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Other predictive models that can be developed using car sensor data include models for predicting engine failure, fuel efficiency, brake wear, and battery life. These models can help in proactive maintenance and improving the overall performance of the car. Additionally, sensor data can be used to develop models for predicting driving behavior which can be useful in insurance and safety applications.
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As an example, Berinato uses Tesla Motors' Data Scientist, Anmol Garg. Garg has used visual exploration to tap into the vast amount of sensor data Tesla cars produce. He developed an interactive chart that shows the pressure in a car's tires over time. "In true exploratory form, first created the visualizations and then found a variety of uses for them: to see whether tires are properly inflated when a car leaves the factory, how often customers reinflate them, and how long customers take to respond to a low-pressure alert; to find leak rates; and to do some predictive modeling on when tires are likely to go flat. The pressure of all four tires is visualized on a scatter plot, which, however inscrutable to a general audience, is clear to its intended audience," Berinato writes.
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