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As you know usually, cars do not speak any human language. If they could, they would be able to provide a wealth of information that would be invaluable to OEMs, drivers, dealer’s network. Gaining access to vehicle information may not be new and in such a way it is already possible using diagnostic tools in the garage but integrating it with information about a vehicle's operating environment at a given moment in time can be world-shattering. To gain access to this data more and more vehicles are already being fitted with sensors and connectivity solutions natively integrated. Connected cars will provide a steady stream of data on vehicle, engine, driving behaviour and ambient conditions. Extracting meaning from this mass of mixed data generated at incredible velocity and volumes is no easy task. The challenge is now how better capturing those data-in-motion, analysing and redistributing to the relevant recipients, hopefully in real-near real time. But the rewards will be huge: an integrated view of the vehicle providing automakers with real-time insight into how the various vehicle systems are performing under specific driving patterns and environmental conditions. Major benefits include the capability to offer timely repair services and parts promotions improving dealer’s network profitable aftermarket service & products sales, providing drivers assistance by issuing alerts or coaching to maximize fuel economy by finding the optimum speeds and RPM range to shift gears. But connected cars can also open new business opportunities to OEMs providing integrated vehicle data available to 3rd parties as insurance companies or road side assistance operators. To meet this market demand, an Apache Hadoop based platform represents the best of breed solution. It can assure to store Terabytes of data and elaborate data streaming in real time and in an economical way. High speed analytics can run on the platform to perform complex algorithms in seconds and provide real time insights directly on the car dashboard and/or the driver’s smartphone app, tailored to the situation the driver is in at that exact moment or to the different players of the connected value chain.
Any production downtime is a potential huge loss of revenue to companies due to loss of production output, cost of repairs and waste generated in the process; to minimize this risk manufacturers usually apply programs of preventative maintenance, which largely is a calendar-based approach that calls for equipment to be serviced or replaced at predetermined intervals or periods of time. This could include replacing a component on a specified time interval or number of operations. On the opposite a condition-based maintenance program focuses on the condition of equipment and how it is operating rather than on a length of time or predetermined schedule. With advancement in technology, every engineering device is now embedded with sensors and RFID that can actively transmit vital information about machines variables as temperature, oil level, vibrations, working loads, humidity, production rate, waste metrics and breakdowns. Attaining in a data lake the enormous amounts of machine-generated data logs and combining them with fault settings registered when a robot break down and the related maintenance history, will definitely help in identifying patterns that led to robot failure, shaping the condition of in-service equipment in order to predict when maintenance should be necessarily performed.
The big promise of Big Data is a move toward data-driven decision making. By integrating Big Data into a CRM solution, automotive companies can predict customer behaviour, improve customer service and manage investments more accurately. Automating the discussion between prospects and advisors about complex products - as cars are - built on a customer’s desires and resources can enhance the sales process. These applications based on business rules go beyond simple decision trees and algorithms to provide faster and more dependable information and options as part of the sales dialogue; they rely on the analysis of all customer touch points, including social media, email, internet, sales rep and call center reports to segment customers according to actions. Then customer trends can be mined from Big Data and used to predict needs, directing product development and promotional efforts. It is sure that the customer journey from product awareness to purchase can be very long, certainly crosses many organizational structures and several information systems. At every touch point structured and unstructured information are generated and it is easy to believe that the unstructured information will increase more and more to become the most sensitive driver to effectively understand the real behaviour of the customer. What a customer tells to a dealer sales rep can be definitely very different from the opinion exchanged on a Facebook product page with his own peers but for sure any relation can reduce or reinforce the desire to purchase and anyway can spread valuable information to the sales and marketing representatives. Acquiring all those data to properly deal with a single customer or to profiling the most similar customers in order to activate an effective one-to-one marketing relationship can be extremely challenging using traditional RDBMS systems but become definitely much more affordable using Apache Hadoop or NoSQl databases. The new data hub then become a powerful starting point to support CRM applications or to suggest choices on web car configurator engine built on earlier customers selections or personal behaviours plotted on the basis of information obtained on web social media.