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What Is Automotive Industry Data?

Automotive data is information about vehicles and their users. The range of data is wide: vehicle type, model, movement patterns and speed, road hazards, and more are available for car owners, dealerships, manufacturers, and city planners.

Where Does Automotive Industry Data Come From?

Some basic automotive data like model, make, and year comes from companies and dealerships. Newer cards, however, come equipped with data collection devices that measure car location, speed, date since the last maintenance check, distance from road hazards and gas stations, and more.

Other sources of this data include accident reports, insurance claims, sources like Ward’s Automotive Yearbook, surveys, and apps like Waze.

What Types of Columns/Attributes Should I Expect When Working with This Data?

Most datasets include vehicle type, model, and year. These may include data on fuel efficiency, emissions, safety, and location. However, there are micro and nano-level datasets for specific purposes. These datasets provide information about specific components of a vehicle that manufacturers and safety testers need.

In short, the attributes of your dataset depend on your need.

What Is This Data Used For?

Many different types of people use automotive data. Car owners use it to determine whether their vehicle runs efficiently or whether a new purchase would suit their needs. Manufacturers and marketers use the data to determine whether they should improve vehicle quality or change marketing campaigns. App developers combine the automotive data with geospatial data to advertise local gas stations or other points of interest. City planners use the data to plan public transportation routes and determine where to build parking lots or which roads are most unsafe.

How Should I Test the Quality of Automotive Industry Data?

With more technological advances in the automotive industry as well as pressures to keep intellectual properties confined to one company, testing automotive data is a daunting task. However, the risks of faulty data can be dire. For this reason, you or your data vendor should first ensure that the dataset is complete, updated frequently, and that the data-measuring devices within the vehicle are in good shape. Second, you should ensure the dataset is properly cleaned to be as accurate as possible. Finally, you or your vendor should build safeguards into your data management systems to flag anomalies as soon as possible.

Interesting Case Studies and Blogs to Look Into

Semi Engineering: More Data, More Problems In Automotive
Seica: Automotive industry quality requirements: testing as a key factor – Automatic Test Equipment

Tangible Examples of Impact

“Less data transfer allows for quicker processing, lower latency, and uses less power. In effect, this is like SMS between vehicles.

“But how it will hold up over time isn’t clear. “When it comes to failure rates and design for life, we are just beginning — along with many of the other challenges with autonomous driving and ride sharing — to look at what it means to have people drive cars more than an hour and half a day,” said Lance Williams, vice president for automotive strategy at ON Semiconductor. “This could be a ‘driving 22 hours a day’ type of scenario.”’

Semi Engineering: Automakers Changing Tactics On Reliability

Relevant datasets

Dataforce Data Collection

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Dataforce Annotation

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Annotation adds meaningful labels to data so that they can be used as a means of learning by various systems.

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Cirrantic Market intelligence

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Market intelligence enables you to identify the relevant players, global and regional champions and hotspots of electric mobility.

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