EMA Research Blog: Navigating IT and Security Horizons

In a perfect world, all business processes would be automated and all work tasks would be accomplished with the click of a button. This idyllic work experience seems to be the realization of Plato’s utopia…or, if you prefer, the world of the Jetsons. Regrettably, however, we clearly do not live in a perfect world. Put simply, while any repeatable process can be automated, not every process is repeatable, so automation is not a practical solution in all cases. This is particularly a problem for enterprises since business productivity is almost entirely dependent on the rapid and accurate performance of business processes.

IT operations managers are cringing all around the world – desperately trying to avoid those inevitable words from their executive management: “You need to support enterprise mobility.” Their concerns are understandable. After all, IT administrators are already overtaxed with supporting desktop, server, application, and infrastructure management requirements. Asking them to layer a whole new management discipline on top of that can be a daunting prospect. IT managers who find themselves in this predicament often recognize it as an opportunity to practice the fine art of procrastination. Particularly skilled procrastinators will employ one or more of the following excuses:

Have you ever tried to create a major slide presentation on a tablet? Or edit a large spreadsheet? Or write a long document? Probably not. While it’s certainly possible to perform more substantial business tasks on a tablet, the small screen real estate and limited system resources (e.g., processing speed, memory, graphic support, etc.) are typically insufficient in current tablet form factors. However, carrying a laptop around with you everywhere you go just so you can access email is not very practical either. The reality is that we live in a multi-device world where the average worker employs 3 – 5 different computing devices in the regular performance of their job function. . . . and I would argue that’s exactly how it should be. Each user employs the device they prefer to optimally perform tasks at any particular time or place.

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!”

If you are an IT manager, have you ever found yourself stuck in the uncomfortable position of having to choose which jobs are given priority access to essential computing resources? Most likely you have as this is not an uncommon problem. Expecting them to invoke the Wisdom of Solomon, enterprises often bestow the power to decide the workload hierarchy on IT operations. But as most IT managers will tell you, this responsibility is typically more of a curse than a blessing.

The term, “workload automation,” often conjures images of an army of robots zipping around an enterprise performing arduous tasks while the IT manager sits comfortably in a lounge chair at the back of the data center sipping a Mai Tai. Well, it’s not quite that Jetsonesque just yet – we’ll have to wait a few years for the robots and Mai Tais – but there is a fundamental truth in how workload automation dramatically reduces management effort and improves IT performance. Put simply, workload automation (sometimes still referred to as “job scheduling”) aggregates the placement and execution of individual business processes (i.e. applications, data bases, web services, or any other jobs that would be hosted by an enterprise IT system) to the optimal location (e.g. a server, a cloud, or a virtual machine) at the appropriate time to maximize performance and minimize costs.

A core precept in cloud computing is that it must deliver the illusion of infinite scalability.  That is, it must fulfill the expectation that any and all workloads will continuously operate at peak performance regardless of the size and number of jobs being added.  In reality, however, private cloud deployments are finite with limited compute, storage, and network resources. It’s kind of like a room full of mirrors – it seems to go on forever, but stretch your hand out and you’ll hit a wall.  The trick to successfully maintaining the illusion of infinite scalability is to allow workloads to extend beyond the scope of a single private cloud into other clouds and, in particular, into public clouds. Adopting this hybrid cloud approach also introduces opportunities for significant cost reductions and increased workload flexibility.

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.

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.

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.