Data & Analytics
Outcomes
Numbers everyone agrees on
A single definition of each metric, documented and applied consistently. Most reporting arguments are definition arguments in disguise.
Reporting built around decisions
Each dashboard tied to a decision someone actually makes, rather than displaying everything available.
Self-service that works
Business users answering their own questions instead of queuing for the analyst.
Reduced manual reporting
The monthly spreadsheet assembly ritual is more common and more expensive than most leadership teams realise.
What we build
Data warehouse or mart design, ETL and transformation pipelines with tested and documented business logic, metric definitions and a semantic layer, dashboards and reporting, self-service enablement, and automated distribution so reports arrive rather than being fetched.
Power BI, Looker, Metabase or Tableau depending on what you have and who uses it. dbt for transformation logic. Snowflake, BigQuery or Postgres depending on genuine scale.
How it works
We start with the decisions: what are people trying to decide, and what would they need to know. Then work backwards to the metrics, then the data. Building forwards from available data produces dashboards nobody opens.
Then pipeline and definition work, then reporting, then enablement and training.
Typical engagement is six to ten weeks.
Where this applies
Strongest where reporting is manual, where numbers disagree across teams, or where decisions are being made on intuition because the data is not trusted.
Weakest where the data does not exist yet. Analytics cannot report on what was never captured, and the first fix is instrumentation.
How we scope and price
Fixed scope, quoted after an assessment. Cost is driven by source system count, data quality, and reporting scope. Where source data is poor, cleanup dominates the effort and we scope it explicitly rather than absorbing it silently.
Frequently asked questions
Almost always because definitions differ — different date logic, different filters, different treatment of edge cases. Agreeing and documenting definitions is usually the highest-value part of the engagement, and the least technical.
Depends on your users and existing stack. Power BI where you run Microsoft; Looker and Metabase for strong semantic modelling; Tableau for exploratory analysis. The tool matters far less than the data underneath it.
Beyond a couple of sources and moderate volume, yes — reporting directly from operational databases becomes slow and fragile. Below that, a well-structured Postgres often suffices.
Six to ten weeks typically, with the first useful dashboards usually landing well before the end.
Only if it answers questions they are actually asking, which is why we start from decisions rather than from data. Training and enablement are part of the engagement.
Yes, and the foundations overlap substantially. Clean, consistent, well-modelled data is what makes forecasting and predictive work possible. Analytics work is frequently the sensible prerequisite.
More Business services
Digital Transformation
Sequenced modernisation of the systems and processes holding a business back — delivered in increments that pay for themselves, not as a multi-year programme.
Technology Consulting
Independent advice on architecture, build-versus-buy, vendor selection and technical due diligence — written so you can act on it with or without us.
ERP & CRM Integration
Connect the systems that run your business so data flows once, reconciles correctly, and stops being re-keyed by hand.