See How Edge Analytics Is Enhancing Fleet Data Usage

See How Edge Analytics Is Enhancing Fleet Data Usage

Smarter engines, more vehicle sensors and in-cab devices — all increase the amount of data generated by the transportation industry.

 

While these devices and sensors allow many advances in safety technologies, they also generate a massive amount of data that needs to be processed quickly in order to be useful in real time. In fact, the engine data elements collected just from the Trimble customer base generate more than 10 billion data points a day. And that’s just the start: some estimates say that a single self-driving car will produce about four terabytes of data per day, or about the same amount as roughly 2,000 hours of movies.

 

Enabling data processing as close as possible to the data source

In order to keep up with all that data coming in, centralized data centers would need to have the computing power to receive and process hundreds of thousands of data points or more per second. To help manage this vast amount of information and more efficiently consume data, fleets are turning to edge analytics, which enables data processing as close as possible to the data source, instead of capturing and transmitting it back to a data center for analysis.  This method of data analysis reduces the processing load on data centers, the cloud and wireless network infrastructure, since the work can be distributed across devices where the data is being captured.

 

Differences from traditional data analysis

In traditional models of data analysis, data is captured from a source and then transmitted to a data center or to “the cloud” where it is analyzed. Edge analytics brings the “intelligence layer” closer to the source of machine-to-machine data, providing real-time intelligence and reducing the amount of data sent back to the cloud. 

 

Data can often be “noisy” –  meaning that it can contain irrelevant data points that obscure the important information. Edge computing uses machine learning, statistical methods or even basic logic to process data in real-time to transmit only the important data, cutting down on data usage from irrelevant events.

 

edge-analytics

Edge analytics provides data insights on “the edge”, increasing response times and reducing latency by eliminating the need to send information back to a data center.

 

Supplementing data collection, not replacing it

To be clear – edge computing does not replace the need for centralized data processing centers or cloud computing, but rather supplements them by providing more real-time insights to drivers on the edge, prior to sending select data to a traditional processing method.

 

Back-office staff who are not on the road with the driver still need to have access to some of that data, which cannot be transmitted on the edge – so in those instances it needs to go through the cloud or data center.

 

One thing to keep in mind is that data shouldn’t be collected simply for the sake of having data – it should provide some sort of insight that can be used to take action. One way to analyze this data on a broad scale is through machine learning, which looks at a large data set and detects patterns that can be used to make predictions.

 

Interested in learning more about how edge analytics can be applied to your fleet? Download our latest whitepaper to discover how this emerging technology can help you improve safety, increase driver efficiency and lower maintenance costs.

See How Edge Analytics Is Enhancing Fleet Data Usage

Smarter engines, more vehicle sensors and in-cab devices — all increase the amount of data generated by the transportation industry.

 

While these devices and sensors allow many advances in safety technologies, they also generate a massive amount of data that needs to be processed quickly in order to be useful in real time. In fact, the engine data elements collected just from the Trimble customer base generate more than 10 billion data points a day. And that’s just the start: some estimates say that a single self-driving car will produce about four terabytes of data per day, or about the same amount as roughly 2,000 hours of movies.

 

Enabling data processing as close as possible to the data source

In order to keep up with all that data coming in, centralized data centers would need to have the computing power to receive and process hundreds of thousands of data points or more per second. To help manage this vast amount of information and more efficiently consume data, fleets are turning to edge analytics, which enables data processing as close as possible to the data source, instead of capturing and transmitting it back to a data center for analysis.  This method of data analysis reduces the processing load on data centers, the cloud and wireless network infrastructure, since the work can be distributed across devices where the data is being captured.

 

Differences from traditional data analysis

In traditional models of data analysis, data is captured from a source and then transmitted to a data center or to “the cloud” where it is analyzed. Edge analytics brings the “intelligence layer” closer to the source of machine-to-machine data, providing real-time intelligence and reducing the amount of data sent back to the cloud. 

 

Data can often be “noisy” –  meaning that it can contain irrelevant data points that obscure the important information. Edge computing uses machine learning, statistical methods or even basic logic to process data in real-time to transmit only the important data, cutting down on data usage from irrelevant events.

 

edge-analytics

Edge analytics provides data insights on “the edge”, increasing response times and reducing latency by eliminating the need to send information back to a data center.

 

Supplementing data collection, not replacing it

To be clear – edge computing does not replace the need for centralized data processing centers or cloud computing, but rather supplements them by providing more real-time insights to drivers on the edge, prior to sending select data to a traditional processing method.

 

Back-office staff who are not on the road with the driver still need to have access to some of that data, which cannot be transmitted on the edge – so in those instances it needs to go through the cloud or data center.

 

One thing to keep in mind is that data shouldn’t be collected simply for the sake of having data – it should provide some sort of insight that can be used to take action. One way to analyze this data on a broad scale is through machine learning, which looks at a large data set and detects patterns that can be used to make predictions.

 

Interested in learning more about how edge analytics can be applied to your fleet? Download our latest whitepaper to discover how this emerging technology can help you improve safety, increase driver efficiency and lower maintenance costs.

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