Prediction problem design
Define the target, prediction horizon and available information so the model reflects the real decision moment.
Predictive Analytics
Predictive Analytics
Estimate a future outcome when historical examples and a decision process make prediction useful and testable.
Inside the delivery
A forecast linked to a decision and a time horizon
Specify what is predicted, how far ahead and which action can use the result. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Target definition
Specify what is predicted, how far ahead and which action can use the result.
Output
Prediction target and decision policy
- 02
Historical preparation
Build features available at prediction time and use time-appropriate validation splits.
Output
Historical dataset and leakage checks
- 03
Baseline comparison
Compare models with simple forecasts across relevant periods and error costs.
Output
Forecast evaluation and uncertainty analysis
- 04
Decision integration
Deliver predictions in the consuming workflow with monitoring and review of changing conditions.
Output
Prediction interface and monitoring plan
Controls across the workflow
- Time-aware splits
- Leakage prevention
- Baseline forecasts
- Uncertainty visibility
Decisions that shape the scope
- Can predictions establish what causes an outcome?
- No. Predictive association does not establish causation. If the decision concerns the effect of an intervention, an experiment or a suitable causal analysis may be needed instead.
- What needs to be available before delivery?
- Historical outcomes, prediction horizon, data available at decision time, seasonal patterns and the cost of prediction errors.
Before you commit
Is this the right engagement?
- What we need from you
- Historical outcomes, feature availability at decision time, the intended intervention and the cost of prediction errors.
- How you accept the work
- Compare against a simple baseline using a time-aware holdout where appropriate, and review calibration, subgroup errors and the decision impact.
- Scope & alternatives
- No. Predictive association does not establish causation. If the decision concerns the effect of an intervention, an experiment or a suitable causal analysis may be needed instead.
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Model evaluation
Compare candidate approaches with an appropriate baseline and inspect leakage, error costs and performance across important groups.
Decision integration
Connect predictions to an operational action and monitor input changes, feedback delays and model degradation.
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Microsoft Azure
AWS
Databricks
Snowflake
dbt
Power BI and Tableau
Frequently asked questions
Questions to resolve before starting
01Can predictions establish what causes an outcome?
Can predictions establish what causes an outcome?
No. Predictive association does not establish causation. If the decision concerns the effect of an intervention, an experiment or a suitable causal analysis may be needed instead.
02What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Historical outcomes, prediction horizon, data available at decision time, seasonal patterns and the cost of prediction errors.
03What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes prediction interface and monitoring plan. Deliver predictions in the consuming workflow with monitoring and review of changing conditions.
04How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Historical outcomes, prediction horizon, data available at decision time, seasonal patterns and the cost of prediction errors. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for Predictive Analytics.