Automotive engineers have access to more data than ever – but right now the majority of companies are not harnessing it effectively. Without the right tools to analyze this data, its potential remains untapped
Without proper data analysis tools, there is no clear way for automotive companies to turn the data they collect into actionable insights that can help shape the next generation of vehicles, which means innovation in the industry is at real risk of stalling.
Automotive companies have been trying their best with the tools available to them. They know that their data could be incredibly powerful. That’s why they’ve continued to collect it wherever they can – from traditional sources such as DAQ hardware, but also in the form of digital data collected by IoT sensors, ADAS and a whole range of communication buses.
However, collecting data is much easier than actually using it. Let alone unlocking its full value.
Right now, automotive engineers are still left working with data analysis tools that are inefficient, that can’t handle their data, and that can’t support their preferred analysis processes.
It’s time for that to change.
The three barriers to automotive data analysis
There are three key flaws in most data analysis tools that stop automotive companies using their data effectively.
The first barrier is data overload. There’s simply too much data to grapple with – most of it unstructured and almost impossible for most tools to analyze in an efficient way. Most platforms require that data is carefully sorted and processed before it can be analyzed, which makes it almost impossible to extract useful real-time insights.
The second barrier is quality and integration issues. The most valuable data today is digital bus data, which tends to be inconsistent, full of gaps and confusing values that most analysis tools struggle to work with. Not to mention that most platforms can’t easily integrate data that comes from different sources – such as sensors, in-car electronics and manufacturing machinery – making it difficult to see the full picture of a vehicle’s performance.
The final barrier is, arguably, the most frustrating one for engineers: their tools aren’t built for the automotive industry. Most platforms can’t extract many of the insights that automotive engineers need most, or follow their most common processes. As a result, engineers are forced to ‘make do’, creating their own Python or custom scripts to adapt existing tools, or even building their own tools from scratch so that they can create workflows that suit their needs.
Building and maintaining these customizations takes up a staggering amount of engineering time. It’s become such a normal part of the testing and development process that most automotive companies don’t even notice how much time their people are wasting on coding. That’s time that could be used to drive innovation and win the company a new competitive advantage.
Not to mention that, with turnover in the automotive industry being so high, these custom scripts carry an added risk: when the person who built or customized the tool moves on to another opportunity, they take all their knowledge and documentation with them, leaving their engineering team with a tool that’s impossible to maintain or use effectively.
These barriers might seem difficult to overcome – but automotive companies don’t have to accept this status quo.
Changing the face of the industry
The first companies that manage to upgrade their data analysis will have a huge competitive advantage.
Their innovation cycles will accelerate and being able to understand real-world usage of their vehicles will enable them to constantly enhance their designs. They’ll minimize guesswork, which means they’ll be able to create new products that make a real difference to drivers’ safety or quality of life.
Their products will become more reliable. They’ll be able to deliver enhanced predictive maintenance, reducing downtime and creating better experiences for customers.
Analyzing driver behavior and system performance will allow them to build more advanced ADAS, adapt to different driving styles, and allow each driver to create their own customised experience.
For example, a major automotive company was struggling to process and use the immense amount of data that its fleet of cars was collecting – just like all of its competitors. In the past, the company’s engineers only needed to process the relatively small amount of data gathered in their labs during testing and development.
But its quest for innovation had left it drowning in data. By the time it spoke to its solution provider, its connected vehicles were sending a deluge of data – every day, every hour, or even every minute. The data center was filling up, but the analysis systems weren’t built to process all of the data being collected.
There was plenty that the company wanted to know. How could it reduce failures in its vehicles and make them more durable? How were customers really using the products? But it had no idea how to extract that information from its data.
The company was searching carefully for the right solution. And it needed something fast; it was already wasting time trying to process and store all this data, so it didn’t need to waste more time coding its own analysis platform from scratch.
Luckily, HBK was able to supply a complete off-the-shelf tool – and, in the process, provide easy access to insights that are now revolutionizing the company’s R&D process.
It’s time automotive companies put their data to work
The future belongs to automotive companies that can make their data work harder for them – using it to shape strategies and fuel innovation, instead of leaving it languishing in a data lake. By harnessing the full potential of their data, they’ll not only develop new products faster, but also deliver safer, more reliable vehicles that redefine the driving experience. However, the industry’s future depends on robust, flexible, efficient data analysis tools. In other words, automotive data has changed – and it’s time that automotive data analysis tools caught up.
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