Gartner classifies analytics into four categories: descriptive, diagnostic, predictive, and prescriptive. Descriptive analysis helps to describe a situation and can help to answer questions like What happened?, Who are my customers?, How many people visited the museum last month?, etc. Diagnostic analysis helps to understand why things happen and can answer questions like Why did it happen? Predictive analysis is forward-looking, and can answer questions like What will happen in the future?. Prescriptive analysis prescribes and is more action oriented. It helps answer questions like What should we do?, What price should we charge?, or How should I allocate my investments?
Descriptive analysis is used to explain what is happening in a given situation. This class of analysis typically involves human intervention and can be used to answer questions like What happened?, Who are my customers?, How many types of users do we have?, etc. Common techniques used for this include descriptive statistics with charts, histograms, box and whisker plots, or data clustering.
Diagnostic analysis helps you understand why certain things happened and what are the key drivers. For example, a wireless provider would use this to answer questions such as Why are dropped calls increasing? or Why are we losing more customers every month? A customer diagnostic analysis can be done with techniques such as clustering, classification, decision trees, or content analysis. These techniques are available in statistics, data mining, and machine learning. It should be noted that business intelligence is also used for diagnostic analysis.
Predictive analysis helps you predict what will happen in the future. It is used to predict the probability of an uncertain outcome. For example, it can be used to predict if a credit card transaction is fraudulent, or if a given customer is likely to upgrade to a premium phone plan. Statistics and machine learning offer great techniques for prediction. This includes techniques such as neural networks, decision trees, random forests, boosted decision trees, Monte Carlo simulation, and regression.
Prescriptive analysis will suggest the best course of action to take to optimize your business outcomes. Typically, prescriptive analysis combines a predictive model with business rules (such as declining a transaction if the probability of fraud is above a given threshold). For example, it can suggest the best phone plan to offer a given customer, or based on optimization, can propose the best route for your delivery trucks. Prescriptive analysis is very useful in scenarios such as channel optimization, portfolio optimization, or traffic optimization to find the best route given current traffic conditions. Techniques such as decision trees, linear and non-linear programming, Monte Carlo simulation, or game theory from statistics and data mining can be used to do prescriptive analysis.
See the figure below for a sample flow of predictive analytics.