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The future prospects of using visual exploration in the automobile industry are promising. As seen in the example of Tesla Motors' Data Scientist, Anmol Garg, visual exploration can be used to tap into the vast amount of sensor data produced by cars. This data can be visualized to monitor various aspects such as tire pressure over time. This can help in identifying 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. It can also be used to find leak rates and do some predictive modeling on when tires are likely to go flat. Thus, visual exploration can play a crucial role in predictive maintenance, quality control, and enhancing customer service in the automobile industry.
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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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