EMA Blog: Business Intelligence and IT Management

(You can read more about the content of the EMA Top 3 - Enterprise Decision Guide in my first Post)

(Click here to jump to the next part and read about how EMA selected its Top 3 products)

Traditional IT infrastructure monitoring focuses on hypervisor hosts, storage and VMs. Application impact is tracked through vSphere resource tagging. This simple and mostly static drilldown approach to full-stack monitoring no longer works for modern microservices based computing.

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.

Every year after VMworld EMA receives countless inquiries regarding the most notable vendors on the show floor. This time around, we have compiled a list consisting of startups taking a new look at traditional IT challenges, as well as larger and more mature companies that have launched industry transforming initiatives. Ultimately, this list of eight extraordinary vendors is focused on one central concept: customer value.

With its roots in mainframe job scheduling, workload automation is often seen as a relic in today's age of cloud, Big Data, mobile management and DevOps. Do we even still need workload automation as a separate discipline or should we simply roll the management of batch jobs into other automation disciplines, such as IT process automation? Is the market for workload automation software stagnating or is there still potential for growth?