Sasha Gilenson, Founder and CEO, Evolven, is a thought leader in the new emerging area of IT Operations Analytics, tackling 15 years of chronic change and configuration challenges.
The pace of change in today’s IT world is truly astonishing. IT is expected to support a wider range of technologies and platforms, as well as accelerated release schedules and still solve time-sensitive business challenges, despite cost pressures and complexity that frustrates many IT ops teams. This landscape is pushing even more complex, multi-layered IT systems that need to keep up with the dynamic atmosphere that produces volumes of data (far more than was ever imaginable), yet is critical to daily performance and operations.
Dealing with this data volume, variety, velocity and complexity is really a “big data” problem for IT operations, forcing many traditional approaches in IT to change, ushering in IT Operations Analytics solutions to take on this challenge.
IT Operations Analytics is better equipped to manage this kind of big data challenge. So, it is no wonder that IT Operations Analytics is gaining both industry interest and expert attention – moving IT management technology into a new S-curve growth cycle.
Investments in Traditional IT Management Tools Deliver Only Marginal Returns
For IT Operations, change is a fact of life, taking place at every level of the application and infrastructure stack, with an impact on nearly every part of the business. Today’s IT environments are complex. Generating huge amounts of data, management of this is made even more difficult as the rate of change grows in frequency, and the departmental silo structure creates further obstacles to gaining a clear perspective of issues affecting service management.
Traditional IT management tools have been applied to collect enormous amounts of raw data, but now lack the analytics capabilities to make sense of today’s “big data” operations. As the recent Forrester report, titled “Turn Big Data Inward With IT Analytics,” noted, “The tools present us with the raw data, and lots of it, but sufficient insight into the actual meaning buried in all that data is still remarkably scarce.”
The frustration with traditional IT management tools has been further demonstrated in a report published recently by Gartner Research which concluded that “the Big Four surrendered share and stunted market growth, while a new generation of ITOM (IT Operations Management) vendors grew significantly faster than the market.” (Source: Market Share Analysis: IT Operations Management Software, Worldwide, 2012: by Laurie F. Wurster et al).
According to this Gartner report, two of the leading providers in the sector, IBM and BMC, did show modest year-on-year growth in 2012 of 0.8 percent and 0.9 percent respectively, but CA and HP declined 0.6 percent and 4.3 percent. In contrast, a group of the fastest-growing companies mustered growth rates ranging from 84.4 percent to 45.5 percent.
Moving to a New Technology S-curve
The life cycle of innovation has often been described using the technology S-curve model, mapping the progress of technology innovation against new performance challenges. During the last 15-20 years in IT operations, enterprises have made huge investments in IT management tools.
With complexity growing, the IT landscape changed as the amount of IT operations data required to keep track of grew, impacting the ability of IT Operations to maintain performance and availability. The long implementation periods involved in running these technologies and the little actionable information yielded has left IT ops vulnerable when failure hits. In order to address this problem, a new approach – IT Operations Analytics – has emerged, tackling the complexity and dynamics in a new and more innovative way. This is driving a transition to a new S-curve, shifted to the right and upward of the original one, delivering better data center performance.
The emergence of IT Operations Analytics solutions is creating an environment in which many traditional elements of IT are also shifting. IT Operations Analytics can quickly discover the root causes of IT system performance problems, assess the relative impact when multiple causes are involved, analyze service cost and anticipate performance impacting events among other under the responsibility of IT operations management.
Gartner Research VP Will Cappelli, explained in a recent report, “IT Operations Analytics Technology Requires Planning and Training,” that the “operational data explosion has sparked a sudden and significant increase in demand for ITOA [IT Operations Analytics] systems.”
IT Operations Analytics Is Redefining IT Operations
As a discipline, IT Operations Analytics combines complex-event processing, statistical pattern discovery, behavior learning engines, unstructured text file search, topology mapping and analysis, and multidimensional database analysis. IT Operations Analytics solutions, in the spirit of business intelligence (BI), are penetrating IT operations.
Analysts such as Gartner and other industry experts are enthusiastic about the technology. Vendors are also approaching company decision-makers to consider how to take advantage of their vast stores of data, and apply advanced analytics in the context of IT operations.
IT Operations Analytics can provide visibility, extracting insight buried in piles of complex data, helping IT operations teams to proactively determine risks, impacts, or potential for outages that may come out of granular configuration changes.
This expectation is underscored by the outlook described in Gartner’s recent Hype Cycle in IT Operations report, stating that “IT Operations Analytics will provide CIOs and senior IT operations managers radical benefits toward running their businesses more efficiently…“
Improving IT Operations Performance
Most IT operations teams spend a disproportionate amount of time chasing various root causes for performance issues, primarily because poor technology obscures clues to resolution. IT operations leaders need new ways to deliver more value to their business. Tools for effective decision-making can improve the infrastructure and operations (I&O) team’s ability to allocate resources to the right types of activities.
Industry analyst firm, Ovum, believes that “gaining actionable information from the wealth of data generated through change and configuration management activities can help IT work proactively, reduce disruptions to normal service, and help more effectively manage change.” Enterprises that have already implemented IT operations analytics solutions report on significant cuts in their MTTR, a reduction in number of incidents and downtime, and enjoy smooth, error-free releases.
With all this data, IT Operations Analytics tools stand as powerful solutions for IT, helping to sift through all of the big data to find patterns. That’s what IT Operations Analytics is all about. Otherwise, IT management will continue to struggle, continuing into a downward spiral. So it’s time to apply some of that same business intelligence thinking to the work in IT, and bring the analysis of big data inwards for IT Operations.
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