Prescriptive analytics leverages big data and machine learning (ML) to provide recommendations, forecast outcomes, and prescribe courses of action. This ebook offers an in-depth look at how this type of analysis works and what you need to know before undertaking it.
From the ebook:
To understand prescriptive analytics, it’s important to have a basic working knowledge of the larger world of business analytics. Business analytics is a multi-stage process. Each step involves the analysis of data to reach a particular type of conclusion, the ultimate goal of which is to build the best possible strategy for optimized organizational action.
There are typically three parts described in business analytics:
- Descriptive analytics is the kind of analysis that is performed to describe an organization’s current circumstances. The data used in this instance can include customer feedback, sales numbers, website traffic—essentially any data that is a record of past events that can be used to analyze business up to the present.
- Predictive analytics uses the same type of data, and sometimes the descriptive outcomes, to predict what will happen given the current circumstances. Businesses often employ machine learning and various forms of predictive modeling to make predictions. Think of predictive analytics as what will happen if current organizational practices and habits remain the same.
- Prescriptive analytics is less fortune teller and more medical doctor. Instead of simply predicting what will happen, prescriptive analysis tweaks certain variables to achieve the best possible outcome, and then prescribes that course of action.
Businesses can employ one or all of these forms of analytics, but not necessarily out of order. To predict the future, you need to know what has already happened, and to change course, you have to know what’s likely to happen without that course correction.