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IoT Data Analytics in the Automotive Industry

IoT Analytics in Automotive: Advantages, Challenges and Use Cases


How to leverage all the data collected by sensors? How to get insights from it? Will they help draw data-driven business decisions? One of the ways to get answers to these questions is to apply data analysis. This article will explain why IoT systems in the automotive industry need analytics for proper operation and how critical decisions can be made based on this analysis.

IoT analytics is the application of data analysis tools and procedures to realize value from the huge volumes of data generated by connected Internet of Things devices. It is an innovative and challenging area. Sensor, mobile, and wireless technologies drive its evolution, but the actual value of the IoT lies in Big Data analytics rather than hardware modernities. Being software developers pioneering IoT analytics, we will explain how it enhances transformative business opportunities.

Primarily due to data analytics the automotive industry has evolved rapidly over the past decade. Data analytics helps the sector grow in many ways - by improving safety, reducing repair costs, extending uptime, and much more. It brings a ton of unique opportunities for experts in the field, helps to decide where to invest funds, recognize opportunities for growth, predict incomes, and handle unusual situations before they convert into problems.

In the article "Big Data Analysis: The value you can get from the data" we describe key benefits that companies across various industries can get from big data.

illustrative image of data analytics process

Data analysis types

There are many types of data analysis in the automotive industry. Below you can find their main types.

  • Descriptive analysis

  • Exploratory analysis

  • Diagnostic Analysis

  • Predictive Analysis

  • Prescriptive Analysis

Now let's take a look at each technology and what benefits it brings.

Descriptive Analysis

This method is the opening point to any analytic process, as it intends to explain what happened. Its tools are ordering, manipulating, and interpreting raw data from multiple sources in order to turn it into valuable insights for the business.

Conducting descriptive analysis is crucial because it enables us to display our data in a meaningful way. However, it is important to mention that using this method solely will not let you predict future results or answer questions like why something happened. Descriptive analysis, though, will organize your data, so it is ready for further analytics.

Exploratory Analysis

Data professionals use exploratory analysis to investigate data sets and compile their main characteristics, often employing data visualization methods, enabling them to find connections and generate hypotheses and solutions for specific problems. In addition, exploratory analysis helps determine how to handle data sources to acquire the insights you need, making it easier to discover patterns, spot anomalies, or test theories. It does so with the help of summary statistics and graphical representations.

Diagnostic Analysis

This one is about the reason something happened and answers the "Why?" question by determining the cause of events. This type of analysis is helpful to identify performance patterns of the given data. If you know why something happened and how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Predictive Analysis

Predictive analysis tells you "what may happen". It makes predictions based on current or past data. We need to bear in mind that forecasting is only an estimate, which accuracy is based on the amount of detailed information you have and how correctly you use it.

Prescriptive Analysis

Prescriptive analysis consolidates the insight from all previous analyses and decides what is best to be done. Most data-driven automotive businesses are using this type of analysis as predictive and descriptive ones are usually not enough to improve performance. So they analyze the data and make decisions based on current situations and problems.

Data engineers working on the problem solving

Benefits of data analysis in the automotive industry

Automotive is presumably one of the top industries that experience the benefits data analysis provides to the fullest. Data analysis use cases in the automotive industry are numerous, and all of them launch a wholesome new niche inside the market.

Carmakers have been using data analysis for quite a long time. They design and develop custom solutions to improve clients' experience. With the help of data analysis, we can make vehicles safer, enhance driving experiences, and increase the level of service. The data may be collected from different sources simultaneously: from the car, its driver, the surroundings, and, of course, from special devices.

Connected cars are getting even more connected and automated thanks to IoT. Data analysis enables car manufacturers to monitor engines and manage powertrain performance.

When it comes to vehicle fleet management, data analysis can help with preventing speeding and aggressive driving by collecting big data from the sensors and interpreting it according to the pre-set requirements. Such technology may help keep cars' peak performance and minimize endangered vehicle usage.

For the mass market, big data analysis is just as beneficial. After predicting the possible issues car manufacturers may notify drivers about car maintenance needs and so preventing unexpected breakdowns.

Predictive data analysis can be advantageous for insurance businesses as well, as it allows companies to predict the behavior of the driver and monitor the driver's safety. It might also be beneficial in case of an accident, as the data collected from a vehicle is reliable and accurate. This helps to reduce the number of false claims, which benefits insurance companies and legal authorities.

