Predictions that can be tested against reality.
Assess whether prediction is useful for a specific decision. Connect suitable data, simple baselines and transparent evaluation criteria.
What would you do differently with a prediction?
Expected demand or a signal of possible churn is useful only when it supports a responsible action. We start with that decision and the cost of mistakes, then assess the data and possible methods.
Include a simple baseline
A complex model must offer identifiable value over a simple approach. We consider time-appropriate test data, missing information and possible bias. Results are interpreted for their intended use; a good historical fit does not guarantee reliable future predictions.
First step: suitability and an evaluation plan
The first scope examines one prediction task, available data and a useful baseline. You receive a feasibility assessment and proposed success criteria, business ownership and next steps. Modelling is planned once prerequisites are clear.
How we deliver
Agree the task
Define the decision and consequences of mistakes.
Review the data
Assess history, outcomes, missing data and permitted use.
Evaluate an approach
Define a simple baseline and suitable test data.
Interpret findings
Interpret errors, value and limitations for the task.
Define the next step
Document findings, limitations and ownership. Agree implementation and operation separately.
Questions before starting
Sources and technical context
Start with a concrete question
Tell us the decision and the available data. Together we assess a useful first step.
Discuss a prediction use case