Introduction to Predictive Analytics in Connected Car Industry
Imagine the flawless automobile vehicle ever, like autonomous car. It exercises itself on the street even when you are having a nap, halts itself at your favorite hotel for your favorite cuisine, and awakens you just in time for a rapid touch-up before you step out of the car. If this is the matter from a science fiction film, it is coming to real life one-step at a time. Connected car industry has been developing smarter cars since last few years and providing more chances to automate the tasks of car driving. Every now and then, they are developing and testing features that will bring the connected cars from ADAS(Advanced Driver Assistance Systems) higher levels to complete autonomous system.
Before proceeding further for predictive analytics applications, understand applications of data analytics in automotive domain. For that, please read this article. This will help you to understand different use cases in this broad automotive industry including smart cars/connected cars.
Due to rapid increase in technology revolution, especially after invention and applications of predictive data analytics, the automobile industry has seen quick development over the last decade, among that, big thanks to big data analytics.
Whether it is improving car safety with cognitive IOT, groundbreaking variations in the conveyance and locomotive facilities and functions, lessening restoration prices and growing up-time with predictive analysis, or driver-less (complete autonomous)cars, the know-hows of the digital economy are making a gigantic impression in the growth of the automobile field. That benevolence an ocean of innovative occasions for specialists in the domain to up-skill and exploit on this rising trend.
Using Localized information for predictive analytics
Connected cars are stipulating localized statistics encircling the whole thing from gas stations to retail outlets as well. Ultimately, automotive car users are using localized information to create personalized suggestions based on consumer predilections.
Predictive and prescriptive analytics make available applicable facts that are later renovated into next-best action suggestions for custom-made consumer practice. Such a method is serving the business to upsurge consumer commitment and recognition. Furthermore, it is authoritative for the automotive domain to confirm excellence and consistency by ordering and punctually solving automotive related problems. For example, automakers are leveraging warranty analytics to discover developing subjects, hence dropping warranty charges and defending product even-handedness.
Location-based information is flowing in because of connectivity within cars. Automotive car users are leveraging statistics to disclose associations and interconnection. These users can use refined mathematical computations and statistics to precisely forecast the future situations. Additionally, they could combine domain knowledge to measure the what-if situations.
For example, automotive car users are spreading consumer involvement beyond the car by providing location-based propositions. Automotive car users could capitalize on car information by indorsing content through infotainment systems, devoted payments, and smart messaging based on travel outlines.
Automotive car players will collaborate with insurance companies over the long-term to generate hazard silhouettes of consumers. Those consumer silhouettes would be based on traffic routes used, miles-driven, speed vs. speed limit among other features. This will enable insurers to severely lessen charges and bring up enhanced facilities
Read more applications and data management in connected car industry in next blogpost.