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ZEDEDA 2020 Predictions: Edge Computing Accelerates, Bringing Benefits to Enterprise Technology

VMblog Predictions 2020 

Industry executives and experts share their predictions for 2020.  Read them in this 12th annual VMblog.com series exclusive.

By Jason Shepherd, VP of Ecosystem at ZEDEDA

Edge Computing Accelerates, Bringing Benefits to Enterprise Technology

As we look ahead to what 2020 will hold for the enterprise IT space, it's clear that IoT, AI, and edge computing are going to become even more important components of the technology stack. Businesses are realizing the power and value of being able to gather data through a growing number of IoT devices and then analyze and act on it by processing it right there at the edge, avoiding the latency and bandwidth issues of having to send it all back to the cloud. Gartner recently named the empowered edge as one of the Top 10 Strategic Technology Trends for 2020, while Forrester is predicting that "2020 will be the most interesting year yet for vendors and users in this exciting space."

With the explosive growth predicted for edge computing-soon, more enterprise data will be created and processed outside of data centers than within them-it's inevitable that there will be some growing pains. This is why we'll see an increase in interoperability efforts this year, along with IT and OT practitioners finding new ways to collaborate. As other technologies like 5G and AI continue to mature as well, the interplay between them and edge computing will be important to watch and may yield some unexpected results. In tandem with all of these developments, we'll see the conversation around data trust and ownership pick up steam as both the volume of data being generated and processed at the edge and the applications it's used for proliferate.

Here's more detail on five predictions for edge computing and IoT in 2020.

1.  Interoperability efforts kick into high gear

Over the past several years, the IoT market has consisted of a paralyzing landscape of platforms reinventing foundational capabilities for data ingestion, security, and management, mixed together with applications and domain expertise. 2019 was a turning point, with many providers feeling the pain of trying to own all aspects of a solution and end users realizing that it's important to take control of their data the moment that it is created at the edge. In 2020, we'll see more effort placed on how best-in-class pure-play offers interoperate as part of a broader ecosystem that also mitigates lock-in to any given component. This will be facilitated by industry efforts like LF Edge that promote open source collaboration to facilitate interoperability through open, vendor-neutral APIs in both the infrastructure and application planes.

2.  5G does not replace the need for edge computing

5G is one of the most talked-about topics in networking right now, but the reality is that the hype is outpacing the near-term impact. It will undoubtedly be a transformational technology, but it's not going to replace the need for on-prem edge computing any time soon. The reason is because, even though 5G promises latency decreases and bandwidth improvements, the majority of applications running at the edge will still need some degree of localized compute for rapid, autonomous decision making and to reduce the amount of data being backhauled, because bandwidth always comes with a cost. In general, connectivity will require a holistic approach, with 5G being augmented with private networks (e.g., BLE, LoRa, Private LTE) bridged through localized edge compute nodes.

3.  Computer vision is the killer app for AI at the edge

The buzz around AI will continue to accelerate in 2020, including more announcements of purpose-built acceleration silicon and toolsets to simplify both training and inferencing workloads from edge to cloud. Computer vision will be the killer app for AI at the edge: cameras are one of the best sensors around for deriving rich information from the physical world, but issues with network bandwidth and privacy will require analytics to be performed close to the source so only meaningful events are backhauled and Personally Identifiable Information (PII) is appropriately obfuscated. In addition to today's common use cases in surveillance, autonomous vehicles, and robotics, computer vision at the edge will power new capabilities and features previously unimagined.

4.  Decrease in separation between IT and OT groups for IoT management

The beginnings of what we think of today as IoT can be found several decades in the past, with internally-networked (originally, hardwired) devices that performed tasks like controlling factory automation equipment. In that environment, management of the devices and their workloads fell to the Operations Technology (OT) department. Today, many early IoT proof-of-concept solutions are deployed via "shadow IT," with OT resources bypassing Information Technology (IT) colleagues using cellular-connected cloud platforms in limited scale. However, as more internet-connected devices generate data, concerns that more typically fall under IT-like security, application and device management, and data analytics-have to be addressed. IoT deployments require a unique hybrid between OT and IT responsibilities, and while the divide between OT and IT has been a roadblock to widespread adoption, in 2020 we'll see decreased separation between these groups as it becomes increasingly clear that collaboration is necessary. Edge virtualization will help accelerate this change, because it allows both legacy and cloud-native applications to run on shared infrastructure, further bringing OT and IT into contact with each other.

5.  Real conversation around data trust and ownership

With the proliferation of data driven by investments in IoT and AI, the next big conversation is around data trust and ownership-after all, if you don't have confidence in your data, you can't extract maximum value from it. In 2020, we'll see increasing collaboration on how we achieve trust across heterogeneous systems and stakeholders, which is key to enabling data sharing and monetization, trusted workload consolidation, and scaling the ability to meet compliance requirements such as GDPR. Achieving trust in data at scale is about more than blockchain: it requires a system-level approach, starting with silicon-based root of trust at the edge, which is where all data is created. An example collaboration is the emerging Linux Foundation effort Project Alvarium, which plans to address the trust challenge by combining various trust insertion technologies to deliver data from devices to applications with measurable confidence, all while helping data owners maintain privacy on their terms. On a related topic, we'll start to see regulators exploring the need to mitigate the data gravity being amassed by a few large players in the market. Ultimately, digital transformation is about balancing value realized with privacy and IP protection, and we'll need both technology and regulation to achieve this.

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About the Author

Jason Shepherd 

Jason Shepherd is VP of Ecosystem at edge virtualization company ZEDEDA. Prior to joining ZEDEDA, Jason was CTO for the Dell Technologies Edge and IoT Solutions Division. His proven track record as a thought leader in the market is evidenced through his leadership building up the award-winning Dell IoT Solutions Partner Program and establishing the vendor-neutral, open source EdgeX Foundry project to facilitate greater interoperability at the IoT edge. In addition to serving on the board of the Linux Foundation's Edge umbrella project, Jason is active in the Industrial Internet Consortium (IIC) and other key industry efforts focused on IoT and edge computing. He was recognized as one of the Top 100 Industrial IoT influencers of 2018. He holds 14 granted and 20 pending US patents.

Published Monday, January 20, 2020 7:30 AM by David Marshall
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