A prediction is only useful if someone will act on it and its accuracy is known. We start from the action — what changes if the number is high or low — then build the simplest model that beats the current baseline, state its error honestly, and put it where the decision is made rather than in a report nobody reads.
Data & Analytics
Predictive Analytics
Forecasts with their error stated, aimed at a decision.
Overview
What the engagement covers
- The decision and the current baseline, established first
- Feature engineering from data you will still have at prediction time
- Model selection favouring the explainable where it competes
- Backtesting on held-out periods, not on the training window
- Deployment into the workflow, with drift monitoring after
What you leave with
- A forecast measurably better than what you use today
- Error you can plan around because it is quantified
- Predictions where the decision happens, not in a separate tool
Data & Analytics
The rest of this practice
Predictive Analytics, scoped to your estate
Tell us where you are and what it has to be worth. We will come back with a scope, a sequence and a number.