In­tel­li­gent Edge

Network tech­no­lo­gies are now part and parcel of almost all aspects of our private and pro­fes­sion­al lives. They offer many ad­vant­ages and make countless working processes simpler. At the same time, the data traffic arising from them leads to hefty tasks, ties up resources, and requires time. One solution for this is in­tel­li­gent edge which reduces the distance between devices and the cloud, letting you gather and analyse data close to its point of col­lec­tion. The result is no­tice­ably quicker and more secure data pro­cessing that is a boon for companies and working processes.

With typical data pro­cessing, the data leaves its point of origin (i.e. the system or device on which it is collected) and is stored on a cloud or in an external data centre, where it is inspected, analysed and then played back. Obviously, this happens super quickly, but the con­stantly growing number of devices that are part of the Internet of Things also means that it is con­stantly getting more complex and that processes are getting longer. This is where in­tel­li­gent edge comes in. In­tel­li­gent edge is located as close as possible to the point of col­lec­tion of the data or at the location of the user. There, powerful edge computers capture the data, read it, play it back and only then do they forward it in a stream­lined form on to the cloud.

In­tel­li­gent edge has two major ad­vant­ages. The first is that it makes processes much quicker. As the data is evaluated, sorted, and played back closer to its point of col­lec­tion, processes can also be optimised much more quickly. The devices that send the data get more direct input and can react to it im­me­di­ately. On-site eval­u­ation also means that po­ten­tially sensitive data is kept even more secure. As the data remains near its point of origin until it has been fully analysed, there is a smaller risk of it being in­ter­cep­ted or damaged en route to an external data centre or the cloud.

To use in­tel­li­gent edge, powerful pro­cessors are needed, and they also need to have enough memory space. It is only with good enough per­form­ance that results can be achieved that actually bring value to users. The tasks that should be automated by AI down the line must first be trained. Once the AI has finished in­ter­n­al­ising the various processes, precise ranges of tasks can be defined and carried out on a routine basis. Careful mon­it­or­ing is therefore required right from the start. Data is trans­ferred over the Internet or via an internal, de­cent­ral­ised network.

In­tel­li­gent edge is first and foremost suitable for companies that handle large amounts of data on a daily basis and therefore need as quick an analysis as possible. In time-sensitive processes, such as automated pro­duc­tion, a few fractions of a second can be decisive when de­term­in­ing an optimal procedure.

If the data stays near its point of col­lec­tion, ad­just­ments can be made much more quickly. This also means that network problems are no longer an issue. In­tel­li­gent edge is also an at­tract­ive solution for companies that work with personal (customer) data. Such data can be protected in this way and any ap­plic­able reg­u­la­tions can be more easily complied with.

Given the ever-in­creas­ing quant­it­ies of data and the in­creas­ingly demanding re­quire­ments for its pro­cessing, the need for in­tel­li­gent solutions will itself also increase. One ap­plic­a­tion of this is the in­tel­li­gent cloud which takes on board the ideas of in­tel­li­gent edge and pushes them further. To do this, an AI creates a computer en­vir­on­ment directly next to your own data pro­cessing en­vir­on­ment. Latency is sig­ni­fic­antly reduced and working processes are auto­mat­ic­ally adjusted and optimised on site.

The in­tel­li­gent cloud is much more than just a simple storage location – it carries a range of functions with it to analyse and sort data in real time and to send it back to its starting point.