What is Data Science?

Before we delve into the various data science applications, let’s take a quick look at what exactly data science is and why it is considered to be so crucial in today’s world. 

To put it simply, data science refers to the process of generating meaningful insights from large pools of data. This is done by combining statistics, algorithms, and other scientific methods. Over the last few years, there has been enormous growth in this field, and various sectors worldwide, have successfully managed to harness the untapped potential of data science.

Data can be in various forms, from texts, images, and audio to other multimedia forms. They can either be stored in structured, unstructured, or semi-structured formats. Data science makes use of this untouched data, which can actually carry the potential to alter the traditional ways in which we conduct business.

Why Data Science Matters

Perhaps one of the most important reasons behind this huge popularity of data science is its ability to transform data. Contrary to the early days, when data usually used to be present in a structured format and was small in size, today’s technology-driven world is witnessing huge amounts of unstructured data. Therefore there has been a growing demand for advanced analytical tools that can handle such massive amounts of diverse data. Furthermore, data science also brings several benefits that have played a substantial role in its popularity. Some of them might include profitability and efficiency, among others. 

There have been several data science applications across different sectors, such as banks, logistics companies, security purposes, and sales and revenue. In this article, we are mainly going to focus on the application of data analytics in the automotive industry.

Witnessing Change

The marketplace ecosystem of the Automotive Industry is witnessing a rapid change. Their customer’s insight is growing, and so is their demand for digitally better products.

Differences in product preferences, earlier and now stand at opposite ends of a scale. The industry now has to walk all the way through the line to reach its customer’s demand-end.

Globalization, cost volatility, and rapid technological evolution are the primary reasons for the changing marketplace, causing industries to change the way they are operating. And the same is the case for the Automotive Industry, which is taking tiny steps into the revolutionary process change. 

Changing Consumer-behaviour

The revolutionizing environment is bringing various demands on the table. With the technological revolution touching all lives, the customer is growing in a digital space.  

The way cars are used, and unused is changing:

• Rising demand for tech-advanced cars that are digitally connected to the human driving them.
• Pools of networks are offering shared services. There has been a decline in the reason for which people buy cars. Millennials are now more inclined to book a car than own one.
• Subscription models and sharing systems are coming up to change the buyer’s landscape.

With these trends witnessed in consumer behaviour, the auto industry is changing their market strategies. 

• Offering direct-to-consumers buying patterns, by eliminating the dealer’s input.
• Introducing digital adoptions and innovations in vehicles to meet the unprecedented demand from all over the sphere.

But how could an industry know what the demands are and what possibly could be the solution to the ever-changing consumer behaviour?

Why this data?

The marketing strategies used by the industry are also changing with the changing methods that are adopted. ‘This’ data is the information in the form of evidential number-set proof that tells the auto industry that such(part- A) are the changes in the marketplace and such (part-B) should be their way of adopting the change for profit.

Building the customer’s profile and harnessing it to understand their needs will help the automotive industry to win the race.

Industries everywhere are hence working coherently to interpret and analyze these various demands. They are finding solutions to the challenge of meeting needs and surpassing them a step further.

Role of Data in the Automotive Industry

The following list will highlight all the data analysis applications in the automotive product lifecycle, step by step.

Product Development – From analyzing new model configurations to modeling the reliability of components, the use of data science in automotive product development is rampant. Businesses no longer need to build components individually, or test them at every stage, since the same can be achieved with the help of data science through simulation and analysis.

Manufacturing- It is also the responsibility of data scientists to ensure that only the highest quality models are sold. Data scientists closely analyze the entire population of supplies and parts. They are also responsible for forecasting the ability of suppliers to deliver on time, checking the economic conditions, as well as evaluating the financial performance of the suppliers.

Sustainability- Last but not least is sustainability. Every auto company has a specific goal of its own that they need to fulfill. The government is responsible for setting fuel efficiency turrets for every company in this industry. Here is where data science comes into play. With the help of this technology, every company is now being able to optimize the fuel efficiency of their entire line of vehicles. Furthermore, these optimization efforts, also help manufacturers in gaining government credits. The optimization efforts lead to a significant reduction in fuel consumption, while also maintaining the guidelines as stated by the company’s global sales targets. 

With that said we come to an end of the various application of data analytics in the automotive industry. There are also several other added data analysis applications as well, such as predicting customer movement, improving the post-purchase experience for customers, and generating improvements in product quality among others. 

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Challenges Posed

To make vehicles more Millennial-friendly, the challenge is to get into the connected networking ecosystem of the generation.

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The growing ubiquity of the Internet of things(IoT) is making the auto industry wonder of ways to get into this connected web and extract data. Data Science in Healthcare: 5 Ways Data Science Reshaping the Industry

The research into the connected systems and uncovering ways to enter the labyrinth ought to result in useful data extraction; the challenge here is to make their vehicle do the data extraction job.

The ultimate result to customize experiences for the user could win their loyalty.

Data’s Scientist Role

The auto industry is placing new products in the market that are feasible, technologically advanced, and more sophisticated.

Data is the solution’s messenger here.

The industry has to mine this messenger to get deeper. Extracting information and analyzing the trends to create actionable customer segments is the new role of the data scientist. 

The data scientist is using the raw, unstructured data to prepare actionable plans. The big data is helping advance the industry in several ways- from increasing security, building IoT friendly vehicles, using predictive analysis to solve operational issues like- increased cost and uptime, and so on.

Areas to Science-upon:

The use of data has to be up the mark where it will provide automated solutions. 

The vehicle that is being driven will be so human-friendly that it has access to understanding another being’s behavior.

• Research and Development

The automotive industry is working the clock for R&D. The sensors gather massive data from users, and that saves vast in the time and energy perspective of the department’s work. 

The data extracted can be vastly used to bring insight into the vehicle’s usage pattern, environmental consumption of users as well as vehicular emissions. Thus utilizing it for regulatory and marketing benefits of the industry. 

• Manufacturing and Supply Chain

The analytics in this domain is not new. Huge data chunks can be analyzed to rule out operational obstacles like shipment performance (on-time in-full) and their credit valuation. Working on evaluations which empower manufacturers to gain more comprehensive control over their supply chains, including logistics and management. Thus helping in a data-driven and precisely mapped decision control.

• Business and Finance

Data science is in use to extract loads of data to analyze problems. An authentic advantage of this process is to delve into unmarked areas to find problems. Similar is the case with business and finance. Deviating from operational benefits, data science can be used in the bottom line processes of business and finance to introduce efficiency in overall working automation.

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How the Auto Industry is developing?

Incorporating the data analyzed into the reasoning for solutions, following are some developments of data science featuring in the automotive industry:

• Customer Satisfaction

By collaborating the technical and non-technical cadre of teams in the industry, the ultimate aim is to create a deep learning vehicular human-friendly model. The industry works to eliminate the data pain points, thus improving data-driven decision making.

• Cost Control

The sensors in automobiles are in use to collect information on speed, fuel consumption, gas emissions, and security resources.

All of it is in use to find loopholes in ways the machines are being over or underused and thus mapping ways to regulate costs and control the smart use.

• Driving Value

The models that are adopted by the automotive industry ought to be drive-able. The data pipeline undergoes step-wise cleaning to get the ultimate transformed product. The worker is the data scientist here, whose aim is the production of final data to bring change in the operating model.

• Analyzing Market Potential

The data scientists are successful in analyzing potential market trends. By exploring the connected information and disconnected data sources, they can now tap on likely market segments by analyzing buyer’s trends.

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Conclusion

Operating between business standards and developing technology, the industry is wavering head-on with the data tool to revolutionize the market space.