Google the word “connectivity” and you’ll be presented with thousands of web pages on the issues and challenges associated with a more connected world. However, it’s important to recognise how to embrace the benefits too. As connected devices proliferate, so too do their capabilities – real-time decision-making is becoming more important than ever before if we’re to make smarter, more informed decisions. This is why our cloud computing architectures need to be able to handle rapid data transactions – but, currently, they are too centralised.
As a result, we’re seeing the current movement of computational capacity, away from the cloud and onto the edge.
Enter: Edge computing
Edge computing is undeniably one of the biggest developments in the blossoming Internet of Things (IoT) sector to date. In fact, McKinsey’s top trends list reports that in many industrial sectors, particularly those with mobile and remote assets like in the oil and gas industry, analysing data at the edge may be more cost-effective than moving data from the edge. This allows analytical decisions to be made in real time.
Edge computing therefore presents the opportunity for businesses to make large-scale decisions, by analysing data procured at the edge – at the edge. Real-time insights like these improve both speed and accuracy by removing the need to stream all data from the edge back into the enterprise or cloud. As such, organisations need to push their analytical computer power further out to the edge, if they are to reduce their costs.
What’s more, edge computing is set to grow. Predictions from McKinsey suggest that it will represent a potential value of $175B – $215B in hardware. Before we know it, edge will be all around us. Tech companies are disrupting the sector and those opting to move to the edge will be able to make smarter decisions, while feeling more confident in the decisions they are making at the time they make them.
Putting it into practice
You may be wondering how edge computing can help a business to make smarter decisions when it comes to IoT projects.
Enterprise companies now have large volumes of data to deal with. Most of this is never analysed and no value is gained from it. This is because it can be costly and complicated when there are so many transactions happening in multiple systems all the time. This is why businesses are working to join the data across systems, normalise them, analyse the data and make decisions from it. The competitive advantage this offers is the reason that data management and analytics is now such a massive industry.
Edge analytics and IIoT
Companies adopting IoT initiatives have millions of devices producing data in real time. Even prior to IoT these organisations were struggling to cope with the volume of data they had – today that struggle is epic. This is where edge computing comes into play – enabling enterprises to analyse the data that adds value.
Let’s take the manufacturing industry as an example. The manufacturing sector is leading the way in embracing smarter technologies such as sensors, to become more connected – and smarter. Currently, lighthouse factories are paving the way for manufacturers looking to become smarter. Edge computing is essential for smart manufacturing. This is because, in an IoT world, it is the sensors and the connected devices that live at the edge. With this in mind, the analytics on the data is best placed to happen on location, rather than being moved to a centralised storage location.
Edge analytics enable the right data to be put into the right peoples’ hands. As opposed to putting large volumes of valuable data into more peoples’ hands than necessary, it’s better to let AI do the grunt work at the edge – enabling humans to focus on the value-add areas.
Ultimately, by combining edge analytics with the Industrial Internet of Things (IIoT), manufacturers can unleash their vision, optimise machine reliability and production, improve quality and drive the creation of innovative business models.
If one thing is for sure, it is that most industries have the opportunity to embrace edge and can therefore derive value from this shift. Without it, they’ll suffer from slower decision making, volumes of data too large to manage and an increase in costs. So – what are we waiting for?