Self-driving cars, electric cars, digital safety sensors and an increasingly digital driving experience are hallmarks of the new automotive market. Electric cars constituted 4% of all auto sales in 2020. That number had more than tripled to 14% by 2022, and EV sales were up 25% in the first quarter of 2023, according to the International Energy Agency.
Technology futurist and strategist Bernard Marr recently wrote about automotive innovations in Forbes. He made note of novel anti-aquaplaning systems, and AI-powered simulations for Formula One teams that model billions of potential race parameters to determine what variables are most likely to lead to favorable outcomes.
The variety and velocity of new data creation from sensor data, images and streaming content such as video is creating new needs and opportunities for unstructured data management in the auto industry. Electric and autonomous vehicles continuously collect and analyze sensor data to inform critical actions, such as alerting the driver when it’s time to recharge or refuel or to auto brake or steer to avoid a collision.
This data has a longer shelf life than its immediate use during a drive; car makers need sensor data to resolve technical issues and improve their cars’ performance and safety. This requires powerful AI and analytics tools, edge and cloud storage, and the right data strategies. Cloud-based data lakes have made it easier and more affordable to query data and run machine learning models continuously on the data, compared with more traditional BI tools.
Common data management challenges
Autonomous cars are projected to generate as much as 40 TB of data an hour from sensors. These massive new data sets are gold mines for analyzing and optimizing vehicle performance and safety and delivering new features to make driving more fun and less stressful. Digital innovations and heightened customer expectations are creating urgency for car makers to manage their data differently than in the past. They need the ability to rapidly analyze and filter the right data at the edge to avoid crashing their data centers and cloud services with too much irrelevant data.
How unstructured data management helps
Unstructured data management solutions can help users tag, search and send the right data sets to AI tools and data lakes in the cloud, while avoiding waste and optimizing costs. Unstructured data management solutions can save money on storage through intelligent data tiering to object storage in the cloud. From there, researchers and engineers have cloud-native access to their data, which is necessary for cloud-based AI.
Using an unstructured data management system, a car manufacturer could create a workflow like this:
- Find crash test data related to the abrupt stopping of a specific vehicle model;
- Use an AI tool to identify and tag data with “Reason = Abrupt Stop”;
- Move only the related data to a cloud data lake to reduce time and cost associated with moving and analyzing unrelated data;
- Move the unrelated data to an archival storage tier for cost savings (or delete it) once the analysis is complete.
In the Komprise 2023 State of Unstructured Data Management report, IT and storage directors responded that preparing for AI is the leading data storage priority in 2023, followed by cloud cost optimization. The right unstructured data management solution and strategy can help accomplish both goals by by automating the curation of data to feed AI tools with data governance capabilities and by ensuring that data is always in the right place at the right time in its lifecycle, which can save organizations significantly by leveraging lower-cost storage when data is no longer active.
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