At a glance
- Recommends not only what is likely to happen, but what should be done, including the rationale and an assessment of the consequences.
- Algorithms, simulation, and mathematical optimization help identify the best option among many possible decisions.
- Builds on descriptive and predictive analytics, without a solid data foundation and reliable forecasting models, recommendations cannot be robust.
- Budget allocation, scenario planning, and resource management are typical application areas in finance and controlling.
- Systems make recommendations, people make decisions. Prescriptive analytics does not replace human judgment; it significantly sharpens it.
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Prescriptive analytics definition
Prescriptive analytics refers to a data analysis method that derives specific recommendations for business decisions based on historical data, statistical models, and algorithmic optimization. It represents the third and most advanced stage in the analytics maturity model. Following descriptive analytics (What happened?) and predictive analytics (What will happen?), it addresses the most decision-relevant question: Which action will produce the best possible outcome under the given conditions?
Technically, prescriptive analytics relies on mathematical optimization methods, simulation techniques, as well as machine learning and artificial intelligence. A fundamental principle applies: The system recommends, the human decides. Prescriptive analytics is not an autonomous decision-making system, but rather a tool for systematically preparing and supporting human judgment.
In business, the concept is generally most effective where decisions are recurring, data-rich, and consequential, for example, in budget allocation, integrated business planning, pricing, or risk management. The value lies not only in the quality of individual recommendations, but also in the consistency of the decision-making process: Prescriptive analytics helps ensure that the same starting conditions lead to equally sound decisions, regardless of individual experience or organizational silos.
„With DeltaMaster, we evaluate alternative courses of action based on drivers and present management with well-founded measures instead of gut feeling. The simulation clearly shows us which option generates volume and which one costs margin.“Head of Controlling
Mechanical engineering
What are the three stages of data analysis?
Descriptive vs. predictive vs. prescriptive analytics: a comparison
Data analysis can be divided into three successive stages, from description to forecasting to recommendations for action:
| Stage | Question | Method | Example |
|---|---|---|---|
| Descriptive analytics | What happened? | Reporting, dashboards, KPIs | Revenue declined in Q3 |
| Predictive analytics | What will happen? | Statistics, machine learning | Revenue will continue to decline in Q4 |
| Prescriptive analytics | What should I do? | Optimization, simulation, AI | A price reduction is recommended for segment B |
Descriptive analytics is now established in most organizations: reports, dashboards, and KPI systems are standard. Predictive analytics is becoming increasingly mature as machine learning advances and more data becomes available. Prescriptive analytics, by contrast, is the most sophisticated discipline: It combines forecasts with decision logic and specifies which action is likely to produce the best results under which conditions.
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How does prescriptive analytics work?
Prescriptive analytics does not just answer the question “What will happen?” It adds two crucial dimensions:
- Why will it happen? Causal models and explainability
- What should be done? Decision recommendations with an assessment of the consequences
A prescriptive analytics system takes forecasts as input, combines them with defined objectives (such as maximizing contribution margin, minimizing inventory, or avoiding risk), and calculates which course of action best fulfills the objective function. Constraints such as budget limits, capacity, and regulatory requirements are explicitly taken into account. The result is a concrete action plan with justified and quantified recommendations.
Example from practice:
A retail company forecasts declining demand for a product segment. Prescriptive analytics then automatically calculates: Which price adjustment will maximize the remaining contribution margin? Which inventory levels should be reduced? Which sales channels should be prioritized? The resulting recommendations are not abstract — they can be implemented directly.
Which methods and technologies are part of prescriptive analytics?
Prescriptive analytics draws on a broad range of methods:
What are the opportunities and limitations of prescriptive analytics?
Opportunities
- Decision quality improves: Recommendations are based on more data, more scenarios, and more consistent logic than human intuition alone can provide.
- Decision-making becomes faster: Routine decisions, for example in operational planning or procurement, can be automated or at least significantly accelerated.
- Complexity becomes manageable: Prescriptive analytics can take thousands of variables and constraints into account simultaneously, far beyond the limits of human cognitive capacity.
Limitations
- Data quality is a prerequisite: Poor input data leads to poor recommendations. Prescriptive analytics amplifies data errors rather than compensating for them.
- Model quality is critical: The quality of a recommendation depends on the quality of the underlying model. Simplifying assumptions can result in recommendations that appear dangerously precise but are actually incorrect.
- Explainability is critical: Decision-makers need to understand and challenge recommendations. Black-box models can create distrust and make accountability more difficult.
- Organizational acceptance: Recommendations that challenge established decision-making practices can encounter resistance, regardless of their computational quality.
- Ethics and governance: Automated decision recommendations must be assessed for bias, fairness, and regulatory compliance.
What does Prescriptive Analytics with DeltaMaster look like in practice?
