Data analysis: the driving force of automotive

IoT Analytics in Automotive: Advantages, Challenges, Use Cases

 

What can we do with all the data we get from sensors? How to draw insights from it? Will they help us draw important decisions regarding the business? 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 are made based on such 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's enhancing transformative business opportunities.

The automotive industry has been rapidly developing for the last decade, and primarily due to data analytics. It 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 to benefit from this trend. In our article "Big Data Analysis: The value you can get from the data" we describe key benefits from big data opportunities across different industries. Data analysis helps them understand where to invest their money, recognize opportunities for growth, predict incomes, and handle unusual situations before they convert into problems. Manufacturers in all the major industries, including automotive, heavily invest in IoT Analytics to improve their performance.

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Data analysis types

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

  • Descriptive analysis

  • Exploratory analysis

  • Diagnostic Analysis

  • Predictive Analysis

  • Prescriptive Analysis

Now let's see what each technology means and what the differences between them are. Also, we will explain how you can benefit from each of them.

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 to 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 analysis.

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 answers 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 analysis are usually not enough to improve performance. So they analyze the data and make decisions based on current situations and problems.

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Benefits of data analysis in automotive

Automotive is presumably one of the top industries that experience the benefits data analysis provides to the max. Its uses 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 solve issues with speeding and careless drivers by collecting big data from the sensors and interpreting it according to the pre-set requirements. Such technology may help keep cars' peak performance as well as save vehicles from misuse and inadequate treatment. For the mass market, big data analysis is just as beneficial. Car manufacturers will notify drivers about all the car's needs after predicting the possible issues.

Predictive data analysis can be advantageous for insurance businesses. Companies may both predict the behavior of the driver and monitor driver safety. Not everybody will agree on being monitored while driving, but safe drivers can obtain better discounts due to data from connected cars.

It will also be beneficial in case of an accident. The data collected from a vehicle is much more reliable than even witnesses. The number of false claims will fall notably, making it easier for insurance companies and legal authorities.

Data Analysis can improve traffic flows; it helps to find the best place to build rest zones. Also, with data analysis, we could better understand where to install stoplights. 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 has recently connected its innovations to data analytics. It now uses sensors to analyze information on trains and tracks. It has helped them 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 numerous:

  • 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 augment that with sensor data and log file data.

Sensors on an Internet of Trains, named after 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.

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 understand the patterns of train delays and predict the time of train delays. Pu Wang and Qing-peng Zhang, Chinese scientists, developed an ML model to predict the delay time. It appeared to be helpful for both passengers wishing to plan their journeys more reliably and railroad operators creating 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. Now Predictive Analysis is actively applied to decrease the risks of recalls. Modern manufacturers are also using 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. We now can deal with city issues such as traffic management, distribution of resources, and environmental issues by combining insights from automotive data and other sources such as GPS, satellite, cellphone, etc.

Car manufacturers now offer round-the-clock connectivity and digital experience across all engagement channels. 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 to change and complement existing retail experiences on offer.

Conclusion

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 a must 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, money.

Companies could be a lot more proactive to the demands of their stockholders/clients as it is always fresh data, which has a huge monetization potential.

If you are considering investing in IoT data collection, 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 to solve the challenges with data strategy design and implementation, so if you have any questions related to ML pipelines in particular or other areas of Data Science — feel free to reach out.


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