Predictive Analytics for Funders

Predictive analytics estimates likely outcomes and highlights drivers so programmes can target resources more effectively.

• Works best with clean, standardised data.
• Should inform, not determine, decisions.
• Transparent methods avoid spurious precision.

Practical use cases

Start with interpretable models and simple questions.

• Forecast completion risks and support needs.
• Identify characteristics of high‑impact projects.
• Optimise monitoring frequency by risk.

Key takeaway: predictions guide action plans and support, not approvals.

Guardrails for responsible use

Mitigate bias and keep outputs explainable.

• Use demographic‑aware fairness checks where appropriate.
• Prefer models you can explain to boards and applicants.
• Monitor drift and retrain with new data.

Key takeaway: Plinth emphasises clarity and proportionate use.

Turning insight into change

Close the loop by adjusting guidance, criteria and support offers.

• Share insights with grantees to improve outcomes.
• Test changes and measure results across cohorts.
• Document learning for future rounds.

Key takeaway: value comes from action, not the model itself.

FAQs

Do we need a data scientist?

Not to start. Many questions are answerable with simple, transparent methods.

How much data is required?

Less than you think; quality beats quantity when fields are consistent.

Can predictions be appealed?

They should never be final decisions; humans decide with context.