Predictive analytics is the science of analyzing current and historical facts/data to make predictions about future events.
Unlike traditional business intelligence practices, which are more backward-looking in nature, predictive analytics is focused on helping companies derive actionable intelligence based on past experience.
A typical application is in insurance: predicting which policy holders (or potential policy holders) will make a claim and how long it will be until they make the claim. The more data available on the history of claims and ‘extraneous’ information about the policy holder the more variables a predictive analytics algorithm can take in to account. For example, a machine learning algorithm could easily take into account the impact of when a parent has children on claim rates. Identifying if such a relationship exists (amongst ALL the other possibilities) is too complex for human analysts. It is an automatic process with machine learning.
A second example is a health insurance company using predictive analytics to identify when patients are likely to have a hospital stay – and to direct health care providers to take preventative actions to avoid the hospital stay. With a growing base of health care data, this sort of data science is set to improve the nature of health care delivery. Other examples include prediction of product demand, options prices, or turnover likelihood of sales leads.