While deployment of Predictive Analytics is steadily becoming mainstream, its effect is only beginning to be felt by industries around the world. Expect drastic and welcome changes in the years to come brought about by the useful insights learned from data mining and analytics.
Finance
Capital markets now have the increased ability to analyze a large amount of data streams in real time from trading operations and other external sources, helping to expose illegal activity much more accurately compared to traditional methods. These same methods are also able to detect when set trading algorithms deviate for unknown reasons and allowing interventions that can prevent large losses.
Predictive analytics greatly helps consumer finance as well, helping banks detect credit card fraud as the transactions are happening and letting them take real-time action by blocking fraudulent transactions instead of waiting for them to be reported or experiencing a delay in becoming aware of the crime.
Transportation and Utilities
Government departments and train companies now use streaming and predictive analytics to make their track infrastructure as efficient as possible. This helps ensure that passengers are brought to the station platform that is best for them and minimizes delays in the process.
Utility companies benefit greatly from predictive analytics, letting them operate and manage their grids in its optimum form, especially in this time of renewable energy. As more residential and commercial properties generate electricity using solar means, it can be challenging to stay up to date with production and demand. With analytics and streaming data, it is now easy to reduce waste and match production with demand.
Healthcare
It has been evident that one of the most significant changes will occur in the field of medicine and healthcare. Patients with chronic health conditions can be tracked using a smart band that sends out a notification to remote caretakers whenever patients deviate from their usual patterns.
Those under acute care can be given sensors that monitor their vital signs, such as blood pressure and heart rate, and this data is fed into a predictive model that alerts staff if a negative event will soon happen. This model learns these insights by matching current data against the vast history of records covering past patient reactions.
