What does it mean for an IT organization to adapt to change? In today’s high-tech world, even the notion of adaptation itself is changing. How can anyone keep up?
IT organizations are being pushed like never before. Timelines are shrinking, projects are multiplying, and tasks are becoming more complex. Computing resources have shifted from expensive and scarce to highly available and affordable—a mixed blessing, since plentiful resources simply allow business expectations to be that much higher.
At the same time, IT human resources haven’t kept pace. A shortage of skilled professionals has created a staffing gap that doesn’t show signs of easing. With production hypergrowth becoming commonplace and time-to-market an essential element of business success, the old rules no longer apply.
Gartner, a leading market research firm, believes we’ve entered the era of Bimodal IT. The term refers to a split in the way IT organizations must be structured in order to promote both reliability and agility. Mode 1, or traditional IT, is focused on dependability, safety and approval-based governance. Mode 2, by contrast, is both nimble and non-traditional, centered on rapid, continuous development. In Mode 2, DevOps doesn’t have to be perfect—but it must be quick.
There was a time when IT change was accomplished through larger staffs, bigger budgets, more training, and newer products and technologies. Harold Leavitt, a famous psychologist and management theorist, believed that factors like these were sufficient for the task. While his Leavitt Diamond, a blueprint for change, specified Structure, Tasks, Technology, and People as the key factors, he strongly stated that it was the interrelationships that were most critical. If all factors are not moved simultaneously, organizational change is doomed to failure.
Leavitt’s thinking is most definitely relevant to the IT realm. While today’s change factors may be unique to IT, there is no doubt that a comprehensive approach is essential. This is where the notion of adaptation has made its shift.
Today, IT must think in terms of interrelationships. Infrastructure is no longer siloed; hardware is both physical and virtual—what’s more, tasks occupy and shift between both categories fluidly. Big Data, another heterogeneous component, accesses hundreds, even thousands of sources for even the simplest functions.
Software-Defined Infrastructure (SDI) is a hot concept these days for this very reason. SDI not only automates the very idea of IT, but also treats it as an interrelated, highly dynamic resource. Decision-making within SDI is instant and independent of human interaction; it also efficiently manages the variable, limitless, and on-demand computing resources so prevalent today.
To be successful, the modern IT organization must, above all else, be agile. It is workload automation, as the core tactical component, that can achieve this goal in the most immediate and cost-effective terms.
Workload automation solutions, of course, have been around for years. What makes modern workload automation well suited for increasing agility and reducing the cost of change, however, are the vast improvements that have turned these solutions into sophisticated platforms that can manage and coordinate virtually every aspect of IT.
The intent of advanced workload automation technologies is not to reduce human resources, but to make them work more efficiently. Automation improvements have been shown to cut the time to build and deploy processing tasks in half. Reliability is increased as well. In an era when custom scripting, manual updates and outdated change management protocols expose organizations to business risk, the pre-set, templated and tested job steps in intelligent automation systems can not only reduce coding time, but also assure IT of reliable, proven logic.
In Gartner’s Bimodal IT model, Mode 2 requires accurate analysis of jobs and tasks before their release. Workload analytics, a powerful capability in the most advanced automation platforms, enables IT to simulate jobs and workflows under real-world conditions. This ability to test workflows before releases is invaluable. Likewise, workload analytics offers the ability to investigate instances where SLAs are not being met; the resulting insight supports continuous improvement and better outcomes.
Finally, intelligent automation crosses boundaries and makes interrelationships possible. The average organization has anywhere from three to eight separate job schedulers or automation tools. Most of these come from one or more of three categories: applications; infrastructure, virtualization and grid platforms; and operating systems. While it’s not necessary to abandon these schedulers altogether, one comprehensive, cross-platform workload automation solution allows IT to develop unified job strategies, as well as master execution of mission-critical jobs. Just as important, an end-to-end automation solution provides a single point for overseeing and tracking the multiple applications, operating systems and mix of physical and virtual resources involved.
The true value of automation is its ability to see and manage the interrelationships between the hundreds of nodes and processes in IT. As the rise of SDI has indicated, it’s no longer possible for overextended IT staffs to define, create, and execute the functions of IT without automation. The list of tasks, with all their complexities and demands, is simply too long. Intelligent workload automation platforms provide true IT agility, enabling IT to do more with less. As the pace and cost of change continues to rise, this may be their most valuable function of all.