The analytics efforts in a business are generally divided into 3 types; descriptive, predictive and prescriptive analytics.
A simple definition of descriptive analytics is that it is used to answer questions about what has happened in a business. It is primary use is to look at the current business situation with an eye towards looking for cause and effect. It helps one to understand how to manage in the present based on what happened in the past.
Per the Commission on Higher Education (CHED), descriptive analytics make use of current transactions to enable managers to visualize how the company is performing. When teaching the concept, it is generally focused on analysis and reporting to guide decision-making.
Most businesses use mostly descriptive analytics in their analysis, reporting and decision-making.
Have to apologize to whoever made this image, I dont know the source, but you have my thanks for making it.
As you can see in the image, predictive analytics takes data and extrapolates patterns to predict likely outcomes. Past, Present, Past Present, Future… the goal being too provided educated guesses on what is most likely to happen next. The primary use of predictive analytics is to predict outcomes using models that will mitigate risk and eliminate choices based on unlikely outcomes.
Per CHED, Predictive analytics allows voluminous data to be used for prediction, classification and association making it very useful tool for projections, forecasts, and correlations. Most lessons around predictive analytics involve data modeling and require a much higher degree of skill then descriptive analytics.
In general, predictive analytics is used by large companies in data-rich industries. Up until recently there were very few tools available to smaller businesses to add this type of analytics to their decision-making.
Prescriptive analytics goes one step further and finds the best course of action for a given situation. Its primary goal is to enhance decision-making by giving multiple outcomes based on multiple variables. The analogy of how doctors prescribe medicine to patients based on a wide range of variables in a patient’s health, using an equally wide range of treatment options.
Per CHED, Prescriptive Analytics help organizations develop insights to make decisions from the current data that maximizes the organization goals. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Largely, instruction take the model building found in predictive analytics and supercharges it with more data, more choices and more outcomes.
Prescriptive analytics is fairly new and just now gaining widespread use in the corporate world. There are not many tools available that are cheap or easy to use. Generally, you find data scientists assigned to prescriptive analytics projects. It also take us closer to some decision-making in a business being completely automated. With enough data on hand, using machine learning to analyze the data, we are starting to see artificial intelligence at play with prescriptive analytics. It is a pretty exciting time.
Its important to keep in mind that to really be good at predictive and prescriptive analytics you need both the high tech tools and the training/experience to use them effectively.