Data science and analytics
Most organisations are not short of data; what they lack is answers. The data sits across several systems, reports are assembled by hand, and decisions end up resting on experience. Our work is closing that gap: from scattered data to a number you can decide on.
What gets delivered
- Integrating data from separate systems into one source that can be relied on
- Reports and management dashboards that update automatically instead of being built by hand
- Predictive models: demand, risk, churn probability, consumption forecasting
- Root-cause analysis: why an indicator moved, not only that it moved
How a project runs
- 1
Define the question and the metric
A project starts from a decision-shaping question, not from the data. If the answer would not change a decision, it is not worth building.
- 2
Clean and model
Data is integrated and cleaned, then a model is built and evaluated on held-out data. A model that only performs on its training data is useless.
- 3
Hand over and enable the team
The output is not just a file or a dashboard; the internal team must be able to run it, interpret it and keep it current year after year.
Frequently asked questions
Where should we start?
From a specific question, not from the data. "We have a lot of data, what should we do?" goes nowhere. "We want to know which customers are likely to leave" is a definable, measurable project.
Our data is scattered and incomplete — is it still possible?
Usually yes. Incomplete data is the normal state of every organisation, not the exception. A substantial part of any project goes on cleaning and integration, and that is planned for openly from the start.
How much data is needed?
It depends on the problem, not on a fixed number. For seasonal forecasting, two to three years of history is usually the minimum; for some classification problems, far less is enough.
What is the difference between a dashboard and a predictive model?
A dashboard tells you what happened; a predictive model tells you what is likely to happen. Most organisations need a correct dashboard first, and jumping straight to prediction is usually premature.
What happens after the project?
A model has to be kept current or it drifts out of accuracy. The internal team must be able to re-run and evaluate it, which is why team training is part of the project rather than an optional extra.
Further reading
The question your data can answer
A short conversation usually makes clear which question your existing data can answer and which it cannot yet.