What’s Driving Greater Adoption of IT Operations Analytics?
With modern business becoming more complex and facing constant changes, unpredictable events, and dynamic demand by the end users – all happening at unprecedented speed – IT Operations & Management is looking to adopt the right tools to optimize operations to handle the complexity and pace of change.
Need to Do More With Less
In 2009, IT budgets fell sharply. According to Gartner, they shrank 8.1 percent in 2009, and another 1.1 percent the year after. Though IT budgets started growing again in 2011, they are only at the level they were in 2005.
At the same time, IT operations teams are running with fewer people and resources, while not only managing an increasing number of systems, but also dealing with the new complexity that comes with hybrid environments and the rapid pace of changes nurtured by agile processes. Increasing productivity while lowering costs seems like a difficult proposition, especially since increased demands are placed on operations staff to manage a variety of rapidly evolving applications across the environment.
Managing Enormous Amounts of Data
Everything from system successes to system failures, and all points in between, are logged and saved as IT operations data. IT services, applications, and technology infrastructure generate data every second of every day. All of that raw, unstructured or polystructured data is needed to manage operations successfully. The problem is that doing more with less requires a level of efficiency that can only come from complete visibility and intelligent control based on the detailed information coming out of IT systems.
Frequent Changes Occur in IT Operations
With the operations staff responsible for the health of the entire business it is in their DNA to resist anything that might introduce unpredictable changes within the IT infrastructure or applications, so much so that IT Ops are rewarded for consistency and for preventing the unexpected or unauthorized from happening.
However, solving business problems requires creativity and flexibility to meet the frequent changes dictated by business requirements. New agile approaches eschew the standard method of releasing software in infrequent, highly tested, comprehensive increments in favor of a near-constant development cycle that produces frequent, relatively minor changes to applications in production. With hundreds or thousands of dependencies, even if the agile iterations are properly tested throughout development, unforeseen problems can arise in production that can seriously affect the stability.
Since every IT service is based on many parameters from different layers, platforms, and infrastructure, a small change in one of the parameters amongst millions of others can create significant impact. When this happens, finding the root cause can take hours and days particularly given the pace and diversity of changes. In many cases unplanned changes lie at the root of many failures. This can create business and IT crises that should be resolved quickly to avoid productivity and business losses.
Traditional Approaches Failed
Problems can be difficult to manage or even identify because so many businesses rely only on monitoring software, which is not sufficient alone to address challenges described above. In fact, problems are often not detected until they have grown out of control. If these issues are not resolved quickly, the result is downtime.
All of the technology infrastructure running an enterprise or organization generates massive streams of data in such an array of unpredictable formats that it can be difficult to leverage using traditional methods or handle in a timely manner. IT operations management based on a collection of limited function and non-integrated tools lacks the agility, automation, and intelligence required to maintain stability in today’s dynamic data centers. Collecting data, filtering it to make it more manageable, and presenting it in a dashboard is nice, but not prescriptive.
One of the holy grails still unresolved in IT management is intelligent IT automation. There are pieces of activities that are automated, targeted at the repetitive, well-known, mundane activities. This can free up people and resources to perform more innovative activities, and offer a more agile, speedy response from IT.
However, while automation is an important tool in the kit, it’s just one of the tools. The effort to automate complex environments is proportional to the complexity. Essentially, automation is just another generation of scripting of those activities that are running as part of operations designed to spawn and manage slave automation gofers.
The Rise of IT Operations Analytics
Given that changes to the operational model are almost guaranteed, a change in perspective is needed where IT operations takes a proactive approach to service management. Applying big data concepts to the reams of data collected by IT operations tools allows IT management software vendors to efficiently address a wide range of operational decisions. Because of the complexity of environments and processes and the dynamics of the environment, organizations need to have automation that is analytics driven.
With all of this data, IT Operations Analytics (ITOA) tools stand as powerful solutions for IT, helping to sift through all of the big data to generate valuable insights and business solutions. IT Operations Analytics can provide the necessary insight buried in piles of complex data, and can help IT operations teams to proactively determine risks, impacts, or the potential for outages that may come out of various events that take place in the environment.
Allowing a new way for operations to proactively manage IT system performance, availability, and security in complex and dynamic environments with less resources and greater speed, ITOA contributes both to the top and bottom line of any organization, cutting operations costs and increasing business value through both greater user experience and reliability of business transactions.