The Volvo Group is an iconic and one of the biggest names in the automobile industry. It had innumerable data sets coming from vibration, pressure and temperature sensors. The data itself was realized not to be as important as the insights gained from it would be. So, it made use of IBM’s SPSS modeler, a predictive modeling software that helped identify patterns which in turn led to enhanced processes through predictive strategies and decisions. This resulted in Volvo reducing its diagnostic time up to 70 percent and repair times of more than 20 percent.
In 2017, big data vendors will be pocketing over US$2.8 billion from hardware, software and professional services revenues in the automotive industry. These investments are further expected to grow, eventually accounting for over US$4 billion by the end of 2020. With the increase in usage of real-time and past data, big data is now quickly becoming a major attraction point for a lot many sectors and the automotive industry is no exception. With the increase in market shifts and cost pressure, the automotive industry is currently facing a myriad set of dynamic challenges.
Data analytics is primarily used for design and manufacturing in the automotive sector. A lot of savings in terms of cost can be achieved with an efficient inbuilt analytics system. The analytics system in the workflow software predicts flaws or designing errors when the vehicle is still in the blueprint stage. Along with it analytics can also help track design changes and keep a tab on work quality and product safety.
The automobile industry has always been a hotbed of innovation and with big data coming into the picture the disruption has increased manifold. Semi-autonomous cars have already made their way into the market and fully autonomous cars are next in line. Big data has had the biggest impact on the development of autonomous vehicles. With estimates from IHS Automotive stating that the average car will be producing almost 30 terabytes data per day, it’s up to the automakers to decide how best they are going to exploit this data island to achieve cost benefits and consolidate their market position.
Another major area where big data is being employed in this industry is customer retention. Automakers understand that trust and loyalty of their customers are of utmost importance and how personalized experiences always result in great numbers for the makers. Hence aftermarket services making use of data, like predictive maintenance and personalized services, are being integrated with big data analytics on which the automobile industry has now become heavily reliant. Predictive analytics also accounts for real-time feedback from customers making use of historical data and helps improve upon services and offerings.
Fleet and fuel management pose a great challenge to this industry. In an article in ‘Automotive Fleet’ industry expert Mike Antich writes that “With the use of fleet analytics and accessibility to greater amounts of data, fleets will become ultra-efficient and gain the capability to make quantifiable increases in driver productivity.” With the use of analytics, damaged parts of a vehicle can be identified before it causes interruptions in daily use. It leads to an increase in driver safety by identifying patterns that frequently precede an accident and causes the fleet system to alert the driver and manage the situation.
With large volumes of data being produced than ever before, the automotive industry has become mostly data driven now. Big data and analytics will allow you to see the unseen and make smart decisions and derive value from the vast amounts of data and to maintain market position and profits in the air of forever shrinking margins.