What is the Difference Between Predictive and Prescriptive Analytics?
🆚 Go to Comparative Table 🆚The main difference between predictive and prescriptive analytics lies in their purposes and outcomes. Here is a summary of their differences:
Predictive Analytics:
- Forecasts potential future outcomes based on past data.
- Often uses structured historical data, such as credit histories, transactional data, and customer data.
- Involves predicting the value of an unknown variable using the values of known independent variables.
- Provides an insight into what is most likely to happen in the future.
Prescriptive Analytics:
- Creates specific, actionable recommendations based on the forecasts from predictive analytics.
- Commonly uses hybrid data, including structured data and unstructured data like videos, pictures, and documents.
- Involves determining the optimum value for a decision variable within specific constraints.
- Suggests various courses of action and outlines the potential implications for each.
While both predictive and prescriptive analytics are essential for making informed decisions, they serve different purposes. Predictive analytics helps organizations understand what is likely to happen in the future, while prescriptive analytics guides them in deciding on the best course of action to maximize opportunities or minimize risks. It is often most effective to use both types of analytics together to make the best possible decisions.
Comparative Table: Predictive vs Prescriptive Analytics
The main difference between predictive and prescriptive analytics lies in the type of information they provide. Predictive analytics uses historical data and modeling techniques to forecast potential future outcomes, while prescriptive analytics goes a step further by using a wide range of data to create specific, actionable recommendations for these predictions. Here is a table summarizing the differences between predictive and prescriptive analytics:
| Predictive Analytics | Prescriptive Analytics |
|---|---|
| Forecasts potential future outcomes based on past data | Uses a wide range of data to create specific, actionable recommendations for these predictions |
| Often uses structured historical data (e.g., credit histories, transactional data, customer data) | Considers the relationship between variables and how these factors impact the final outcome |
| Examples include predictive maintenance and sales forecasting | Focuses on optimizing decisions and outcomes by suggesting various courses of action and outlining potential implications |
In practice, combining both predictive and prescriptive analytics can yield more granular, actionable insights. For example, when planning a flash sale, prescriptive analytics could suggest using the SMS channel for maximum impact, while predictive analytics could estimate that doing so will increase sales by a certain percentage.
- Prescriptive vs Descriptive
- Preventive vs Predictive Maintenance
- Analytical vs Descriptive
- Forecast vs Prediction
- Inference vs Prediction
- Descriptive vs Inferential Statistics
- Classification vs Prediction
- Descriptive vs Analytic Epidemiology
- Hypothesis vs Prediction
- Data Mining vs Machine Learning
- Preventive vs Preventative
- Descriptive vs Correlational Research
- Prescriptivism vs Descriptivism
- Business Analyst vs Business Consultant
- Budgeting vs Forecasting
- Prediction vs Prophecy
- Proactive vs Reactive Risk Management
- Cognitive Computing vs Machine Learning
- Reactive vs Proactive