Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future activity, behavior and trends.
“Our predictive analytics experts help companies go beyond what has happened in the past to providing a best assessment of what will occur in the future.” – Perry Lea, RUMBLE co-founder
Marketing, financial services and insurance companies have been notable adopters of predictive analytics, as have large search engine and online services providers. Predictive analytics is also commonly used in industries such as healthcare, retail and manufacturing.
For example, predictive maintenance has emerged as a valuable application for manufacturers looking to monitor a piece of equipment for signs that it may be about to break down. Employing the internet of things (IoT), manufacturers are attaching sensors to machinery on the factory floor and to mechatronic systems, such as those found in automobiles. Data from the sensors is used to forecast when maintenance and repair work should be done to prevent problems from occurring.
“An organization that doesn’t leverage its data using predictive analytics is like a person with a photographic memory who never bothers to think.” – Eric Siegel, author of Predictive Analytics: The Power to Predict Who will Click, Buy, Lie or Die
IoT also enables similar predictive analytics uses for monitoring oil and gas pipelines, drilling rigs, and various other industrial IoT installations. Financial institutions are using predictive analytics to identify and prevent fraudulent transactions by flagging those which deviate from a standard customer behavior based on previous transactional history and geographical locations. Retailers use predictive analytics to better tailor a customer’s experience online and in store and to drive more informed decisions on what types of products should be in stock.