How OEMs Can Reduce SDV Fleet Data Costs by 50% or More Without Sacrificing Insights

As vehicles become increasingly complex, so does the amount of data they generate. This influx of data is essential for modern automotive OEMs to monitor vehicle performance, improve product offerings, and enhance the customer experience. However, the explosion of data-particularly with Software-Defined Vehicles (SDVs) and connected ecosystems-comes at a steep price, as the cost of data transmission and storage skyrockets.

OEMs are faced with balancing the need for rich data collection with the financial burden of cloud storage, cellular data costs, and operational overhead. Without optimized strategies, these costs can escalate quickly, especially in a fleet of hundreds of thousands of vehicles transmitting data in real-time. For example, a fleet of 500,000 vehicles could easily generate over 1,260 TB of uncompressed data annually, creating a significant cost burden.

In this blog, we’ll explore some of the key strategies OEMs can adopt to significantly reduce data costs-by as much as 50% or more-without sacrificing data quality or critical insights. We’ll focus on practical, actionable methods that optimize the data collection process, and we’ll show how intelligent technologies can make a significant impact on your bottom line.

The Rising Cost of IoT Data in Complex Machines

IoT devices, including vehicles, generate massive amounts of data from hundreds of sensors and control units, and the cost of collecting, transferring, and storing this data can be overwhelming. Let’s look at a few contributing factors:

1. Cellular Data Costs: Vehicles rely on cellular networks to transmit data back to the cloud, where it can be analyzed. The more data collected, the higher the cellular costs. Without data compression or optimization, these expenses can grow exponentially, especially with fleets transmitting data continuously.
2. Cloud Storage Costs: Storing unoptimized or uncompressed data in the cloud can lead to significant storage costs. If the data is not adequately processed at the edge, it often results in large volumes of unnecessary information being stored in the cloud, further driving up expenses.
3. Real-Time Data Processing: Many OEMs opt to log and process vehicle data in real-time, but this can increase the load on network bandwidth and storage systems. Without efficient data management practices, the cost of real-time processing can become unsustainable.

To put this in perspective, consider a typical OEM that logs 400 signals every minute from a fleet of 500,000 vehicles. Over the course of a year, this can result in over 1,260 TB of uncompressed data-an enormous figure. The good news is that with the right approach, this number can be drastically reduced, leading to substantial savings.

Strategies for Reducing Data Costs Without Compromising Insights

There are several strategies that OEMs can implement to optimize data costs while ensuring that they still collect valuable insights from their connected vehicles. Below are a few of the most effective methods.

1. Data Compression and Efficient Serialization

One of the most straightforward methods for reducing data transmission and storage costs is through data compression. By compressing the data before it is transmitted to the cloud, OEMs can reduce cellular transmission costs by as much as 90%. Additionally, using efficient serialization formats such as Protocol Buffers (Protobuf) instead of formats like JSON or CSV ensures that data is encoded in a compact, binary format, further reducing the size of data sent over the network.

In a practical example, compressing the data from 1,260 TB to 63 TB can result in substantial cost savings without losing critical data points. This is achievable through technologies like Sibros’ Deep Logger, which implements advanced compression algorithms and efficient serialization techniques.

2. Batching and Conditional Logging

Another key strategy is to avoid transmitting data continuously. Instead, OEMs can use batching, which aggregates multiple data points into a single transmission, reducing the number of transmissions and optimizing network usage. Similarly, conditional logging allows data to be logged only when predefined conditions are met. For example, instead of logging engine data continuously, it could be logged only when certain thresholds (such as RPM spikes) are exceeded.

Event-based logging can also be highly effective. For instance, an OEM could choose to log specific vehicle parameters only when an event like a crash occurs, capturing data before, during, and after the event. This approach ensures that the data collected is meaningful and actionable, eliminating unnecessary data transmission.

3. On-Change Logging

OEMs can implement on-change logging to further reduce data transmission. Instead of logging every sensor reading, only log the data when a significant change occurs. For example, if the vehicle's temperature sensor reads 70°F for an extended period, there is no need to continuously log this information. Logging only when the temperature fluctuates by a defined threshold significantly reduces the data volume.

4. File Logging and Edge Processing

Storing data locally and processing it at the edge-on the vehicle itself-can help OEMs minimize the need for real-time data transfers. File logging allows OEMs to log data to local storage on the vehicle and transfer it later, during periods of lower network usage or when it is more cost-effective. Moreover, performing edge processing on this data allows OEMs to extract insights and only transmit essential, actionable information to the cloud.

By using file logging and edge data compression, some OEMs have achieved data reduction of up to 60%, slashing cellular and storage costs significantly.

Leveraging Vehicle Data Efficiently: A Case Study in Savings

Let’s revisit the earlier scenario of a fleet of 500,000 vehicles generating over 1,260 TB of uncompressed data annually. By implementing batch logging, event-based logging, and on-change logging, an OEM can reduce this volume by up to 95%-from 1,260 TB down to 63 TB or even less, as seen with Sibros’ Deep Logger.

The impact on costs is substantial. Not only are cellular data bills slashed, but cloud storage costs are also minimized, all without compromising the breadth or depth of insights OEMs need to make informed decisions. Moreover, the intelligent logging features offered by Sibros enable OEMs to dynamically configure their logging rules based on real-world usage, further optimizing costs throughout the vehicle's lifecycle.

Conclusion: Optimizing Data Costs Without Sacrificing Insights

Reducing data costs is a top priority for any automotive OEM operating a connected fleet of vehicles. By adopting intelligent logging strategies - such as compression, batching, and on-change logging - OEMs can optimize data transmission and storage without compromising the quality of the insights they derive from their vehicles.

Solutions like Sibros’ Deep Logger enable OEMs to make smart, data-driven decisions while simultaneously cutting data transmission and cloud storage costs. In a world where connected vehicle data is essential to staying competitive, optimizing your data management strategy is the key to maintaining profitability without sacrificing innovation.