Data analysis is helpful for improving traffic flows, it helps to define the best place to build rest areas and understand where to locate the traffic lights or pedestrian crossing. Improved alarm systems can alert people about weather conditions, construction works, or sharp turns, which all serve human safety and comfort.

Inspiring automotive use-cases

It's always easier to grasp the full power of technology when you see a real case with actual companies, data, and results. So here are several success stories of data analysis making industrial magic happen.

Data analysis on rails

Let's take Siemens as an example. Having more than 150 years of experience in train engineering, the company connected its innovations to data analytics. It now uses sensors to analyze information on trains and tracks, which helps to move railway maintenance methods from reactive to predictive. Siemens can detect patterns that indicate when a breakdown is likely to happen by assessing the condition of components through diagnostic sensor data. As it's possible to monitor information in near real-time, the producer can promptly react to concerns before they disrupt the system. When an anomaly is detected, it just sends the part for inspection.

The benefits of this approach are:

  • A reduction in delays

  • Increased mileage

  • Lower labor costs

  • More efficient maintenance scheduling

Gerhard Kreß, director of mobility data services at Siemens, explained that his customers invested a lot into assets like trains, rails, and signaling. Hence, their task was to help them gain better results. Commenting on technical details, Mr. Kreß added that the data they used was from received rail vehicles and the infrastructure. As they are complex systems with their own diagnostic elements, they took everything they could learn from the onboard diagnostics and augmented that with sensor data and log file data.

Sensors on an Internet of Trains, named after the Internet of Things, monitor almost everything: door position, vibrations, engine temperature, and machine data. It is all used to show how the system is operating.

Heading for Data-Driven Rail Systems: a pit stop for rail vehicles
Heading for Data-Driven Rail Systems: a pit stop for rail vehicles

In general, Siemens started offering clients more performance-based maintenance contracts due to the implementation of data analysis.

Data analysis can also be used to explore the patterns of train delays and make long-term delay predictions. Pu Wang and Qing-peng Zhang, Chinese scientists, are the ones who developed an ML model for such predictions. They help both passengers wishing to plan their journeys more reliably and railroad operators create more effective rail timetables and reasonable pricing strategies. They collected multi-source big data sets, which included train schedules, train delays, and weather data. Based on their analysis of the impact of various factors on train delay time, the scholars chose three factors as the input features of the prediction model. They used data collected during the first 75 days as the training set and the one collected during the final 15 days - as the testing set. As we can see, using the model, we can predict the trend of train delays quite reliably.

Prediction results of train delay times
Prediction results of train delay times

Understanding the mechanisms of train delays and the ability to predict them helps railway operators improve their train management plans.

Data-driven cars

Automakers, drivers, and citizens benefit from data analysis as well. Modern cars can contain more than 50 sensors that collect data on speed, fuel consumption, emissions, and security. We can use all this data to instantly detect patterns, resolve quality issues, and prevent them from happening. Analytics is used to improve customer satisfaction and quality management quickly and cheaply.

Car recall used to be a common menace in the industry. Today Predictive Analysis is actively applied to decrease the risks of recalls. Modern manufacturers use Predictive Analytics to collaborate with the government so that they can identify and predict high-congestion zones using data from automobiles for urban planning and building. By combining insights from automotive data and other sources such as GPS, satellites, cellphones, etc we can deal with urban issues like traffic management, distribution of resources, and environmental problems.

Car manufacturers now offer round-the-clock connectivity and digital experience across all engagement channels. For instance, Audi cooperated with Adobe to provide visitors of their website with a completely new brand experience: news, links to dealers, and vehicle guides. An interactive application called Audi Configurator enables their customers to design tailored cars. The role of the dealer is now to use opportunities and transform the existing retail experience on offer.


Major sectors of the automotive business are growing and getting smarter every month. Gathering data isn't enough now, as the global market makes the competition stronger than ever. Thus, data analysis is necessary for every business aiming for the future. Aside from offering low-cost computation, storage, and operational capacities, data analytics can save a lot of time and, as a result, costs.

If you are considering investing in IoT data collection in the automotive industry, now is a great time to get on the bandwagon since there is so much you can achieve with IoT analytics.


We at Intelliarts AI love to help companies with solving challenges through data strategy design and implementation. If you have any questions related to ML pipelines in particular or other areas of Data Science — feel free to reach out and get our experts' consultation.


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