TECHNOLOGY SERVICE · 06
Data & Analytics
Pipelines, warehouses, governed metrics and the models that sit on top of them. For organisations where every department reports a different revenue number and the monthly pack takes a week of manual work to assemble.
Pick the decision, then build the pipeline
Data projects fail when they start with a platform and hunt for a use afterwards. Each engagement starts from a decision somebody makes on a schedule: which routes to run, which stock to reorder, which customers to call this week. The pipeline is then built backwards from the data that decision needs.
This keeps the first release small enough to land in weeks and gives you a way to judge whether the platform is earning its cost.
One definition of revenue
Most reporting disputes are definitional, not technical. Metric definitions are written once in a modelling layer, version controlled and reviewed like code, so finance, operations and the board draw from the same logic. Changing a definition becomes a pull request with a visible history.
Tests run on every pipeline execution: row counts, freshness, referential integrity and business rules that should never be violated. A dashboard that is quietly stale is worse than one that is visibly broken.
- Version-controlled metric definitions with named owners
- Automated freshness and quality tests on every run
- Lineage from dashboard figure back to source system
Models earn their place after the basics work
Forecasting, churn scoring and demand planning are worth doing once the underlying data is trustworthy and someone owns the resulting action. Each model is measured against the naive baseline it must beat, and if it does not beat it, the project stops there rather than shipping something decorative.
Access control before dashboards spread
Analytics estates leak through copies: an export to a spreadsheet, a shared link, a personal drive. Row-level access, defined groups and a governed export policy are configured while the estate is small. For personal data, retention rules and pseudonymisation are set at the modelling layer so nobody has to remember them later.
What you get
- Warehouse or lakehouse deployed in your own cloud account
- Ingestion pipelines with scheduling, retries and alerting
- Version-controlled transformation and metric layer with tests
- Reviewed dashboards for the decisions agreed at the start
- Data dictionary and lineage documentation
- Access model with row-level rules and an export policy
- Predictive model with baseline comparison, where one is justified
- Runbook and training for the analysts who will take it on
Typical outcomes
6-8 weeks
To a governed warehouse and first dashboards
1 day
Monthly reporting pack, down from a week
95%+
Pipeline runs passing quality tests
Stack we use
Questions
For many mid-sized organisations a well-modelled Postgres warehouse is sufficient and far cheaper to run. A separate analytics platform earns its place at higher data volumes or when concurrency starts to hurt the operational database.
Sensitive fields are classified at ingestion, pseudonymised where the analysis does not need identity, and covered by retention rules in the modelling layer. Access is granted by role, and exports are logged.
It is normal, and the assessment covers it explicitly. Where a source is too unreliable to model, we say so and either scope a fix in the source system or narrow the first release to the data that can be trusted.
Yes. The warehouse and metric layer are the durable part; the visualisation tool is comparatively easy to change, so we build against what your analysts already know unless there is a strong reason to move.
Related
Next step
Start with a 20-minute call.
Tell us the roles you need filled, the system you need built, or both. You will speak to someone who has done the work, and leave the call with a route forward.