EMA Research Blog: Navigating IT and Security Horizons

Greg Ferro recently blogged about how attempts to define software-defined networking (SDN) are a waste of time. He wrote:

[Author’s Note: This post is sponsored by Edgeworx. The content is entirely mine.]

Recently, I’ve been thinking about why we haven’t come all that far in machine learning and artificial intelligence over the previous decade. Today, Keen Browne, Bonsai’s Co-founder and Head of Product, summed it up for me in a concise manner: “The tools really suck and are meant for scientists and mathematicians, not for people who work for the line of business.”

Today, HPE paid $650 to acquire Simplivity. HPE paid this sizeable amount mainly to acquire OmniStack, Simplivity’s data virtualization software platform. OmniStack’s central value proposition is OPEX reduction through the elimination of traditional infrastructure management tasks. In short, Simplivity enables customers to manage their infrastructure at the virtual machine (VM) – level. Please note that there will be a separate EMA Impact Brief covering this topic in much more detail.

The Evolution of the “Endpoint”
Over the past few years, the perception of what an “endpoint” is, and therefore the definition, has changed. The original perception was a user workstation independent of operating system. With the expansion of functionality, data persistence, Internet connectivity, and ultimately the threats against them, that definition no longer applies. Laptops became included, then smartphones, servers, tablets, internal file shares, and dedicated or special function devices such as point of sale terminals and processors.

Ahhh, the annual resolutions list! A time-honored tradition when we all look at the new year as an opportunity for a fresh start.

Review of Statistics Done Wrong by Alex Reinhart

Since the advent of the big data era, organizations have been crying out for data scientists. Initially it was finding the true data scientist. But as these resources were considered scarce, it was a search for people who could hand-code analytical models in a Hadoop environment.  As statistical tools such as R, Alteryx, and RapidMiner augmented Hadoop environments, we started to include traditional tools such as SAS and SPSS. These data scientists, or data scientists in training, were asked to take large amounts of data and divine the nuggets that would create a “cross-sell/up-sell” recommendation engine that would launch the next Netflix or link two disparate data sets that explain how markets interact and find the next groundbreaking investment opportunity.