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.'

Reflecting on my earlier career in IT management, I have to confess to a level of astonishment at how naïve IT administrative practices were just a decade or two ago. Failure events were common, and most organizations just accepted as immutable fact the reality of systemic firefighting. IT services critical to business operations were all too often held together with little more than a hope and a prayer. Sure, my colleagues and I were acutely aware of the importance of performing “root cause analysis” and implementing proactive management practices, but who had the time for that? The inevitability of business pressures, support limitations, and time constraints most often contributed to sustaining a mantra of “just get it working and move on!”

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.