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Automotive Industry

Introduction to Automotive Analytics

As of today, 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.

Build Smarter Vehicles

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.

Automative Analytics - or Telematics - in the Insurance Industry

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.

Big Data in Automobile Analytics

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.

Using Automotive Analytics Data to Build Smart Cities

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.

Using DSS to Understand Automotive Analytics Data

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:

  • Versatile Data Connections: DSS can connect to 25 different data sources, including but not limited to Hadoop, Cassandra, or Excel. Data types are recognized automatically and data can be pushed out to any target. This capability is particularly important in the automobile design process, where Big Data connections to data-collecting sensors are needed in order to understand the effects of design changes on aerodynamics;
  • Intelligent Data Cleansing: With DSS, there is no need to spend countless hours in the data preparation stage. Your analysts will be free to spend their time discovering models and visualizing the data instead of performing the tedious task of data cleansing. DSS automatically discovers data errors in your data and, with over 85 integrated click-and-go processors, your analysts can quickly cleanse and enrich data;
  • Machine Learning: Use integrated algorithms from scikit learn, MLlib, or H2O or choose to code your own for supervised and unsupervised learning. Models can be easily trained and optimized so that they can be deployed to your system quickly. Predictive models can be built to discover critical information, such as traffic movement patterns, driving behavior, peak fuel usage, and vehicle component wear.

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