We know by now that to extract optimal value from data intelligence, it needs to be highly accessible, captured in real time and at its freshest state and quality if it is to drive immediate operational decisions and responses.
It’s why we have seen the decentralisation of the cloud as the sole vacuum of system intelligence and the migration of data capture and processing to the most remote part of the network edge. Not only does this provide for enhanced agility in terms of data access and handling, but also for improved security. The time data spends travelling across the network bandwidth and potential for corruption is much reduced, while the regular bottle necks that ensue as multiple devices communicate back to a centralised core network are fully consigned to the past.
Unsurprisingly, traction has been buoyant in the data-heavy environs of the IoT space, where more and more imaginative applications have demanded greater efficiency in the processing and transmitting the volumes of data generated. Specifically, the digital edge has flourished in industrial IoT settings, where increased data usage in remote devices becomes a norm, rather than exception.
Here, data must be transmitted across some of the most remote and challenging environments. This means sensors require sufficient processing power to make the kind of mission critical decisions that can’t wait for data to be sent to the cloud. Collecting data fast and flexibly in a gateway solution is a major bonus, not only lowering operational costs, but localising certain kinds of analysis and decision-making in a move that empowers the end user.
Yet the complexity of IoT ecosystems dictate that it is not just about getting data to the brink and job done. First, there’s the question of which approach is best to facilitate it, which can see many caught short by an over

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