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Entuity 2018 Predictions: Cloud Disruption Gathering Speed, Big Data Getting Bigger and AI Creeping into Analytics

VMblog Predictions 2018

Industry executives and experts share their predictions for 2018.  Read them in this 10th annual series exclusive.

Contributed by Lee Walker, CTO at Entuity

Cloud Disruption Gathering Speed, Big Data Getting Bigger and AI Creeping into Analytics

Cloud and Hybrid

Cloud continues to be one of the major disruptors of 2017, and this disruption will gather speed in 2018. Gartner predicted that the infrastructure-as-a-service (IaaS) market will grow by 38.6% and the software-as-a-service (SaaS) industry to grow by 20.1% by the end of 2017. Enterprises are gradually divesting from their data centers and moving applications workloads to the public cloud. According to the CSA survey report, in 2016, 60.9% of applications workloads were still in enterprise data centers. By the end of 2017, however, fewer than half (46.2%) will remain there. This is, in part, due to new applications primarily being deployed in the cloud, and because enterprises plan to migrate 20.7% of their existing applications to the public cloud.

But data centers aren't disappearing just yet. For most large enterprises, data centers will form an integral part of their thinking for the next few years, and the focus will be on developing effective strategies for managing the hybrid combination of on-premises, off-premises, cloud and non-cloud architectures.

Cloud migration presents the IT professional with new problems - such as how to troubleshoot issues between layer 2 (on-premises) and layer 3 (public cloud). Traditionally, if an issue was reported with an on-premise application/service, IT could look within their own network for the cause. Now, there are dependencies beyond the firewall, such as ISPs or servers hosting the cloud application/service. NMS vendors will need to arm their customers with tools to help them quickly eliminate blind spots and remediate issues on distributed networks. In 2018, we can expect to see vendors offer innovative solutions to help IT pros solve these problems.

Analytics on the rise

Big data is getting bigger every day. As business grows, so does the data it generates. Many correlate "network analytics" with machine learning capabilities, but there's so much more to it than that. Data modelling is an art - extracting all of those useful 1s and 0s in a network and transforming that data into actionable insights with the aim of improving business efficiency, reducing costs and keeping the network up, running and secure. Migration to the cloud is complicating matters further. A whole new world of virtualization is demanding our attention, and now more than ever, we need access to quick insights to help us be as efficient as possible in resolving performance issues.

In 2018, we'll see more of an emphasis on how information is presented. User experience (UX) will be key in translating complex data into visually stunning user interfaces (UI) that quickly tell a story of the networks health through carefully architected dashboards. For many years, IT have been prone to delivering lacklustre user interfaces that bear resemblance to artefacts from an earlier analogue age, but users expect and demand better user experiences, and vendors will start delivering.

AI will continue to grow

AI is starting to creep into analytics, at both network and cloud layers. Machine learning in network monitoring is still very much in its infancy because there is no hard and fast rulebook for managing a huge, sprawling, distributed network. Before we apply machine learning, we first have to understand where it can be of most benefit. We've seen various methods employed so far, including pattern discovery and prediction that learns what is normal on a network (dynamic thresholding) and helps to prevent issues before they impact the business. Attempts have been made to reduce alert fatigue by learning to filter important alerts/events from extraneous ones. And we've seen collective intelligence, whereby customer data is pooled and insights shared for more proactive remediation, for example; a customer reports an issue with a patch, that insight is fed proactively to other customers before they apply the same problematic patch.

In 2018, we can expect to see a clarification of the use cases for AI in networks and we'll see growth in the field of AIOps (Artificial Intelligence for IT Operations). One of the real benefits of machine learning is the opportunity to automate repetitive tasks, enabling humans to focus on more important, strategic work and helping IT move from a reactive model to a proactive one. It's important to point out that AI will not replace humans, rather it will enhance human analysis. 2018 promises to be an exciting year.


About the Author 

Lee Walker 

As CTO at Entuity, Lee works closely with the CEO to help guide the company's product strategy, and oversee the people and processes that ultimately turn that strategy into reality, ensuring that Entuity continues to deliver cutting-edge solutions to its customers.

Lee has more than 20 years of experience in the networking and communications industry. Prior to joining Entuity, Lee worked for 3Com Corp. to design and develop their Network Management software, during which time he added several patents to the company's portfolio. He also gained critical first-hand experience supporting the corporate network and overall IT infrastructure at Nuclear Electric, and has a Bsc in IT and Computer Science.

Published Thursday, January 04, 2018 8:07 AM by David Marshall
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