Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or other unknown events. It is considered a form of advanced analytics that implements statistical techniques, algorithmic programming, and analytical queries. With the emergence of big data this form of analysis has grown together since the presence of large masses of information has brought with it greater opportunities for analysis.
Table of Contents
Predictive models for predictive analytics
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. These models capture relationships between many factors that allow the assessment of the risk or potential associated with a particular set of conditions, to guide decision-making for potential transactions.
The functional effect that defines these technical approaches is that predictive analytics provides a predictive assessment (probability) for each individual (customers, employees, products, components, machines or other organizational unit) in order to determine, inform, or influence organizational processes that belong to the other side of a large number of people, such as marketing, credit risk assessment, fraud detection, manufacturing…
Predictive analytics is used in current science, marketing, financial services, insurance, telecommunications, retail, travel, and other fields.
Predictive analytics is an area of data mining that deals with extracting insights from data and using it to predict trends and behavior patterns. Often, it is not known if there is going to be an event of future interest, but predictive analytics can be applied to any type of event, whether in the past, present or future. The core of predictive analytics is based on the relationships between explanatory variables and predicted variables from past events, and exploiting them to predict an unknown outcome. It is important to note, however, that the accuracy and ease of use of the results will largely depend on the level of data analysis and the quality of assumptions.
Predictive analytics is often defined as predicting at a more detailed level of granularity, that is, the generation of predictive outcomes (probabilities) of each element of the individual organization, which distinguishes it from forecasts.