Analytics
Analysis that answers a decision, not a request for a chart.
The analysis behind a decision, and the data product that keeps it available.
Advisory + implementation. Scoped to one decision or one product, with the readiness check up front.
Operations, finance and commercial leaders with a decision that keeps being made on instinct.
A recurring decision has no evidence behind it, a forecast is consistently wrong, or an analytics project has been running long enough that nobody remembers the question.
The practice
Framing the decision
What is being decided, by whom, how often, and what would change the answer. Most analytics work fails here rather than in the modelling.
Readiness
Whether the data underneath can carry the question — completeness, history, granularity — and what it would take if it cannot.
Diagnostic analysis
Why the number moved. Usually more valuable than prediction, and almost always faster to deliver.
Forecasting and prediction
Where history supports it, with the error range stated plainly rather than a single number that implies certainty.
Data products
The analysis packaged so it keeps running: refreshed, monitored and owned, instead of a notebook on somebody's laptop.
Operational analytics
Measures put in front of the people doing the work, at the moment it is useful, rather than in a monthly pack.
Interpretation
What the result means, what it does not mean, and what would have to be true for it to be wrong.
Handover
Documentation, the assumptions written down, and a team that can rerun and extend it.
Start from the decision, not the data.
- 01The decision
What is being decided, by whom, how often, and what evidence would change the answer.
- 02Readiness
Whether the data can carry the question — history, granularity, completeness.
- 03The analysis
Diagnostic first. Prediction where history genuinely supports it, with the error range stated.
- 04A data product
Refreshed, monitored and owned, so the answer is still there next quarter.
A request arrives as a chart. An afternoon on the decision behind it routinely shows the data to answer the real question is somewhere else entirely — which is cheaper to find out now than after a quarter of modelling.
We frame the decision, check whether the data can carry it, run the analysis, and package it so it keeps running. Diagnostic work first, prediction where the history genuinely supports it, and a data product at the end rather than a notebook on someone’s laptop.
Start from the decision, not the data
What is being decided, who decides it, how often, and what evidence would change the answer. That conversation takes an afternoon and routinely removes half the requested work, because the data to answer the real question is somewhere else entirely.
Diagnostic before predictive
Everybody asks for a forecast. Most businesses get more from knowing why last quarter moved, reliably and quickly, than from a prediction of the next one built on history that does not support it.
A result that only exists once is not a capability
If the analysis matters, it has to keep running: refreshed on a schedule, monitored, owned, and documented well enough that someone else can extend it. Otherwise it decays into a screenshot in a deck.
What you keep
The code and the data·Your existing relationships·Approval and control·The ability to stop·The off switchWhat that means
Data Architecture
The target shape of the estate, and the governance that keeps it that shape.
Learn more ↗Business Intelligence
Reporting people trust, built for the decisions it is supposed to serve.
Learn more ↗Data Integration
Getting data from where it is produced to where it is used, reliably.
Learn more ↗Start with a 45-minute briefing.
No pitch. We’ll map your situation against what actually works and tell you honestly where to start.