With DeltaMaster, controlling runs through all three analysis stages in the same system, from the detected variance to the justified action. The following example shows this process at a mid-sized machine builder whose order intake in May falls short of the annual plan.
| Analysis stage | Question | Result in DeltaMaster |
|---|---|---|
| Descriptive Analytics | What happened? | Order intake from January to April is 6% below plan. The AI analysis automatically highlights the variance and explains the cause: a decline in spare parts in two sales regions. |
| Predictive Analytics | What will happen? | The AI forecast extrapolates the full year to 111 million euros; 120 million euros were planned. The forecast fan shows the range from pessimistic to optimistic. |
| Prescriptive Analytics | What should we do? | The simulator runs through three courses of action in a driver-based way and shows their effect on order intake and contribution margin. |
The management sets the goal and the constraint: the contribution margin must not decline, and the order gap of 9 million euros is to be at least halved. The simulation compares the options against these requirements.
| Option | Action | Order intake | Contribution margin |
|---|---|---|---|
| A | Flat discount of 5% on all products | +6.0 million euros | −2.1 million euros |
| B | Shift sales budget from stable regions to the two weak regions, spare-parts campaign for existing customers | +5.0 million euros | +1.2 million euros |
| C | Option B plus discount for major customers in one region | +7.5 million euros | −0.4 million euros |
The simulation reveals a common mistake: the flat discount brings volume but costs margin and does not address the cause. Only option B meets both requirements, because it starts where the AI analysis located the decline. Controlling presents option B with justification for decision.
Management decides on option B. The justification and assumptions are recorded as a comment directly in the report, and audit trails make the data basis and decision logic verifiable. Via the DeltaMaster Publisher, the sales managers receive the action along with its justification. In the following months, the plan-actual comparison shows whether the action is taking effect.
What are some examples of prescriptive analytics?
Prescriptive analytics draws on a broad range of methods:
Where would you like to use Prescriptive analytics?
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Bissantz and prescriptive analytics
With DeltaMaster, Bissantz takes an analytical approach that views the stages of data analysis as an integrated system for generating insights. Descriptive and predictive analytics are deeply integrated into DeltaMaster.
The transition to prescriptive analytics is not a technological leap, but a logical extension of analytical thinking: Anyone who understands what has happened and can reliably assess what will happen is in a position to derive well-founded recommendations. DeltaMaster supports this process through powerful simulations, what-if analyses, and integrated planning models that enable controlling teams to systematically evaluate alternative courses of action rather than making decisions based on gut instinct or precedent.
Bissantz places a strong emphasis on transparency and explainability: Recommendations must be understandable so that decision-makers can take responsibility for their decisions.
| Function | Benefit |
|---|---|
| Integrated simulations | Play through and evaluate alternative courses of action based on drivers |
| What-if analyses | Make the impact of options on target figures visible |
| Integrated planning models | Derive recommendations from a solid planning basis |
| Transparency and explainability | Make recommendations comprehensible so that decision-makers can take responsibility |
„We run through all three analysis stages in the same system and record assumptions and justifications directly in the report. This keeps our decisions traceable and auditable.“BI Manager
Retail company
Frequently asked questions
What is prescriptive analytics in simple terms?
Prescriptive analytics is like an experienced advisor who not only tells you what is likely to happen, but also what you should specifically do. While predictive analytics warns, “Revenue in segment B will decline next quarter,” prescriptive analytics says, “Reduce the price by 8% in segment B and shift the sales budget from channel C to channel A — this will maximize your contribution margin under the given conditions.”
Does prescriptive analytics replace human decision-making?
No — and that is a deliberate principle. Prescriptive analytics recommends; humans decide. Systems provide quantified and well-founded courses of action, but responsibility, contextual understanding, and final judgment remain with people. Especially for complex, high-impact decisions, this principle is not only ethically important but is also increasingly required by regulation.
What is the difference between prescriptive analytics and decision intelligence?
Prescriptive analytics is an analytical method that optimizes decisions based on data, models, and defined objective functions. Decision intelligence is an overarching approach that combines prescriptive analytics with cognitive science, behavioral economics, and organizational decision theory. Decision intelligence asks not only, “What is the optimal decision?” but also, “How can people and organizations systematically and sustainably make better decisions?”
What are the prerequisites for successful implementation?
Three prerequisites are critical: first, a solid, high-quality data foundation, since incorrect input data leads to incorrect recommendations. Second, reliable predictive analytics models on which the recommendations are based. Third, clearly defined objective functions and constraints — prescriptive analytics always optimizes toward a goal that must be explicitly defined.
How does DeltaMaster from Bissantz support the transition to prescriptive analytics?
Bissantz provides AI-generated recommendations for action and enables the systematic comparison of alternative courses of action through integrated planning models, what-if analyses, and simulations. Controlling teams can therefore quantify and evaluate decision options transparently and traceably, without black-box logic. This reflects the Bissantz principle: Recommendations must be understandable so that decision-makers can take responsibility for their decisions.
Summary
Prescriptive analytics is the most advanced stage of data analysis: It combines forecasts with decision logic and provides specific, well-founded recommendations for action that are quantified, actionable, and consistent. Its greatest value lies not in individual recommendations, but in systematizing the decision-making process: The same starting conditions lead to equally sound decisions, regardless of individual experience or organizational silos. Bissantz supports this approach through integrated recommendations for action and simulations based on the principle: The system recommends; the human decides.