OK, the data is in! Three hundred respondents and analysis that took me multiple weeks and resulted in a summary deck of nearly 200 slides. And that's just the summary deck.
But I promise a much more focused exploration of the "IT analytic universe," one that's all digestible within 45 minutes (including Q&A), with the upcoming EMA webinar on October 10.
The goal of the research was to look at how advanced IT analytics (AIA) — or EMA's term for primarily what today is best known as "AIOps" — is being deployed, as mentioned in a prior APMdigest blog.
We asked what contributes to its success in terms of technology, process and best practices, organizational ownership, and functional priorities.
We also wanted to map how AIOps, or IT operations analytics, was being deployed in the context 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.
We asked these questions to a respondent base that was about 2/3 North America, 1/3 Europe (England, Germany and France), across a wide range of roles. We got a solid IT executive presence, along with technical stakeholders such as data scientists, security-related stakeholders, and operational and IT service management (ITSM) stakeholders.
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 is winning strategy
AIOps was the overall the winning strategy. While AIOps was not the most pervasive technology associated with advanced IT analytics in our research (big data led as the most prevalent before quotas), 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. AIA are eclectic in use case
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 are significant. AIOps and AIA more broadly are evolving 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
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.
Read Advanced IT Analytics, AIOps and Big Data - 7 Key Takeaways - Part 2, covering 4 more key takeaways from EMA's research.