Predictive analytics is a subset of data analytics dedicated to identifying future outcomes using historical data, statistical algorithms and machine learning. Organizations utilize predictive analytics to forecast events and pinpoints trends based upon past and present data. Predictive analytics can be used to influence everything from future marketing campaigns to project budgets to new products and innovations to inventory. Predictive analytics is also useful in preventing damage to organizations such as fraud, security breaches, mismanagement and other common business pitfalls. Predictive analytics can connect the dots within an organization and plan for better future behaviors.
While Big data is an important ingredient in the predictive analytics kitchen, predictive analytics tools
wrangle big data models to procure real-time insights that help organizations forecast future business plans by predict customer behavior and patterns. This is also known as predictive modeling—templates that organizations can use to combine historical and present data to predict business outcomes. Common predictive models include classification model (categorizes historical data), clustering model (sorts data into groups), forecast model (assigns numerical value to data), outliers model (unusual or suspicious data patterns and time series model (sequence of data over a time frame).