Fidest – Agenzia giornalistica/press agency

Quotidiano di informazione – Anno 33 n° 348

Frost & Sullivan: Big Data’s Predictive Functions to Help Identify and Deliver Opportunities in the Automotive Industry

Posted by fidest press agency su mercoledì, 26 marzo 2014

londonLondon, UK Both the European and North American automotive industries are on the cusp of change, with big data and connected services combining to foster the development of diverse mobility integration business models. Frost & Sullivan expects 60 percent of original equipment manufacturers (OEMs) to devise big data strategies and offerings in the next two years, prompted by the proliferation of high-bandwidth, embedded connectivity technologies, such as long term evolution (LTE). OEMs’ strategies could involve strengthening in-house capabilities or outsourcing the job to big data experts. The focus is on standing out from the competition based on brand awareness, digital engagement of customers, and response time. New analysis from Frost & Sullivan, Strategic Analysis of the Impact of Big Data on the European and North American Automotive Industry, finds that by 2020 approximately 35 million vehicles in Europe and North America will make relevant data sets available for OEMs to assimilate and convert into actionable insights. Around post-2015 or 2016, the industry is expected to witness the emergence of integrated service providers with expertise in end-to-end services across the automotive value chain, from consulting to implementation. The concept of connectivity is gaining currency in the European and North American automotive markets, as there is a rising need to create a three-way data sharing network among dealers, customers and the OEMs. This collaboration is crucial for providing a seamless after-sales experience through convenient service scheduling or proactive diagnostics. Furthermore, having access to data regarding the vehicle’s performance and issues before it reaches the service center, will also help dealers better manage their inventory.

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