Ever since Karl Benz invented the first car in 1886, the automotive industry has seen a significant boom in business, to say the least.
From the first car to the introduction of the Ford Model T, the first mass-market car, to the introduction of the Tesla Roadster, the first all-electric sports car, the growth of the automotive industry has been astronomical.
In the last 140 years, the industry has improved significantly with contributions from visionaries like Gottlieb Daimler, Ferdinand Porsche, Ferruccio Lamborghini, Enzo Ferrari, Kiichiro Toyoda, Henry Ford, and most recently, Elon Musk.
Vehicles have now become very intertwined with our lifestyle and culture.
Along the way, the industry has changed how it has operated. While it once relied heavily on human intervention and intelligence, it now relies on more technology-backed tools to make decisions on design, manufacturing, marketing and sales.
One such widely-used tool is data analytics.
Do you remember the cars of the late 1980s and the early 1990s?
This period is widely considered to be the dark age of automobile design, because of the soap box-like design of the vehicles from that era.
Modern automobiles, however, have ditched these boxy designs in favour of flowing lines and aggressive body panels.
These elements not only improve the look of the vehicle but also improve key performance metrics like fuel efficiency (battery efficiency in electric vehicles), drag, braking performance and speed.
Fuel efficiency or battery efficiency, for example, relies heavily on how much drag the vehicle experiences. The higher the drag, the lower the efficiency.
The flowing design of modern vehicles also improves their aerodynamics, reducing drag, which results in better fuel efficiency.
These design improvements are thanks to data analytics, which helps engineers and designers gain insight into how their designs are impacting the overall performance of the vehicle.
Data analytics can also help engineers and designers create 3D models of the vehicle and run computer simulations based on real-world conditions to measure these metrics.
By leveraging the power of data analytics in the design process, manufacturers can reduce research and development costs and prototyping costs to a significant extent.
Early automotive production relied heavily on manual labour. Even today, certain artisan automotive manufactures use a very high percentage of manual labour in their production lines. Manual labour is slow and inefficient, however.
In response to this, mass markets brands like Toyota and Volkswagen have shifted to a mostly automated production line with minimal human intervention, which is fast, reliable and more efficient than manual production lines.
Volkswagen, for example, produced close to nine million vehicles in 2020 thanks to their optimised production line.
These production lines depend on data analytics to function efficiently and smoothly. Sensors and cameras placed in the production line collect a vast amount of data, which is then processed by data analytics tools and fed into the pieces of equipment that run the production lines.
Without data analytics, the equipment that supports automated production will become obsolete.
According to recent statistics, fatalities as a result of automotive accidents have decreased from 3,798 in 1970 to 1,195 in 2019 in Australia alone.
The decrease in fatalities is largely thanks to better road safety regulations and better safety standards implemented in modern automobiles.
Automotive manufactures are now including more than 50 sensors in their vehicles to collect data from the vehicle and its surroundings, which can then be analysed and used in lifesaving technologies like collision detection, airbag deployment, and driver eye-tracking.
Without big data analytics, this kind of technology would not be as effective as it is now.
Automobiles are a vital part of modern society, given their role in supporting large-scale, life-changing mobility. The industry has constantly made improvements to make transportation better, and data analytics has helped them make these advancements.
With data analytics, automakers are creating faster, safer, and more reliable vehicles. This will only get better in the future.
Today, we see vehicles that are now capable of producing and collecting vast amounts of raw data for automated analytics. Most cars contain at least 50 sensors that are designed to collect detailed information such as speed, emissions, distance, resource usage, driving behaviour, and fuel consumption. When combined with a sophisticated data analytics software, data scientists and analysts are able to transform raw unfiltered data into meaningful information for application in the automotive industry.
The automobile industry has always been a hotbed of innovation and with big data coming into the picture, the disruption has increased significantly. Semi-autonomous cars have already made their way into the market and fully autonomous cars are next in line. It goes without saying that big data has had the biggest impact on the development of these autonomous vehicles.
With estimates from IHS Automotive stating that the average car will be producing almost 30 terabytes of data per day, it’s up to the automakers to decide how best they are going to exploit this data to achieve cost benefits and consolidate their market position.
With large volumes of data being produced now more than ever before, the automotive industry has become mostly data driven. Therefore, as an automobile manufacturer, big data analytics software will allow you to see the unseen, make smart decisions, and derive value from the vast amounts of data to maintain and potentially expand market position and profits.
Our latest blog explores a few ways that data analytics is changing the landscape of the automotive industry.
Big data analytics software empowering the Connected Car
If you own a vehicle that has internet and Wi-Fi, this will serve as an impetus that enables users to stay connected on the go. In fact, it is estimated that 90% of new cars will have connectivity setup by 2020, which will result in big data analytics solidifying its position as an important piece of technology that automotive manufacturers cannot afford to miss out on.
The features offered by these manufacturers for connected cars serve as evidence that it is, in fact, big data analytics software that is all set to spearhead the next generation of technology revolution within the Automobile Industry.
Designing and producing smarter vehicles
Car manufacturers are now using data science to improve driving experience by building smarter vehicles. The collection of fuel consumption and emissions data, for example, enables car manufacturers to reach aggressive fuel economy targets while remaining eco-friendly. If a vehicle is equipped with the proper sensors, mechanics can use predictive analytics to view potential issues before they become problems — a transmission system may be performing below average, indicating the need for early repair work and consequently negating the need for a costly replacement job.
Car manufacturers are even using data analysis tools in the design process by analysing performance-based metrics to determine the most aerodynamic design.
Vehicle Sales and Marketing
A major area where big data is being employed in this industry is customer retention through smart marketing and sales tactics. Automakers understand that trust and loyalty of their customers are of utmost importance and how personalised experiences always result in great numbers for manufacturers.
Hence, aftermarket services making use of data, like predictive maintenance and personalised services, are being integrated with big data analytics on which the automobile industry has now become heavily reliant.
Predictive analytics also accounts for real-time feedback from customers making use of historical data that helps improve services and offerings.
Data analytics software has taken to the F1 circuit
At the extreme end of data science analytics, we have the Formula One racing circuit. In today’s world, the analysis room of an F1 racing team is where high-speed racing meets big data. Racing cars are equipped with hundreds of sensors that provide thousands of data-points for metrics such as tire pressure, fuel burn efficiency, and acceleration & braking patterns when turning corners.
Racing teams often set-up their own private cloud to facilitate data transmission between an off-site analysis centre and the on-site racetrack. In 2015, racing teams at the Grand Prix collected over 243 TB of data, all of which was cleaned, formatted, and analysed off-site so that teams could make the appropriate changes on-site.
Today, we stand on the cusp of automotive innovation, with electric and self-driving vehicles poised to completely change our world. Of course, big data has played a part in this shift, and will only become more important as automotive businesses use analytics for everything from optimising manufacturing to improving customer satisfaction.
Thanks to data-driven predictive analytics, AI, sophisticated data analytics software and other cutting-edge technologies automotive manufacturers are able to build the cars of today while at the same time envisaging and bringing to reality the cars of tomorrow.
For more information on how data analytics software aids in the supercharging of the automotive industry, visit our website.