Predictive Analytics

Many of us wear some kind of fitness tracker, such as a smartwatch or smart ring, or track fitness data using an app on our phones. These trackers will notify us when we need to get up and move, tell us when we should go to bed for optimal sleep, or suggest when we may need to practice some breathing exercises. These are personalized notifications—so how does the tracker know what our bodies need?

Smart devices use predictive analytics to provide us with tailored health recommendations. While we wear them, they track various metrics, including heart rate, steps taken, calories burned, and sleep quality. Through the daily collection of this data, these devices begin to understand our habits and activity patterns. Your tracker may notice that when you get less than seven hours of sleep, your energy tends to dip around 3:00 pm. Using predictive analytics and machine learning, the tracker may notify you to take a short walk or drink water near that time, as it predicts you may be feeling fatigued. It would also recommend that you wind down earlier that night and perhaps practice meditation to ensure you get a good rest. 

Predictive analytics uses statistics and modeling techniques to find patterns in historical and current data and predict future outcomes. It forecasts how trends may continue to provide recommendations on what adjustments should be made. Predictive analytics underlie how we determine weather forecasts, how our phones suggest auto-responses, how streaming services recommend shows, and how Google Maps provides the fastest route. Predictive analytics help to optimize our daily decisions, shaping our experiences in ways we often do not notice.