We’ve been hearing about edge computing as a trend for the better part of a decade now. Edge computing was supposed to be the emerging computing paradigm that processes data close to where it was generated, enabling a range of networks and devices to process data at greater speeds and larger volumes. Heath Thompson, President and General Manager of Quest Software, explores why promises haven’t quite been delivered yet and how Edge can live up to expectations in the near future.
With edge computing, it has always been about the possibility of making more use of “big data” (a term we rarely hear now) for artificial intelligence, new types of applications, and greater efficiencies. However, there has been little traction for edge computing except for specific, well-known use cases. As network speeds increase, and cloud and SaaS infrastructure becomes more robust, widespread, and secure, the advantages of edge computing matter.
Edge computing works by moving the computing resource closer to the data. It makes sense to do this with things like the Internet of Things, and we can all appreciate iPhones and IoT wearables like watches that can process data offline and in real time. In general, the advantages of edge computing relate to response time, cost savings, data aggregation and standardization, privacy, and reduced threats of security breaches. In many regions, data sovereignty is also a major concern; Organizations cannot, by law or policy, allow data to go outside of certain domains. While this is critical, public cloud service providers have solved this by geographically distributing their data centers. It remains important as a driver for some edge computing but is not universal.
Sailing intricacies and edge cost
While the concept of edge computing is sound and the benefits of processing data close to the source are undeniable, the reality is that this approach also brings with it greater complexity and management cost. As with any other part of an enterprise’s infrastructure, edge computing platforms also require management, maintenance, and security. Organizations are already struggling with the size of their networks, IoT devices and users on the network, and edge computing is adding to that burden. Edge computing becomes another “node” on the network, which will require patching, opening up new attack surfaces for threats like ransomware and data breaches.
Advances in technology will continue to hinder the concept of edge computing
Simply put, edge computing doesn’t simplify anyone’s life — it can even make it more difficult. In addition to security and manageability considerations, edge computing can also add complexity to data management and compliance. Data processed by edge computing platforms may contain local storage of personal information (PI) data, which is subject to privacy controls such as the General Data Protection Regulation (GDPR) or the Consumer Privacy Protection Act (CCPA). Also, edge computing may perform significant data transformations, which need to be managed and governed as part of data ratio considerations in the overall governance strategy.
None of these points are intended to be exclusionary factors for edge computing, but rather to invoke the obligations that organizations must assume when they decide to adopt edge computing. There is a dance of opposing forces here: the ever-increasing volume of data from our ability and desire to measure everything, vs. the need to produce actionable results from data, vs. the challenges and complexities of managing edge computing infrastructure, vs. The quality and availability of other computing approaches. We already apply edge computing to some critical use cases, but we also realize that edge computing is not as cheap and flashy – and therefore widespread – as we used to think.
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If not edge computing, then what?
Peripheral computing is a slightly outdated term if truth be told. Modern companies don’t think of “computation” as a way to design for growth in 2023. Instead, organizations envision their future outcomes and put data at the center of their attention. Organizations build data networks, or data fabrics, like neural systems that will drive business processes.
Data networks make data more accessible and available to users, connecting data directly to those who use it: data subjects, data producers, and data consumers. They also give organizations better decision-making power, enabling teams to create data while creating usable data data products to other teams. Data networks solve problems such as data bottlenecks globally across the enterprise; Where edge computing does it locally, a mesh network does it from wherever it makes sense. It can safely and securely connect cloud applications to customer-held sensitive data. It can create virtual data catalogs from sources that cannot be centralized and can give developers the ability to query data from a variety of storage devices without access issues.
The technology follows four key principles that deliver many of the promised but never delivered business benefits of edge computing: domain-oriented, decentralized data ownership; data as a product; self-service data infrastructure as a platform; and unified data governance. Most importantly, the data grid puts data where it belongs: at the center of any strategy that enables the growth and transformation of modern businesses.
Shifting focus from computing to data is essential and also keeping in mind the right volumes for edge computing or cloud computing. Organizations today focus on data, and computation is a means to an end on how to achieve results from data. We have long understood that we will live in a “hybrid” world, where it is not either / or, but both / and. But modern business is now properly focused on its data, and enabling organizations to use their data is where we need our focus in 2023 and beyond.
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