Innovation and technology in the automotive industry have played a significant role in advancing the industry. Automotive analytics have played a significant role in the rapid development of this industry in recent years. In addition to decreasing repair costs and increasing vehicle safety through cognitive IoT, auto dealer analytics and business intelligence automotive to further regulate the industry’s growth.

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As this industry undergoes a digital revolution, professionals now have new opportunities to enhance their skills and take advantage of this trend. In these areas, the auto industry is leveraging business intelligence and data analytics to stay on top.

Automotive Industry Development with Business Intelligence

  1. Retail Change: The digital economy is challenging retail standards and the aftermath of automobile companies. With 24/7 connectivity, expanding access routes, and a seamless digital experience across all engagement channels, car manufacturers now offer a seamless customer experience. With Adobe’s help, Audi is able to provide a consistent branding experience. Now, Audi’s website offers a whole new brand experience that includes current news, dealer links, vehicle guides, and the ever-popular Audi Configuration app, which lets visitors customize their dream vehicle.
  1. Analysis of customer satisfaction: Cars have 50 or more sensors that collect information on speed, emissions, fuel consumption, resource consumption, and safety. In this way, we can identify patterns and prevent issues from recurring over time. Customer satisfaction is increased and quality management is improved to an effective level with an analysis. For automotive product recalls, major forecasting and predictive analytics tools are often used to reduce development risks. Using data collected from cars, businesses and the government are combining predictive analytics to forecast and identify places with high congestion. The information obtained from vehicle data and other sources such as satellites, cell phones, GPS, etc. can help solve urban issues such as traffic management, resource allocation, and environmental issues.
  1. The Next Level of Automotive Business Intelligence in F1: Data Analytics and business intelligence tools have revolutionized F1 racing teams. Computer science combined with high-speed racing provides new high-tech measurements to measure performance using data points on tire pressure, cornice braking patterns, fuel efficiency, acceleration time, etc. To improve performance and fix issues, offline data centers are configured for each computer to deliver real-time data on the road. Dataiku reports that, in 2015, the U.S. Formula 1 teams collected more than 243 TB of data, which was cleaned, formatted, and analyzed off-site so the teams could make appropriate adjustments on-site.”
  1. Using predictive analytics to optimize customer purchasing patterns: IBM says, “Predictive analytics allows companies to understand what drives customers’ purchasing patterns so that they can predict which products they want, how much they want, and when they want them.”. Other prescriptive analytics optimize production planning, scheduling, inventory, and supply chain logistics to meet business objectives. Combining mathematical algorithms, machine learning, and artificial intelligence can provide a prescriptive analytics solution. Automotive is about to catch up to sectors such as banking and retail that use marketing mix analytics. An advanced analysis allows auto manufacturers to identify trend features, custom products, and options such as automatic transmissions or color changes. Those reports are then used to provide companies with a level of detail.

Have you heard that Big data can help prevent accidents?

According to a study, there were about 718 accidents per hour in the United States in 2018. Moreover, onboard computing can reduce accidents by building a platform for onboard computing. In response to changing road conditions, onboard computing would be able to provide real-time processing and reaction. The use of sensor data for lane departure warnings is an example of edge processing.

It provides real-time alerts to drivers and passengers. Big data is also being used to develop other safety features, such as:

  • Automatic braking
  • Alert for collisions
  • Rear-view cameras
  • Distance control automated
  • Parking assistance
  • Leaving the lane warning

A couple years ago, only 5% of cars had advanced driver-assistance systems. Vehicle computing resources are costly to develop and install, among other factors. It is also possible that the process of updating software over the air may pose a challenge. These updates should be downloaded, backed up, and verified for integrity.

Essential Takeaways

The automotive industry is experiencing a period of innovation. Our everyday lives are being transformed by innovations we could only imagine in the past, such as fully electric cars and self-driving cars powered by Artificial Intelligence. Our world has changed, and Business Intelligence can be credited with significantly contributing to this progress. Increasingly, automotive businesses must make full use of analytics to optimize their manufacturing, customer service, and other business processes.

In the era of big data, software can now help the automotive industry design and build better cars today while helping the automobile industry create cars of the future.


With improved on-road performance and driver experience, vehicles will be better in the future. With big data, automobiles will surely provide more surprising and interesting features in the future. It’s only a matter of time. The Business Intelligence in automotive industry has gone through enormous growth and transformation since big data analytics was introduced. It enables manufacturers to catch information successfully from a variety of sources and to manage these informational indexes for explicit business settings in order to increase their revenues and to improve their clients’ experiences.

By Mr Stew

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