EMA Research Blog: Navigating IT and Security Horizons

Today, where there are almost as many approaches to digital transformation as there are enterprise software vendors, Docker refocuses its strategy on providing the best unified container management platform for DevOps. Docker’s key value proposition is to enable developers to build an application once and then deploy it to any Kubernetes-driven private or public cloud, where DevOps teams and IT operations can manage it throughout its lifecycle and move it to another location at any point in time. However, Docker also aims to absorb traditional enterprise applications, edge and IoT workloads, big data apps, blockchain, and serverless functions, both on Windows and on Linux.

What should machine and artificial intelligence (ML/AI) do for IT operations, DevOps and container management? The following table represents my quick outline of the key challenges and specific problem ML/AI needs to address. The table is based on the believe that ML/AI needs to look over the shoulder of IT ops, DevOps, and business management teams to learn from their decision making. In other words, every virtualization administrator fulfills infrastructure provisioning or upgrade requests a little bit differently. Please regard the below table as a preliminary outline and basis for discussion. At this point, and probably at no future point either, I won't claim to know the 'ultimate truth.'

“Today, a dev team leveraging Kubernetes containers can get a cloud app up in minutes.” This statement by Arvind Krishna, IBM’s GM for Hybrid Cloud, at the beginning of his InterConnect 2017 keynote should have received a lot more recognition than it did. This one sentence shows the fundamental shift in IBM’s strategy, away from the old Tivoli-centric IT ops company and toward a modern DevOps-focused organization that is looking for differentiation up the stack. Today’s IBM encourages developers to deploy entire application environments without IT administrators even being aware.

In an ideal world, customers would be able to fully take advantage of the benefits of hybrid cloud by rationally matching infrastructure parameters -cost, performance, reliability, availability, security, regulatory compliance, scalability- with the requirements and dependencies of each application.

As we -Evan and I- were ranting last week about how OpenStack and VMware fit together (see #EMACloudRants), we were mainly focusing on the central conundrum that VMware faces within this context: “Should we support an open platform that could commoditize away a substantial part of our profitable infrastructure business or should we ignore the threat and do our own thing”

As promised in my previous post on “Software Defined Storage – Why Customers Should Care”, I want to follow-up with a brief overview of the competitive landscape.

Evan Quinn and I have been collecting popular customer questions for a while and wanted to share our thoughts on these questions in the form of a new format: EMA CLOUD RANTS. Each week we will discuss one of the hot topics in enterprise IT to provide the viewer with rapid analyst insights, without any fluff. Here goes the first one:

Of course, I always encourage practitioners to carefully study the full EMA research report on the “Obstacles and Priorities on the Journey to the Software-Defined Data Center” or at least read the research study summary or at the very least join the EMA SDDC Research webinar on February 18, but I still want to briefly summarize the key findings here.

What does Big Data mean to traditional enterprise IT? Organizations of any size and industry are becoming more and more aware of the incredible importance of capturing, managing and analyzing the data available to them. The more comprehensively companies are able to tap structured and unstructured data sources, the quicker they can refresh this data and the more successfully they make this body of data available to all business units, the better they can develop advantages in the market place. Today’s business units are demanding the rapid implementation of these big data use cases, as well as optimal resiliency, cost efficiency, security and performance.

Much marketing hype and heated discussions should be seen as excellent indicators for the fact that Software Defined Storage (SDS) is one of the hottest topics in today’s data center. Naturally, every vendor defines SDS based on their own product range, sometimes leaving customer out of the equation.