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The automobile industry is banking on data science to make better and safer vehicles. The automotive industry is on the leading edge of technology, transforming the way people travel. What's the difference between the automobile industry a decade ago and the industry now? The key difference is data-driven innovation, not just manufacturing.
Data science is making transportation easily accessible to everyone, especially to the lower-income groups. They are experiencing the ease of traveling without having to deal with the high costs of ownership. As a matter of fact, this is bringing change for everyone, without any bias.
For example, optimization algorithms can provide businesses with vehicles that are energy efficient that depend on deliveries like Amazon deliveries and food delivery. Data scientists are working with reliability engineers to manufacture vehicles that help differently-abled communities. These are just a few examples of how data science is facilitating change in the industry. But there are endless applications that are unexplored.
The automotive industry is a widely used and profitable industry. This means there's more scope of customer-centric innovation with data. One such use case is working with data across various data systems and data types. Typically, data scientists work with a tabular form of data, similar to excel. But automobile scientists can work with a variety of data forms. For instance, raw instrumentation data is typically stored as a stream of hexadecimal digits. They can also use data from intelligent systems which come in the forms of images and sensor point clouds. These point clouds can be combined with instrumentation data and join that to a set of tables to understand why an autonomous vehicle works in a certain way and that differs with every vehicle model. Another advantage this industry has is the huge volumes of data. Due to that, many companies in the automobile industry have data chunks that go until petabytes (a million gigabytes).
Data science is a central part of every automobile product cycle. Before a vehicle is manufactured, there are several steps that follow. Data science is involved in the preliminary step of product development. It enables tasks like analyzing new model configurations and making the components reliable. Instead of testing every component individually, data science scales the process through simulation and analysis.
Automotive data scientists aim at delivering only high-quality vehicles. While the engineers test the vehicle through multiple quality checks, this is a time taking process as the tests are done individually. Data scientists can analyze all the parts, suppliers, and test data. In this sense, they closely examine the financial performance of suppliers, predict their availability to deliver the parts on time based on past performance, and use econometrics regressions to check the economic viability of the supplier's location.
Autonomous vehicles are a hot topic in the automobile industry. This relies on deep learning models and sensor fusion algorithms. Data science is used to translate IoT indicators like battery change monitors into actionable insights. When the vehicle is put to use, it's not enough if the system detects a pedestrian, the sensors must be able to identify which way they are going.
Manufacturers of every industry are shifting towards sustainability. Governments are setting targets for fuel efficiency and every vehicle has different fuel efficiencies. So data science is crucial to optimize the efficiency of all the vehicles of a company. This will not only help companies earn government credits for fuel efficiency but also be good for the environment and give customers a more value-added vehicle. Beyond this, data science also impacts other aspects like marketing, sales, and predicting customer demand. It also improves customer's post-purchase experience.
