In this guest post, Alisha Kamat provides an introduction to the rapidly evolving field of data analytics in the automotive industry.
In a nutshell, the article covers the uses and benefits of using artificial learning and machine learning on big data and predictive analysis.
There’s also a section on data analytics jobs in the automobile sector. If you’re a beginner, this should give you a good overview of the field and the opportunities it provides.
Introduction to Data Analytics for Automotive Industry
Benefits and use cases for big data and predictive analytics
by Alisha Kamat
The automotive industry has witnessed a massive makeover over the last several years leading to the disruption of the usual ecosystem of automotive players.
Multiple advancements in the field of data analytics and its connection to the automotive industry has led to smarter, efficient, and more connected vehicles, leading to a radical boost in sales as well as marketing.
The automotive industry has transformed into a data-driven industry lately as so much data is being generated. This has allowed automobile manufacturers to use data analytics to gather better insights into their business and make improved and advanced decisions. This is an important step when it comes to expanding market position and profits.
But these newer advancements in the field of technology and big data analytics have led to a complicated consumer buying behaviour and fuelled a newer array of challenges for companies.
Consumers now require an extra push in the form of concessions on purchases, and additional offers which has led to depleting margins for the manufacturers.
It has been furthered by the industry shifting towards a global supply chain, so automobile manufacturers are now exposed to competition not just on the local level but on the global level too.
How is data analytics used in the automobile industry?
Big data analytics forms the basis of all other applications as large chunks of information are being gathered and organised for use.
Major use cases include changing the automotive business, supporting mechanization, and boosting automation.
In addition to this, the effective utilization of big data is assisting automotive players in exploring newer ideas and using materials having extraordinary advantages as compared to those earlier. This big step will not only help in tremendous expense reduction but also lead to better vehicle quality.
Manufacturing safer and superior quality vehicles requires a data-driven approach. Data science can lead to better mobility solutions with more connected and autonomous vehicles.
How big data analytics will impact the automotive industry?
Today, automotive innovations like electric and self-driving cars have completely changed the world. This major advancement in the automotive industry wouldn’t have been possible without big data analytics.
Big data in the field of automotive industry includes data on consumer behaviour, preferences to data on driving patterns, and locations.
Many applications of AI rely majorly on big data, emphasising the need for automotive engineers to better understand data analytics.
How is predictive analytics used in automotive industry?
In the automotive industry, predictive analytics is massively used to understand basic consumer purchase trends and to make predictions for the future using techniques such as data mining / modeling, machine learning (ML) and artificial intelligence (AI).
Quality management teams can now process a larger chunk of data to reveal the underlying reason with the use of predictive analysis. It assists in the early detection of faults and reduces the chances of its occurrence in the near future.
How does data science drive sustainability initiatives?
Sustainability is a crucial aspect for all automotive manufacturers. Each automotive company has its own goals when it comes to setting targets for fuel efficiency. Data science is crucial to optimize the fuel efficiency of a company’s entire line of vehicles as each one has its own set target.
For companies offering both, large gas-guzzling SUVs as well as electric vehicles in its product line, automotive data scientists can perform optimizations to minimize the fuel consuming capacity of the entire set of vehicles while also sticking to its global sales targets.
Automotive companies manufacturing new generation vehicles that push the boundaries of optimization (such as designing fuel efficient automobiles) can get government credits for their efforts.
This provides more value to its customers while also being completely environment friendly and opens up a potential source of income.
Using Data analytics to manage growth in the supply chain management and control risks
Organising customer information for data analysis key for any business and the automotive industry is no exception to this. Nowadays, customers perform a thorough research before taking the final decision to buy any vehicle.
It generates a huge volume of information that the automakers can leverage, to understand the competition and the trends that are driving the industry.
This data is generated across all categories of sources making it increasingly difficult to gather and analyse the available information.
Using big data analytics, sales and marketing teams can understand the levers that have worked in the past and help understand the situation at hand.
When done correctly, automakers can enhance customer engagement and interaction with their brand through more targeted, controlled and informed sales and marketing initiatives.
Data science is involved in every step of the automotive product life-cycle.
Other uses of advanced analytics in the automotive industry
One of the use cases includes, helping suppliers in identifying defective parts in advance in the early stages of manufacturing.
With these possibilities of analysis, predictive data analytics is particularly reliable in the development of initial testing prototypes, in quality management, and in supply chain optimisation.
As observed, it becomes pivotal to harness the data from the supply chain of the automotive industry to open up benefits including increased sales, lesser downtime risks and a lean supply chain.
Good automotive analytics practice can go a long way in attaining a sustainable competitive advantage over others.
Big data within the automotive industry is also extremely valuable when it comes to marketing vehicles. With data analytics, car companies can analyse their existing customer groups and identify traits that help predict a purchase.
Big data can also help the automotive industry maintain and manage several insights like prior vehicle purchases, online user behaviour, and demographics to develop personalized marketing communications including sharing relevant content.
Companies can also use it to identify strategic locations for such auto dealerships to guarantee maximum customer retention.
Data analytics jobs and careers in automobile sector
For anyone who follows the developments in the automobile sector, the indications and lessons are very clear. Companies in the automotive industry have no option but to embrace big data analytics and integrate it within their business process.
This includes upgrading in their technology and enabling their IT systems to start collecting and analysing big data sets using machine learning and artificial intelligence.
This in turn compels them to hire the best professionals for jobs in big data analytics, predictive analytics, AI, ML and other related technical skills.
If you’re interested in data analytics jobs and careers in the automotive sector or any other industry for that matter, start picking up the technical skills as well as industry knowledge. You’ll need a good combination of the two to impress the recruiter during big data analytics job interviews.
Let me know in the comments if you found this article helpful.
Check out this application I’ve made (called CarDB) that highlights the various ways in which data analytics can be used to understand the car industry.
About the author: Alisha Kamat is a Computer Engineering student at VJTI (Mumbai) and a Google Summer of Code (GSoC 2022) contributor, with a keen interest in technology. Connect with her on Linkedin.
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