“Nobody really argues whether cloud computing is going to take off or impact our lives, because it’s already everywhere. Edge computing is the logical evolution of distributed computing vis-a-vis the cloud.”

“Cloud is often thought of as centralized, but this imposes challenges including latency and location of data. What we need is a way to counterbalance that centralization.” 

“Edge computing offers the ability to provide innovative applications and business models.” 

“Edge computing is proving itself to be so revolutionary in areas like the industrial sector, logistics, oil and gas, manufacturing and retail, that it’s beginning to blossom over into more horizontal industries as well.”

“The edge is the physical location where things and people connect with the networked digital world.”

“Today we have people doing proofs of concepts in the edge world that are not based on a standard architecture and so we have all these incompatible silos. 

“There’s a need to create an edge strategy, and a set of frameworks and templates to use, to ensure that we’re all creating something that can be reused and optimized across different use cases.”

Continuous endpoint experiences: Technology is now the primary way of being in touch with stakeholders, so it needs to work flawlessly. Unified endpoint management (UEM), zero trust network access (ZTNA) and other similar technologies enable this. 

The move to 5G is inevitable but don’t hurry: The hype around 5G is causing a lot of market confusion. As with LTE, 5G roadmaps will contain multiple releases that are improved over time. Enterprises should plan for true innovation to appear in later iterations.

Visual intelligence: The demand for high quality video - and thus visual intelligence - is going up to address a myriad of use cases from facial recognition to detect car accident patterns. 

Health, wellness, compliance and safety: A focus on enterprise safety is leading to mobile technology adoption such as contactless identification and selection, movement analysis, and lone worker protection via sensor monitoring. 

Workplace analytics: These technologies give IT a way to look into the performance of a deployed technology and its associated user experience. In a perfect world, they allow organizations to predict and find problems within applications before the end user does, and improve them accordingly.  

Frontline worker technologies and wearables: The majority of net-new mobile investments are geared toward front line workers, for example augmented, mixed and virtual reality. Remote expert guidance, immersive workflow, safety and health, and worker training are the most common use cases for this category of devices.

Value Stream Platforms: The end-to-end sequence of activities performed to deliver value to the customer, from building and deploying an application to maintaining it in production. There are 2 types: 1) value stream management platforms and 2) value stream delivery platforms.

AI augmented devops: The use of AI-enabled technologies to augment the entire software development life cycle. One example of this is email platforms auto generated or auto complete a user’s draft sentences. 

Continuous infrastructure automation: This trend applies agile and DevOps practices such as version control, continuous testing and CI/CD to infrastructure code rather than application code, bringing the same benefits to apps teams and I&O teams alike. 

Gitops: A technique for operating a cloud native application using only declarative constructs stored in Git, which is increasingly popular with organizations that are using Kubernetes and containers.  

Site Reliability Engineering (SRE): A software engineering mindset that uses automation to reduce toil, or low value tasks, as the guiding principle.

Chaos engineering: The use of experimental and potentially destructive failure testing in pre-production to uncover vulnerabilities and weaknesses (e.g., single points of failure, latency) within a complex production system.