Automotive OEMs and suppliers are in a hurry to move from an engineering-centric approach to a data-centric approach – the right data management approach will be critical going forward.
Automotive OEMs are in for a rude awakening. Data-driven technological advances are sweeping away decades of engineering traditions based on mass production and vehicle sales. Pioneers like Tesla set the pace. They are primarily a software and data company, while their role as a manufacturer becomes almost a support function.
In this new world, telemetry and vehicle features take precedence over vehicle looks and quality. Under pressure to stay relevant, traditional automakers are rushing to transform their business models, Pure Storage reports. Old, isolated methods of vehicle development are being jettisoned. In their place are integrated systems and streamlined processes that equip vehicles with data-driven capabilities and limitless personalization opportunities.
Pure Storage explains what this development and a future-proof orientation mean for IT and data management systems:
The transition from technology to data
The process of unifying entire ecosystems for better collaboration, seamless connectivity, and greater cross-business information sharing is moving automakers away from an engineering-centric model and toward a more data-driven approach. To achieve this, they must quickly transform their business. Many of them are currently embarking on a digital transformation process that includes data analytics for better visibility of innovation, development and resource needs.
Tesla is at the forefront of adopting new industry methods for this data-driven approach. The company started this process long before anyone else and has a greater amount of quality data compared to others. Another reason for Tesla’s leadership in this space is that the company sees itself fundamentally as a software and data company, rather than a manufacturer. Unlike traditional manufacturers, Tesla is focused on data-driven personalization and functionality.
IT is changing from a cost center to a profit center
In the automotive industry, IT has long been viewed as a stepchild, one of the necessary costs of running a business along with insurance. In contrast, automotive operations (core manufacturing functions) have historically been held responsible for product development and ultimately for sales. Today, however, OEMs see that customers appreciate the difference technology can make to their experiences and appreciate its potential to positively impact the bottom line.
When we talk about data in connection with vehicles, we are actually talking about telemetry. Motorists, especially young ones, are no doubt familiar with insurance companies mandating a telemetry box to measure and analyze driving behavior – a process known as telematics. It seems that telemetry will soon be a ubiquitous aspect of driving. Rapid ingestion, analysis, and extraction of telemetry data is key for embedded functions such as in-vehicle infotainment (IVI), advanced driver assistance systems (ADAS), and gesture and temperature control. The question is: how do you collect and store this telemetry data?
Effects on data storage in motor vehicles
The data generated by vehicles is managed in two ways. There are in-vehicle control units that are responsible for engine management systems (EMS), on-board accessories, etc. There are also cloud-based management systems for over-the-air updates and for collecting and analyzing telemetry data for the OEM. This is where things start to get quite complex. Big data systems contain what is characterized by the three Vs: volume, i.e. volume, variety, i.e. diversity and velocity, i.e. speed. Complexity refers to the variety of data, the analysis of the data that is present, and the ways in which it can be put to good use. Velocity refers to faster development or faster time to market. It’s also about the speed at which you can extract insights from that data.
Above all, the quantities are significant. About 70 million new cars are bought worldwide every year. While estimates vary widely, the amount of data each connected car generates can easily reach tens of terabytes per day. Even with one terabyte, that’s 100 million terabytes per day. When multiple cloud providers are involved, it can get very expensive.
Data storage technology for the automotive industry must therefore be inherently flexible, working with all major hyperscalers while supporting every leading global cloud provider. Some storage technologies have a high-performance, massively-parallel architecture that can accelerate development for faster time-to-market. OEMs must therefore fundamentally rethink their storage architecture so that everyone in the company can make optimal use of the data.
In summary, vehicle manufacturers are rapidly shifting their business from an engineering-centric approach to a data-centric approach to meet growing customer demand for a more personalized driving experience. As OEMs adapt to this new data-driven ecosystem, everything depends on the right storage platform for efficient data handling.