23 Jan 2018
4 Ways Predictive Analytics is Helping the Connected Car Industry
Harish Thyagarajan
#Analytics | 5 min read
4 Ways Predictive Analytics is Helping the Connected Car Industry
Harish Thyagarajan

The growth of the connected car industry provides a good number of challenges as well as opportunities for the automotive sector. The biggest challenge being data management. Connected cars are now increasingly streaming data from telematics systems into the cloud. The key to utilizing this huge chunk of data wisely depends on the data analytics and predictive analytics.


Here are some of the ways as to how predictive automotive data analytics will drive the rapidly growing connected car industry.

Predictive Maintenance

Predictive maintenance aims to clearly identify vehicle maintenance issues before they occur. By extracting data from repairs with vehicle sensor data, predictive data analytics can find some useful correlations that would be hard for humans to identify.


A performance inconsistency that may appear irrelevant when observed on a single car can be a red flag when it is aggregated with data from millions of other vehicles having similar issues. The modern-day analytical applications can extract relevant data from pretty much every vehicle of a given year or model and compare that with the warranty repair trends.


As predictive analytics has access to larger datasets, the automakers can help your connected vehicle in spending more time on the road and less time in the shop.



Predictive Collision Avoidance

One of the best examples of a PDA (predictive collision avoidance) system is the predictive forward collision warning feature developed by Nissan.


With the help of advanced sensors, big data and vehicle to vehicle connectivity, predictive analytics technology may one day make auto accidents a things of the past.


By using sensors on the front of the vehicle, the system will be able to analyze the speed as well as the distance of the vehicle traveling ahead of Nissan and that of the next preceding vehicle, which is generally outside the driver’s field of view.


When either of the preceding 2 vehicles behaves in a manner that could force Nissan driver to brake abruptly, the system alerts the driver with a visual alert and audible signal. A signal is also sent to temporarily lock the seat belts in case of impact.


Nissan’s noble effort represents the most fundamental example of a predictive collision avoidance system. As modern-day developers create apps that enhance communication between connected vehicles, more complex and effective collision avoidance systems will emerge based on the predicting driver behavior.

Automotive Marketing

Apart from managing their products, one could agree that the most important thing the automakers do is to attract new customers. Failure, here, means a decline in market share or worse.


Old marketing strategies have kept most car makers afloat, however, TV or print media are losing their effectiveness as an advertising medium, as the audience size has been steadily declining.


Today, every penny that an automaker spends on advertising has to go further than ever, and the increasing need for nurturing repeat business cannot be overstated.


Predictive analytics is ideal for assisting the automotive industry in tackling these marketing challenges. With the help of big data and predictive analytics, the modern day applications can accurately identify and classify the segment of people that are interested in buying a car.


Complex algorithms take into consideration some factors such as the total number of repairs on the current vehicle, the mileage, and information collected from social media to recognize potential buyers.


Developers who can integrate predictive analytics with CRM (Customer Relationship Management) platforms can help dealers in delivering highly-targeted advertisements to interested buyers.

Data Management of Connected Cars

All the analytic applications that are exclusively made for connected cars, in a way represent examples of data management.


Whether it is about using predictive data for improving the effectiveness of marketing, maintenance, security or other activities that are related to the connected vehicle industry, the data has to be managed in a way that makes it helpful for the intended purpose. However, there is still a need to manage the data from connected vehicles.


The need will become more visible as more onboard applications receive and send data through the net. Considering that each connected vehicle generates around 25 GB/hour and that more than 250 million connected vehicles are expected to be on the road by 2020, clearly, there is a big problem ahead.


Even with low cost – cloud storage, simply storing such huge amounts of data, even in the cloud is not an option. Moreover, there isn’t a data plan at present that can handle the required bandwidth without impacting drivers’ wallets in a big way.


The solution to the data glut is to implement intelligent data management solutions that can effectively manage data both in the car as well as in the cloud. Only by employing the predictive analysis, and probably deep learning, can the big data of connected cars be managed efficiently.


Whether solutions are developed as stand-alone applications or if they are integrated into multiple platforms, the market potential is huge for developers who can excel in this area.


By intelligent analysis of data streams to and from connected vehicles, the data management applications will permit only the required data to be exchanged, and only when it can be used.


Instead of analyzing the stored data, effective solutions will manage data in real time, making effective use of the connected car resources.

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