Analytics comes in three (3) general flavors: descriptive, predictive and prescriptive. See: Predictive, Descriptive, Prescriptive Analytics.

Descriptive analytics describes the past and predictive analytics provides a probability of what might happen. In contrast, prescriptive analytics helps an organization evaluate different scenarios and seeks to determine the best course of action to achieve optimal outcomes - given known and estimating unknown variables.

Prescriptive analytics provides decision options and shows the likely impact of each decision option using probability theory.

To design and implement an effective prescriptive analytics strategy, an organization needs an information management strategy (including both internal and external data as well as both structured and unstructured data), a technology strategy and a data science strategy . The organization must invest in a team of data scientists to use sophisticated simulation techniques, machine learning and statistical algorithms for crunching relevant data and applying probability theory. The data science team works with leaders to design a prescriptive strategy for evaluating scenarios and making optimal decisions.

There are three types of data analysis:

Predictive (forecasting)

Descriptive (business intelligence and data mining)

Prescriptive (optimization and simulation)

Increased compute speed, decreased data storage costs and recent development of complex algorithms applied to diverse data sources and larger data sets has made prescriptive analysis feasible and affordable for most organizations. Scientific techniques include data science (e.g., machine learning, algorithms, artificial intelligence, bayesian probability, monte carlo simulations...etc.), game theory, optimization, simulations, and decision-analysis methods.