Edge Analytics enables data-driven companies to go straight to analyzing their data after it has been collected by IoT devices. It helps eliminate data processing bottlenecks.
Learn more about Edge Analytics, its benefits, and concrete use cases to better understand this new data trend.
Speed up data processing and analysis, and reduce the number of steps between collecting and using your data assets: That’s the promise of Edge Analytics. This method of data processing is all about proximity to the data source. It avoids all the steps involved in sending data to a data processing center.
How Edge Analytics works
Edge Analytics responds to a very different logic than traditional data analysis, with which data is generally transferred to a remote processing center, such as a server or cloud, and the analysis is performed. In the case of Edge Analytics, connected devices or sensors located at the edge of the network collect data in real-time from various sources such as industrial machines, vehicles, surveillance equipment, IoT sensors, etc.
The raw data collected is pre-processed locally – it is then filtered and prepared for immediate analysis. The purpose of this local pre-processing is to clean, sample, normalize and compress the data, in order to reduce the quantity of data to be transferred and guarantee its quality, prior to analysis. Once this preliminary phase has been completed, data analysis is also carried out on-site, at the edge of the network, using algorithms and models previously deployed on local devices or servers.
With Edge Analytics, you can fine-tune your data analysis strategy by transferring only essential data or exceptional events to a remote processing center. The objective? Reduce network bandwidth requirements and save storage resources!
What are the benefits of Edge Analytics?
If the proximity between the source of the data and the means of processing and analyzing it appears to be the main advantage of Edge Analytics, you’ll be able to reap five main benefits:
Benefit #1: Accelerate real-time decision-making
Less distance between the place where data is collected and the place where it is processed and analyzed means the prospect of time savings on two levels. As Edge Analytics processes data at the network edge, where the data is generated, this enables real-time analysis, eliminating the latency associated with sending data to a remote location. Another advantage of this real-time dimension is that it enables autonomous data analysis.
Benefit N°2: Reduce latency between data collection and analysis
Edge Analytics is a promise of real-time exploitation of your data assets because data processing is done locally. In the case of applications requiring rapid responses, such as the Internet of Things (IoT) or industrial control systems (production or predictive maintenance, for example), proximity data processing drastically reduces latency and optimizes processing times.
Benefit N°3: Limit network bandwidth requirements
Traditional data analysis almost always relies on the transfer of large quantities of data to a remote data processing center. The result: intensive use of network bandwidth. This is particularly true when your business generates large volumes of data at high speed. Edge Analytics has the advantage of reducing the amount of data that needs to be transferred, as part of the analysis is carried out locally. Only essential information or relevant analysis results are transmitted, reducing the load on the network.
Benefit #4: Optimize data security and confidentiality
As you know, not all data have the same level of criticality. Some sensitive data cannot be transferred outside the local network for security or confidentiality reasons. Edge Analytics enables this data to be processed locally, which can enhance security and confidentiality by avoiding transfers of sensitive data to external locations.
Benefit N°5: Embark on the road to scalability
Because Edge Analytics enables part of the data analysis to be carried out locally, it enables a significant reduction in network load. In so doing, Edge Analytics facilitates scalability by avoiding bandwidth bottlenecks and paves the way for the multiplication of IoT devices without the risk of network overload.
Data analysis can be distributed across several processing nodes, facilitating horizontal scalability. Adding new devices or servers at the edge of the network increases overall processing capacity and enables you to cope with growing demand without having to reconfigure the centralized processing architecture.
What are the main use cases for Edge Analytics?
While the Edge Analytics phenomenon is relatively recent, it’s already being used massively in many business sectors.
Edge is already widely used in manufacturing and industrial automation. In particular, it helps to monitor production tools in real-time, in order to detect breakdowns, optimize production, plan maintenance, or even improve the overall efficiency of equipment and processes.
In the healthcare and telemedicine sector, Edge Analytics is used in connected medical devices to monitor patients’ vital signs, detect anomalies, and alert healthcare professionals in real-time.
Smart cities and mobility
Edge Analytics is also well suited to the urban mobility and smart cities sector. In the development of autonomous urban transport, for example, real-time analytics can detect obstacles, interpret the road environment, and make autonomous driving decisions.
Security & surveillance
The surveillance and security sector has also seized on Edge Analytics, enabling real-time analysis of video streams to detect movement or facial recognition.