This guide provides definitions and examples to help you understand the key differences between predictive and prescriptive analytics.
Both predictive and prescriptive analytics leverage statistical methods and modeling to assess future performance based on current and historical data. Predictive analytics focuses on forecasting outcomes to help you make decisions. Prescriptive analytics takes the extra step to recommend the specific, optimal course of action or strategy for you to take. Both types of advanced analytics simplify complex situations and help you make better, data-driven decisions.
Predictive analytics is the use of data mining techniques, statistical modeling, and machine learning to generate predictions about future outcomes based on your historical and current data. There are many types of predictive modeling techniques. The two most commonly used are regression and neural networks. These predictive models help guide your decision making to mitigate risk, improve efficiency, and identify opportunities.
Prescriptive analytics provides a specific recommendation on the best course of action for a business challenge. It uses a combination of business rules, heuristics, machine learning algorithms, and rule-based systems to simulate various strategies and make recommendations based on data and probability-weighted projections.
Dataset. Once you’ve defined your project and built the right team, the process begins by gathering the raw data you need and preparing your dataset. Predictive and prescriptive analytics are both based on current and historical data, whether structured data, images and video, or language data (unstructured data such as social media content, customer service notes, and web logs).
Data Modeling. The next step involves building, training, evaluating and deploying models. This can be done by hiring a data scientist to develop a model or develop one yourself by using an AutoML tool (automated machine learning). Either way, the accuracy of your models depends on the data quality, the choice of variables, and the model's assumptions.
Outputs. Predictive analytics focuses on forecasting possible outcomes to help you make decisions. The output typically comes in the form of reports and visualizations such as graphs, charts, and dashboards. Prescriptive analytics takes the extra step to recommend the specific, optimal course of action or strategy you should take. Specific use cases and business outcomes for certain industries are provided in the Examples section below.
Many organizations use multiple types of analytics, business intelligence, data science, and analytics tools to cover the spectrum of their analysis needs. Let’s take a side-by-side look at predictive and prescriptive analytics based on 10 key factors:
Predictive Analytics | Prescriptive Analytics | |
|---|---|---|
Output | Provides a forecast of possible outcomes but gives no guidance.
| Gives a specific recommendation for a given business decision.
|
Data Requirements | Relies on historical and current big data for trend analysis. | Requires not only historical and current big data but also additional contextual information for decision optimization.
|
Complexity of Analysis | Involves analyzing patterns and trends. | Requires more complex analysis, considering multiple variables and potential scenarios. |
Scope | Typically only focuses on limited aspects of your business. This can result in optimizing one area at the expense of others. | Prescriptive analytics takes interdependencies into account and models your entire business. |
Models | Hypotheses of predictive models are typically based on predetermined scenarios and these scenarios usually have a limited number of options.
| ML models consider all variables and potential outputs to more accurately represent how your organization operates. |
Human Bias | Requires human decision making because the outputs do not provide guidance. | Data-driven recommendations via artificial intelligence remove the risk of personal bias. |
Time Sensitivity | Emphasizes future predictions based on historical and current data. | Offers real-time or near-real-time recommendations for immediate decision-making. |
Feedback Loop | Limited feedback loop for model refinement. | Incorporates continuous feedback for ongoing model improvement and adaptation. |
Adaptability to Change | May not easily adapt to changing conditions without retraining models. | Designed to be adaptive, considering dynamic factors and adjusting recommendations accordingly. |
Risk Management | Provides actionable insights into potential risks based on historical trends. | Actively manages and mitigates risks by recommending actions to achieve desired outcomes. |
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Here are some common examples of insights and recommendations across industries.
Predictive analytics can analyze sales metrics to help you forecast demand for products to help ensure popular items are in stock. | Prescriptive analytics can analyze customer behavior and automatically set online pricing and marketing messages to reduce customer churn. |
Predictive analytics can forecast market trends and help you make informed decisions based on future asset price movements. | Prescriptive analytics can recommend specific, optimal investment strategies, considering risk tolerance and market conditions, to maximize returns.
|
Predictive analytics can anticipate patient admission rates, allowing hospitals to allocate resources efficiently and optimize staff schedules.
| Prescriptive analytics can develop personalized treatment plans for patients based on their medical history, current conditions, and predictive risk factors, improving overall healthcare outcomes. |
Types of Analytics | Question Answered |
|---|---|
What happened? | |
Why did it happen? | |
What will happen? | |
What should we do? |
Below we show the distinct processes and stages of human involvement for each approach. As you can see, only prescriptive analysis guides you through to a precise recommended course of action, labeled as "Action." In specific scenarios where human intuition and judgment are crucial, prescriptive analysis offers decision support rather than complete decision automation.
