Prescriptive Analytics From What Will Happen to What to Do
Predictive analytics tells you what will happen. Prescriptive analytics goes a step further and tells you what to do about it — turning data and predictions into specific recommended actions, so analytics doesn't just inform decisions but recommends them.
From prediction to recommended action
Prescriptive analytics is the step beyond predictive analytics — going from predicting what will happen to recommending what to do about it. Where predictive analytics anticipates an outcome (demand will rise, this customer will churn), prescriptive analytics takes the next step and recommends the action to take in response (so order this much, intervene with this offer). It uses data, predictions, and optimization to turn analytics into specific recommended actions, so the analytics doesn't just inform a decision but suggests what the decision should be.
This is the most advanced form of analytics, building on the others. Descriptive analytics tells you what happened; predictive analytics tells you what will happen; prescriptive analytics tells you what to do about it. Each builds on the last — prescription typically rests on prediction, which rests on understanding the data — and prescriptive goes furthest by closing the gap between insight and action. Rather than leaving a person to figure out what to do with a prediction, prescriptive analytics recommends the action directly, often using optimization to find the best course given the predicted situation and the goals.
We build prescriptive analytics that turns data and predictions into recommended actions — going beyond telling a business what will happen to recommending what it should do about it. The aim is analytics that closes the gap to action: not just insight or prediction, but specific recommendations a business can act on, derived from data and optimized toward its goals. Because the value of analytics is ultimately in the decisions it drives, prescriptive analytics — which recommends the decision itself — is analytics taken to its most actionable form, and we build it to genuinely recommend actions worth taking.
What prescriptive analytics provides
How we build your prescriptive analytics
Define the decisions
We start from the decisions you'd want recommendations for, since prescriptive analytics recommends actions and needs to know which decisions matter.
Build the prediction
We build the predictive foundation, since recommending what to do typically rests on predicting what will happen.
Optimize the action
We use optimization to find the best action given the predicted situation and your goals, the core of prescription.
Recommend clearly
We turn the analysis into clear, specific recommended actions, so the analytics closes the gap to action rather than just informing it.
Make it actionable
We build prescriptive analytics that recommends actions worth taking, since the value is in driving decisions, not in the analysis itself.
Analytics that recommends the decision
Prescriptive analytics matters because it closes the gap between insight and action — the gap where a lot of analytics value is lost. Analytics exists to drive better decisions, but most analytics stops short of the decision: descriptive analytics tells you what happened, predictive analytics tells you what will happen, and then it's left to a person to figure out what to do about it. That last step — turning insight or prediction into the right action — is where analytics often fails to deliver its full value, because the analysis informs the decision but doesn't make it, and the translation from insight to action can go wrong or not happen at all.
Prescriptive analytics takes that last step, recommending the action itself. Rather than leaving the prediction sitting there for someone to interpret and act on, prescriptive analytics goes further and says what to do — using the prediction, the data, and optimization to recommend the best course of action given the situation and the business's goals. If predictive analytics says demand will rise, prescriptive analytics recommends how much to order; if predictive analytics flags a customer likely to churn, prescriptive analytics recommends the intervention. It closes the loop from data to decision, which is the whole point of analytics in the first place.
This makes prescriptive analytics the most advanced and most actionable form of analytics, building on the others. It rests on prediction (which rests on understanding the data) and goes furthest by recommending action directly. The value of analytics is ultimately in the decisions it drives, and prescriptive analytics drives them most directly by recommending the decision — though the recommendations have to be genuinely good and actionable to be worth following. We build prescriptive analytics that turns data and predictions into recommended actions worth taking, closing the gap to action, because analytics that recommends the decision is analytics delivering its fullest value: not just informing what to do, but recommending it.
Close the gap to action
We build prescriptive analytics to close the gap between insight and action, because that's where it adds value beyond other analytics. Most analytics stops at telling you what happened or what will happen, leaving the decision to a person — and that last step is where analytics value is often lost. Prescriptive analytics takes the step, recommending the action itself, turning data and predictions into specific recommendations. We build it to close that loop from data to decision, because driving the decision is the whole point of analytics.
We build it on a sound predictive and data foundation, because recommendation rests on prediction. Prescriptive analytics typically builds on predicting what will happen, which builds on understanding the data — so a recommendation is only as good as the prediction and data beneath it. We build the predictive foundation soundly and use optimization to find the best action given the predicted situation and your goals, because prescriptive analytics that recommends actions on a weak foundation recommends bad actions, which is worse than not recommending at all.
And we make sure the recommendations are genuinely actionable and worth following, because that's the test of prescriptive analytics. A recommendation no one can act on, or shouldn't act on, delivers nothing — so we aim prescriptive analytics at real decisions and build it to recommend actions worth taking. The value is in the decisions it drives, and prescriptive analytics drives them most directly by recommending them, so we build it to genuinely close the gap to action with recommendations a business can confidently act on, which is analytics delivering its fullest value.
Frequently Asked Questions
It's the step beyond predictive analytics — going from predicting what will happen to recommending what to do about it. Where predictive analytics anticipates an outcome, prescriptive analytics recommends the action to take in response, using data, predictions, and optimization to turn analytics into specific recommended actions. So the analytics doesn't just inform a decision but suggests what the decision should be.
Predictive analytics tells you what will happen; prescriptive analytics tells you what to do about it. Prediction anticipates the outcome (demand will rise, this customer will churn); prescription recommends the action (order this much, intervene with this offer). Prescriptive goes a step further, closing the gap from prediction to action by recommending the response, rather than leaving a person to figure out what to do with the prediction.
Descriptive analytics tells you what happened; predictive analytics tells you what will happen; prescriptive analytics tells you what to do about it. Each builds on the last — prescription typically rests on prediction, which rests on understanding the data — and prescriptive goes furthest, recommending action directly. It's the most advanced form, closing the gap between insight and action that the others leave to a person.
Because it closes the gap between insight and action where analytics value is often lost. Analytics exists to drive decisions, but most stops short of the decision, leaving a person to translate insight into action — which can go wrong or not happen. Prescriptive analytics takes that last step, recommending the action itself, closing the loop from data to decision. Since the value of analytics is in the decisions it drives, prescriptive drives them most directly.
By using the prediction, the data, and optimization to find the best course of action given the situation and the business's goals. If a prediction says demand will rise, prescriptive analytics optimizes to recommend how much to order; if it flags likely churn, it recommends the intervention. It builds on prediction and applies optimization to turn the predicted situation into a specific recommended action aimed at the business's objectives.
They have to be genuinely good and actionable to be worth following — a recommendation no one can or should act on delivers nothing. Prescriptive analytics is only as good as the prediction and data beneath it, so recommendations on a weak foundation can be bad. We build the predictive and data foundation soundly and aim the recommendations at real decisions, so the analytics recommends actions a business can confidently act on, which is the test of prescriptive analytics.
Typically, yes — prescriptive analytics builds on prediction. Recommending what to do usually rests on predicting what will happen, which rests on understanding the data. So a sound predictive foundation underpins good prescriptive analytics; recommending actions without reliable predictions beneath them recommends poorly. We build the predictive foundation as part of delivering prescriptive analytics, since the recommendation is only as good as the prediction it's based on.
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