Visualizing sensor data can be challenging due to several reasons. Firstly, the sheer volume of data generated by sensors can be overwhelming, making it difficult to identify patterns or trends. Secondly, the data may be in a raw, unprocessed format that is difficult to interpret. Thirdly, the data may be dynamic, changing in real-time, which can make it difficult to capture and analyze. Lastly, the data may come from different types of sensors, each with its own unique characteristics and measurement scales, which can complicate the visualization process.
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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.