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As of 2015, vehicles are now capable of producing and collecting vast amounts of raw data for automated analysis. Cars contain at least 50 sensors — frequently more — designed to collect detailed information such as speed, emissions, distance, resource usage, driving behavior, and fuel consumption. When combined with a sophisticated predictive analytics solution, such as Dataiku Data Science Studio, data scientists and analysts are able to transform raw unfiltered data into meaningful information for application in both the private & public sectors.
Car manufacturers, in particular, are now using data science to improve the driving experience and to build smarter vehicles. The collection of fuel consumption and emissions data, for example, enables car manufacturers to reach aggressive fuel economy targets while remaining eco-friendly. If a vehicle is equipped with the proper sensors, mechanics can use predictive analytics to view potential issues before they become problems — for example, a transmission system may be performing below average, indicating the need for early repair work (and consequently negating the need for a costly replacement job). Car manufacturers are even using data analysis tools in the design process by analyzing performance-based metrics to determine the most aerodynamic design.
Insurance companies have also started using data science tools to analyze driving behavior. Sensor-derived data is fed to insurance providers, such as the driver’s speed, acceleration & braking habits, turning style, and usage of the vehicle in specific locations and/or conditions. All of this data helps to create a comprehensive driver profile and enables insurance companies to offer more accurate premium costs. Better drivers qualify for lower costs, while those who take greater risks may incur higher premiums.
At the extreme end of data science analytics, we have the Formula 1 racing circuit. In today’s world, the analysis room of an F1 racing team is where high-speed racing meets Big Data. Racing cars are equipped with hundreds of sensors that provide thousands of data-points for metrics such as tire pressure, fuel burn efficiency, and acceleration & braking patterns when turning corners. Racing teams often set-up their own private cloud to facilitate data transmission between an off-site analysis center and the on-site racetrack. In 2015, racing teams at the U.S. Grand Prix collected over 243 TB of data, all of which was cleansed, formatted, and analyzed off-site so that teams could make the appropriate changes on-site.
Progressive-minded governmental organizations around the world are now starting to harness the power of data science by combining automobile-derived data analysis with urban planning initiatives. Sensor-based data is fed into predictive analytics solutions to understand the overall movement of drivers over a large area and, consequently, ascertain when and where the most traffic-congested areas appear. This level of understanding, coupled with data from other sources (e.g., satellite, GPS, cellular phones), provides a holistic view of traffic management across large urban areas. There are several ways in which this information can be used to build smart cities. Typically, the goal is to optimize driving routes so that drivers will not be as inconvenienced and to lessen the environmental impact in certain areas. This information can also be used for ad-hoc event planning, such as sporting events or holiday-based events, that may temporarily affect the smooth flow of traffic.
Dataiku Data Science Studio (DSS) is a powerful predictive analytics solution that enables companies to take advantage of Big Data in an accessible and easy-to-use environment. Some key features of DSS in terms of automotive data analysis include:
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