Data analytics is playing a major role in the high-tech developments of the automobile industry. Data harnessed better makes the everyday lives better. Here is an article on how data analytics is shaping the future of automobiles.

1) Product Recall & Customer Satisfaction

Data aggregation increases everyday as automobiles move towards a connected environment. Big Data Analytics solutions enables a complete user experience. In addition, big data includes analyzing customer data from social media, call center enquiries, and sales data from the ERP.

Predictive data analytics helps to reduce product recall risks. Besides, big data enables automakers to determine patterns and resolve quality issues. This leads to better customer satisfaction and quality management.

2) Manage Risks & Drive Growth in Supply Chain Management

Better data visibility and data analytics helps automobile makers to reduce inventory stock-out risks. Supply chain analytics models enables to adopt proactive decision making by getting a real-time view of the inventory stock. Data analytics in automotive supply chain are used in the following:

• Supply chain optimization: Find potential flaws in the automotive supply chain and proactively take measures to optimize the supply chain.
• Visibility tracking: Track resolved and unresolved product issues and investigations. Highlight issues related to shared suppliers.
• Governance and oversight: Ensure supply chain governance with established oversight responsibilities, better communications, and reporting among stakeholders.

3) Streamline Sales & Marketing

Customers research in detail before deciding to buy any product. So, streamlining customer information is important. Customer analysis enables automakers to understand both the competition and the trends in the industry. Besides, they can enhance the customer interaction through targeted sales and marketing schemes. Automobile sensor data helps automakers to determine factors such as mileage, repair history, vehicle age, and social media data to find new sales opportunities.

4) Determine Traffic Congestion and Set Insurance Premiums

Modern cars contain about 50 sensors gathering data related to speed, fuel consumption, fuel emission, and security. Moreover, automobile leaders use predictive analytics solutions and work with the government to predict high traffic areas based on automobile sensor data.

Data related to speed, acceleration, driving behavior, turning styles, braking habits, and abidance with traffic rules are gathered. Consequently, the data helps to create a driver profile. Insurance companies provide different premiums based on the driver profiles. Furthermore, it enables insurance agencies to deliver better services and reduce costs.

Here are some of the use cases of data analytics in Automobile Industry

1) Big Data Analytics in F1 Circuits

High-speed racing combined with big data analytics is enabling a new high-tech metric based on data points from acceleration time, tire pressure, fuel burn efficiency, and braking patterns around corners. Each team sets up data centers to get real-time track data, fix glitches, and increase performance. Dataiku reported that the racing teams at the 2015 U.S. Grand Prix gathered over 243 TB of data. The data was cleaned, formatted, and analyzed so that the teams could make the necessary changes.

2) Automobile Financing

Finance companies collect customer data and analyze the customer financial history. The financial providers offer personalized schemes by analyzing customer data according to demographics and geography. Such custom services generate more business leads and helps customers to stay away from fraudulent dealers.

3) Connected Automobile

There are more than 20 million connected GM vehicles on the road. GM Motors is collecting data to improve its automobiles. For example, Verizon Connect Fleet for GM helps automobile owners track stolen vehicles and unlock their automobiles using a smartphone. GM Motors also works with Apple and Google to provide infotainment in its connected cars.

4) Driverless cars

OTT video-streaming providers partner with driverless car manufacturers to deliver infotainment. OTT applications provide tailored marketing to smartphones and infotainment systems. Automobile makers partner with ride-hailing service companies to promote driverless taxi services. For example, Daimler AG supplies self-driving cars to Uber for ride-hailing services. And General Motors partners with Cruise Automation to manufacture driverless cars that can make logical decisions.

Tesla’s autopilot software has accumulated data from more than 1.3 billion miles. The data includes where the driverless cars have slowed down or deviated around obstacles. This data helps to produce roadmaps and make intelligent decisions even in any condition.

Roadside assistance providers leverage self-driving data and distress calls to send rescue resources on time. Similarly, battery recharging dealers, toll operators, and refueling operators analyze the driving data to efficiently deploy services at specific place. As a result, insurance agencies collaborate with roadside assistance providers to formulate usage-based insurance policies.