Predictive analytics is often considered the crystal ball of modern business, since it helps us anticipate consumer behavior, forecast market trends, and, in the case of Waymo, teach cars to navigate—roundabouts. Yes, Waymo, the self-driving car pioneer that recently faced challenges as its robo-taxis struggled with roundabouts. These circular intersections, which often cause mild anxiety for human drivers, have presented a significant hurdle for AI technology, too.
What can businesses learn from Waymo’s experience? Quite a bit. Waymo’s cars are designed to predict the movements of other drivers, cyclists, and pedestrians. However, roundabouts are inherently chaotic; drivers approach them at varying speeds, with confusing signals and sometimes, a degree of recklessness. Predictive analytics thrives on patterns, but roundabouts are where these patterns frequently break down.
In your own predictive analytics efforts, be cautious of the “roundabouts”—those unpredictable variables that defy straightforward forecasting. Sudden market disruptions or unexpected competitor strategies can throw your predictions off course, so always be prepared for the unexpected.
Despite being cutting-edge technology, Waymo’s systems demonstrate that human behavior remains difficult to predict. Whether it is someone treating a yield sign like a stop sign or another driver merging unpredictably, human actions often do not align with algorithms. Even the most advanced predictive models cannot fully account for human unpredictability, so use analytics as a guide, but also allow for intuition and flexibility when the data falls short of telling the full story.
Consider how your analytics might help you identify your company’s own roundabouts, which could mean pinpointing bottlenecks in your supply chain, forecasting customer churn before it escalates, or anticipating when a competitor might disrupt your market position.
