By 2025, according to Gartner, more than 50% of business-critical data will be created, processed and analyzed outside the cloud/datacenter. This is a paradigm shift for data management and CIOs, but also for business teams who should greatly benefit from this change.
This area outside the cloud/datacenters, closest to the machines is called the "Edge."
Edge computing is the concept of processing and analyzing data near where it is generated, rather than sending it to a centralized data processing center. The goal is to reduce latency and improve responsiveness by processing data at the "edge" of the network, close to the sensors and devices that generate that data. This also ensures the protection of the data.
Edge computing is often used in applications that require fast response, such as industrial control systems, autonomous vehicles, IoT (Internet of Things) sensor networks and mobile applications. It can also be used to reduce the amount of data that needs to be transferred to a centralized data processing center, which can help reduce bandwidth costs and protect user privacy/industrial production secrets by processing data more confidentially.
These technologies are increasingly used in the industry because they correspond in all respects to the very specific needs of the factory, in addition to offering additional capabilities compared to traditional technologies (machine learning training for example, outside the cloud and at no cost).
Zero latency: by processing data close to the sensors and devices that generate them, it is possible to reduce latency and improve the responsiveness of industrial control systems. This is necessary when dealing with machines that need to be calibrated to the microsecond.
Reliability: by processing data locally, it is possible to reduce dependence on a centralized data processing center that could be vulnerable to breakdowns or computer attacks.
Cost savings: by processing data locally, the industry reduces the amount of data that needs to be transferred to a centralized data processing center, thus reducing storage and bandwidth costs. By processing data locally, the cost of training machine learning models is divided by 10.
Data protection: by processing data locally, you protect your process data which are production secrets.
Scalability: Edge computing enables data processing systems to be deployed in a more flexible and scalable manner, adding new devices and sensors as needed.
Start by identifying your data processing needs and the applications that require a quick response. This will help you determine the devices and sensors needed and select the most appropriate data processing technologies.
Use pre-integrated, proven solutions to simplify deployment and reduce development costs.
Use a no-code solution that your business users can use on the factory floor, thus reducing IT development bottlenecks and consulting costs.
Leverage flexibility and scalability by using Edge computing solutions that allow for the addition of new devices and sensors as needed.
Check that the solution has a 360 monitoring interface for your different deployed instances.
Make sure the solution allows you to send data to your cloud so you can unlock big data use cases for your data science teams at headquarters.
Don't forget to implement security measures to protect data and devices from computer threats.
And most importantly, make sure you choose a solution that will deliver Day 1 value!
Do you have questions (potential use cases, deployments, advice, etc.) or would you like to know more about our solution?
Don't hesitate to contact us to discuss!
