion pipelines
- Modelled warehouse layer with tests and documentation
- Metric definitions agreed and version-controlled
- Dashboards for the questions that prompted the work
- Monitoring for freshness, volume and schema drift
QUESTIONS
The things people ask first.
Do we need a warehouse, or is our database enough?
If reporting queries are slowing your production database, or you need to join across systems, you need a warehouse. Otherwise, often not yet.
Which tools do you use?
PostgreSQL and the managed warehouses on GCP and Azure, with standard SQL transformation tooling. We avoid anything that locks the modelling layer to one vendor.
How do you handle disagreement about definitions?
We surface it early and force the decision, in writing. The disagreement already exists — the modelling work just makes it visible.
Often paired with
- 08Cloud & Backend EngineeringScalable APIs, distributed systems and cloud infrastructure engineered for reliability.
- 06Machine LearningPredictive models, recommendation systems and intelligent optimization.
- 02Custom SoftwareBusiness-specific software designed around your workflows, data and operational needs.
Need data & analytics?
Tell us the problem and the constraint. We will tell you what it takes — and whether this is even the right service for it.