Around 10% of enterprise-generated data is created and processed outside of the traditional centralized data center or the cloud. By 2025, Gartner predicts this figure will reach 75%1.
Where is all this “outside processing” occurring? Well, it’s happening at the edge. In simple terms, edge computing is a shift left process by which decision-making or actions are executed at the same point where the data originated.
Traditionally, decision-making has been looked as something that requires complex processing and data churning in order to execute meaningful action. Yet as more and more systems started to generate more data, the decentralization of decisioning, or in other terms empowering the edge device itself to take action that is based on the event it was capturing, became the new norm. Examples include: wearable devices that have the capability to dial emergency services based on elevated patterns; or nuclear plant sensors that gather data such as temperature or asset resilience and make decisions based on whether the information captured exceeds or drops beyond a safe threshold. In these examples, edge computing supports taking instant action to avoid what would otherwise be catastrophic consequences.
Edge computing has been mostly made possible through IoT technologies. However, edge computing itself is what has made IoT a reliable lever of digital transformation. Both possibility and reliability are two faces of the same coin, but only a reliable implementation delivers ROI and furthers the progress of the business’s digital transformation journey. By pushing the boundaries of compute and cognitive capabilities at the edge, we’re able to further build on the possibilities digital transformation and, as consequence, or ability to rely on them to make decisions for us. One of those emerging capabilities is machine learning at the edge. By deploying ML models to the edge, we’re actually creating the ability for the edge to advance its own effectiveness through self-learning. If designed and developed with a research-driven mindset, these solutions can enable us to overcome industry challenges such as interoperability, security, compliance, and more. From there, we’re able to create real business impact by delivering operational efficiencies, business innovation, and new revenue models.
While the world of IoT and cloud have transformed how enterprises use these technologies for optimization and scalability scenarios, it has also pushed the bar on innovation as it pertains to addressing issues in the new normal that we’re all experiencing. These use cases are empowered further by combining them with even and better technology such as 5G. A great example is remote manning of trucks and cranes in an industrial site far away from home. This example is a hallmark of business innovation in the new normal, when human skills are still essential, but human safety is of paramount importance.
It is an understatement to say never even imagined partnerships are sprouting up; leading platform providers and CSPs are now partnering with operators. A case in point example – Microsoft has partnered with AT&T to bring forth even more integrated edge computing capabilities. These partnership incubated innovations are resulting in newer, scalable and decentralized decisioning that will completely change the approach to computing. It now to choice of enterprises to respond or react to these innovations as part of their respective transformation journeys.
Edge servers are the new buzzwords for enterprises. Edge provides phenomenal opportunities to drive decisioning, efficiencies, and optimizations, thereby driving business value. At the same time, an argument can be made that these edge serves which could be considered “micro data centers” that bring in additional management overhead. There is a perspective that operationalizing such setups in zones and areas poses significant challenges given location, connectivity reach, and other factors. Thus, enterprise looking to transform into this space will also need to evaluate their readiness before embarking on such large initiatives.
Let’s contextualize edge with some real-world examples:
In healthcare, edge technologies present providers with real-time patient information without reliance on centralized data centers. Advanced hospitals receive vital patient data from the edge in the ambulance to asses and alerts the relevant medical staff, which helps prepare care based on the severity of the patient.
In manufacturing, edge technologies provide real-time data on equipment status and condition, product quality, and the efficiencies of operational processes. With a proactive edge landscape, manufacturing businesses are able to perform preventative maintenance (and thereby avoid equipment downtime) and can ensure that their workforce is producing at pace and quality.
In nuclear power plants, where any little detail missed could result in a devastating impact, decision- making and acting on parameters has to occur in real-time to proactively keep the plant running while also mitigating disaster scenarios.
Agile and responsiveness are primary elements of digital transformation and, correspondingly, edge computing. Edge computing offers several advantages over existing environments and plays an important role for enterprises that are early adopters of emerging technologies as part of their push to remain competitive leaders in their respective industry. There is already large adoption of edge technologies across various industries such as energy, manufacturing, smart cities, automobile, and building management. As adoption progresses, we can expect many use cases to become industry standards; what will be most exciting is to see the innovative prowess of these businesses and how they navigate the unknown to prove the true potential of edge computing.
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