Advanced IT Analytics, AIOps, and Big Data: Seven Key Takeaways

by Dennis Drogseth, Vice President of Research, IT Megatrends, Analytics and CMDB Systems

The data is in from my newest research on advanced IT analytics, AIOps, and big data! Three hundred respondents participated in the survey, consisting of approximately 2/3 North American and 1/3 European (England, Germany, and France) respondents. There was a wide range of roles represented in the survey, with a solid IT executive presence, along with data scientists and technical, security and IT service management (ITSM) stakeholders. Further insights into demographics will be revealed during my webinar on October 10, titled AIOps and IT Analytics at the Crossroads: What’s Real Today and What’s Needed for Tomorrow.”

The goal of the research was to look at how advanced IT analytics (AIA), or what EMA has termed “AIOps,” is being deployed. We asked what contributes to its success in terms of technology, organizational ownership, functional priorities, and process and best practices. We also mapped how AIOps was being deployed with other analytic technologies, such as big data, as well as more niche areas such as security-specific analytics, end-user-experience analytics, change management analytics, and capacity analytics.

So what did we find?

Without giving away the heart and soul of the webinar, which will give you data to draw your own conclusions, here are seven of my own personal takeaways, some of which frankly surprised me.

#1 - AIOps was, overall, the winning strategy. While AIOps was not the most pervasive technology associated with advanced IT analytics in our research, it was the most effective and pervasively advanced. Indeed, AIOps showed the highest success rates, the greatest likelihood of supporting DevOps, IoT, and AI bots, and led in use case capabilities as well.

#2 - Advanced IT analytics are eclectic in use case and becoming more so. Overall support for DevOps, IoT, AI bots, and multiple use cases, including end-user experience, security, capacity analytics, cost-related optimization, show increasing diversity in need and value. The implications of this finding are significant. It means AIOps and AIA are evolving more broadly as platform investments rather than niche solutions. This means that the data consumed and applied can be leveraged in multiple ways, bringing added benefits to the investment, while also helping to more effectively unify various roles, organizations, and stakeholders across IT.

#3 - AI bots and automation are not a separate world from AIOps and AIA. The strong and perhaps surprising correlation between AI bots in use, AI bots as a sign of overall analytics success, and AI bot integrations into broader analytic directions all indicate that the AIOps market and the AI bots market should not be viewed in isolation. This also helps to reinforce the critical handshake between automation and AI, which was also reinforced by the research findings indicating that, on average, respondents targeted more than five automation integrations.

#4 - Capturing interdependencies and the CMDB/CMS both stood out in importance. As an average, respondents sought to capture nearly five interdependencies across the application/infrastructure, while 54% of respondents viewed the CMDB as ‘extremely important’ to their analytics strategy, a surprising valuation that correlated strongly with success. The implications of this may seem to contradict notions that the CMDB is ‘old hat’ technology. Rather, what’s indicated in these findings is that CMDB/CMS and application/infrastructure dependency mapping are technology areas that are both being reinvigorated by AIA/AIOps investments, while also providing valuable contexts for leveraging and optimizing analytic insights for a variety of use cases.

#5 - Security is on the rise. Priorities in cloud, vendor selection, heuristics, and best practices all indicate that security is a leading and largely integrated concern in advanced IT analytics, and AIOps in particular. This was not altogether a surprise given similar findings in EMA’s 2016 research. Moreover, it is both a welcome and a much-needed advance, as the trend toward a true SecOps (security + operations) integration across IT organizations is becoming ever more critical given rising vulnerabilities, as well as the growing demand for OpEx efficiencies across IT.

#6 - Top-down for everything is the winning strategy. It is also the most pervasive. The executive suite (VP and above) was more likely to be successful, and more likely to drive, AIA strategies, deployment, and purchasing decisions. The reasons for this make sense once AIA/AIOps is understood as a unifying technology that can help bring IT silos closer together with shared data and common insights. But to realize this advantage fully, process issues, organizational barriers, and even habits of mind can be transformed through improved dialog and leadership.

#7 - Advanced analytics are showing strong evolutionary values compared to prior years. EMA research from early 2016 and 2014 indicate strong growth in heuristics, data sources, integrations, stakeholder roles, and overall versatility in terms of function and purpose. The implications are that AIA/AIOps solutions are evolving dramatically in terms of functionality, use-case, and breadth. Here, the progress wasn’t surprising, but the degree of progress in terms of hard numbers actually was.

…and there’s a lot more.

There was no spoiler’s alert at the beginning of this article because the real proof of the pudding is the hard data and the many other insights that I plan on sharing during the “AIOps and IT Analytics at the Crossroads: What’s Real Today and What’s Needed for Tomorrow” webinar. But, hopefully, you’ll find some of the discoveries mentioned here intriguing, and as always, I welcome your thoughts and comments (