Formula 1 racing is the ultimate human-machine partnership. Every driver is like an engineer, making split-second decisions based on the data from over 200 sensors generating over 13,000 pieces of information. Although it's the drivers piloting the vehicles around the race track, in a sport where performance is the puzzle and data is the key, it's the engineers in the pit who are piecing it all together.
Arash Ghazanfari is CTO at Dell Technologies UK.
Few businesses epitomize the need for 'edge computing' quite as clearly as Formula 1. On an average race weekend, the McLaren team collects around 100 gigabytes of data on each car. They access the data in real-time, in the car, trackside and mission control. A spectator may feel like they're in the heart of the action. But the engineers can see things like a gear change in the data before it's heard on the track. Machine learning and analytics are constantly digging into that data and optimizing the performance of every component in the car to get the best racing results possible. And for those uninterested in fast cars, checkered flags and quick tire changes? The technology devised for the racetrack has far broader implications than one might imagine.
The body is an engine, and data is the fuel
The same digital technology used to power McLaren's Formula 1 team is helping a British pharmaceutical company monitor recovery in stroke victims and people who have severe arthritis. Like the cars, the key is finding predictive, actionable insight (in this case, from the human body) efficiently for smart sensors and data analytics.
Clinicians typically relied on recording stroke sufferer's activity levels each time they visited the clinic. But now patients can wear sensors - not dissimilar to those on F1 cars - that report back in real-time, accurately monitoring recovery with real-world, evidence-based insights. This level of biotelemetry has also ensured increasingly accurate results and increased confidence in clinical studies. The long-term hope is that this technology will bring new pharmaceuticals to market sooner, at a lower cost.
What does this mean for business?
Markets and Markets reports that edge growth is set to grow by 34% by 2025. But a Dell survey of 4,300 business leaders found that although 82% are investing in data management and analytics to some degree today, investment drops sharply for emerging technologies like AI. The same study revealed that 'data overload and inability to extract insights from data' is the third-highest barrier to digital transformation, up from 11th place in 2016. High-performance sport and medicine can often feel like ivory towers when it comes to data collection and analysis. So how does that translate more broadly across the enterprise ecosystem?
The first step is to stop thinking edge computing is the solution to all of your troubles. A recurring barrier to edge adoption is knowing which aspects of your business you should be instrumentalizing to add the most value. Using our previous example of Formula One, it's telling that it's still a human in the cockpit for all the technical wizardry on the car. Even in F1, technology isn't the solution for all situations.
Humility is essential to understand where to apply edge computing. Saying "I don't know" gives room for the incredibly diverse ecosystem of partners to step in and guide your business to a suitable solution. Simply put, edge computing is a new method of solving existing business problems. More often than not, the boardroom does not have the technical understanding required to identify how Edge computing can help their business. Business leaders are the ones who have identified a problem and come up with a solution, and those offering edge solutions can simplify and speed up the process. But they can only do so if they're welcomed in and shown where the problems are.
Cutting through the noise
A frequent pain-point for businesses lies in the fragmented nature of the edge ecosystem. Understandably, businesses' technical debt builds up over the years, leading to a diversity of type, location, and ownership. Retrospective application leads to edge computing partitioned in application-specific, software-stack-specific, data source-specific silos. In short, it's a warren that falls short of the ideal vision of edge computing where the edges of a network are 'cloudified'. Without a standardized way of interfacing, securing, managing and cohesively collecting the data within these ecosystems, edge adoption can be chaotic.
It's still early days
Many still consider machine learning, artificial intelligence and 5G as emerging technologies, so it's no wonder that enterprises are still getting to grips with them. The scale of innovation occurring 'at the edge' across every vertical is extraordinary. But with that speed comes a lack of clarity about how to channel it effectively.
Businesses looking to capitalize on this should have a long-term vision and cohesive strategy to avoid more significant issues. Once again, this is where asking vendors to assist pays dividends. Treat them like management consultants to your business problems and pain points. They should bring consistency and sanity to deploying edge computing resources and effectively manage your life cycle. All vendors will help identify the business need for Edge compute regardless of the platform you buy. What's more, they'll encourage customers into a standardized approach that will allow them to scale effortlessly with whatever comes next.
Conclusion
There is no quick fix or easy answer when it comes to implementing edge solutions. But there are empathetic experts to advise on how to reap the potential rewards it offers. What is certain is that forward-thinking businesses will automate every business process imaginable using machine and deep learning algorithms deployed at the very edge of the network. Business and IT management leaders who fail to recognize edge computing possibilities will soon find themselves lagging behind rivals. For a business to put itself into the driving seat, it should look at those already delivering meaningful results with Edge and ask for assistance from those who can help them navigate the ecosystem.
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