Data & Knowledge Intelligence

Predictive Analytics

When decisions currently made on historical averages would be better made on current signals, and there is enough history to learn from.

Prediction is only useful if someone acts on it, which makes calibration more important than raw accuracy. A model that is confidently wrong in a narrow band is more dangerous than one that is broadly uncertain, because nobody checks it.

The work therefore includes deciding what happens when a prediction is wrong, before anyone relies on one being right.

What the work includes

  • Forecasting built on current signals rather than historical averages alone
  • Calibration against your own outcomes, revisited as conditions change
  • Clear thresholds for when a prediction should route to a human
  • Monitoring for drift as the underlying behaviour shifts
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// Common Questions

Questions
About This

Common questions about Predictive Analytics.

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Enough history for the pattern to be visible, an outcome recorded consistently, and a decision that would genuinely change based on the prediction. The last is the one most often missing — forecasts nobody acts on are expensive to produce and free to ignore.

Most existing forecasting extrapolates historical averages. Prediction on current signals responds to what is happening now, which matters where conditions change faster than the averaging window — and matters very little where they do not.

Unknowable in advance, and measurable on your historical data before anything is deployed. Testing against known outcomes tells you whether accuracy clears the threshold at which the decision would actually change — which is the only number that matters.

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