Forecasting + predictive analytics
Build a forecast around the decision—not the other way around.
Useful predictive work begins with a baseline, the cost of different errors, the decision horizon, and the people who must act on the result.
Method before model
A complex model does not earn its place by being complex.
Work should compare against naive and current-process baselines, use time-aware validation where appropriate, expose error by segment and horizon, and connect predictions to an operating action.
Working sequence
From decision design to monitored use.
- 01Frame the decision
Owner, cadence, horizon, current approach, costs of error, and constraints.
- 02Audit the evidence
History, leakage, missingness, seasonality, changes, rights, and external drivers.
- 03Establish baselines
Compare the current process, naive methods, and justified predictive approaches.
- 04Validate honestly
Use evaluation that matches how the forecast will be produced and consumed.
- 05Integrate and monitor
Put outputs into a real workflow with ownership, thresholds, retraining, and review.
Required evidence
Every result needs context.
- Baseline comparison
- Validation window
- Error by useful segment
- Uncertainty or range
- Failure conditions
- Decision impact
Have a recurring decision?