Predictive analytics is a technique used to analyze current and historical data to identify patterns and predict future outcomes and trends. The basic goal of predictive analytics is to make predictions about unknown future events. It allows analysts to identify relationships between independent and dependent variables. Through rigorous statistical modeling and machine learning techniques, predictive analytics helps estimate customer behavior, future trends and outcomes based on historical and current data. Some common techniques used in predictive analytics include regression analysis, decision trees, and neural networks.
Prescriptive analytics takes predictive analytics one step further by not just predicting what will happen but also recommending the optimal actions to take. While predictive analytics forecasts the most likely outcome based on past events and trends, prescriptive analytics evaluates millions of scenarios and recommends the best ways to achieve the desired targets. It moves beyond prediction by prescribing the best future state and recommending what actions should be taken today to get to that preferred state. Prescriptive analytics uses optimization, simulation and predictive modeling to help decision makers choose the best options.
There are a few key differences between predictive and prescriptive analytics:
Goal – The goal of predictive analytics is prediction while the goal of prescriptive analytics is optimization and prescription. Predictive analytics forecasts the likelihood of future outcomes based on historical data, while prescriptive analytics recommends the best course of action.
Techniques used – Predictive analytics relies on statistical techniques like regression analysis, decision trees and neural networks to make predictions. Prescriptive analytics layers optimization algorithms on top of these predictive models to figure out the best prescriptions. It uses optimization and simulation techniques like linear programming, integer programming and Monte Carlo simulation.
Analysis complexity – Predictive models analyze historical data to detect patterns and relationships between variables. Prescriptive models consider a much larger number of variables, constraints and alternative scenarios to arrive at optimal recommendations. This makes prescriptive analytics more complex to develop and implement compared to predictive models.
Accuracy – Predictive models estimate what is most likely to happen based on past trends. Prescriptive recommendations are oriented towards achieving specific targets and objectives in the future, hence they are typically more accurate than predictive forecasts if implemented properly.
Focus – Predictive analytics focuses on anticipating what will occur. Prescriptive analytics focuses on attaining desired outcomes by recommending specific actions. It cares more about attaining targets than accuracy of prediction.
Output – The output of predictive analytics is a prediction or forecast of the most probable future event. The output of prescriptive analytics is an optimal plan or recommended course of action to achieve a specific goal.
While predictive analytics forecasts the future, prescriptive analytics evaluates millions of scenarios using optimization to recommend the best path forward to achieve chosen goals and objectives. Predictive models analyze historical relationships to anticipate future trends, prescriptive models optimize future state by suggesting precise actions. Prescriptive analytics takes optimization a step further by translating insights into tangible recommendations for decision makers. It helps determine not just what will happen but what should be done to direct outcomes. Both are critical techniques used together in advanced data-driven decision making.
Andrew Stroud