This question is really two questions.
The first would be: What's really going on in terms of a confusion of terms? — as we wrestle with AIOps, IT Operational Analytics, big data, AI bots, machine learning, and more generically stated "AI platforms" (… and the list is far from complete).
The second might be phrased as: What's really going on in terms of real-world advanced IT analytics deployments — where are they succeeding, and where are they not?
This blog will look at both questions as a way of introducing EMA's newest research with data just coming in from North America and Europe (UK, Germany and France). Like this blog, our research will also examine both questions, with the weight on examining real-world deployments. We hope to have at least a few real answers for you by September, with fresh data and timely analysis.
A Term by Any Other Name …
I'm borrowing, admittedly, from Shakespeare, to suggest that buzzwords in tech often get in the way of understanding real value, even as they seek to clarify it. In the case of what EMA prefers to call "advanced IT analytics" the fugal use of AI, machine learning, and big data, among other terms, often confuses what's really afoot. The real value is almost always in the mixture of science and artistry with which the analytics are applied to various use cases, not a purely academic discussion about what heuristics lie underneath the hood.
But EMA believes there is nevertheless a commonality across all true AIA solutions.
Last summer, EMA embarked on research that strongly indicates that there are common benefits, requirements and challenge surrounding an investment in AIA. Some of the more dramatic benefits typically included values in unifying IT across silos, toolset consolidation, dramatic reductions in mean-time-to-repair and mean time between failures, as well as other use cases that typically ranged from performance and availability management, to change management and capacity optimization, to support for DevOps and SecOps, to optimizing migrations to public cloud. As such we view AIA as a potentially transformative arena for both IT and the business it serves.
In our current research, we will be asking some simple questions regarding terminology and attributes to test the waters, especially in the now prevalent area of AIOps. But we'll also be able to track deployments centering on big data, security-related analytics, capacity-specific analytics and end-user or customer experience analytics, to see what patterns emerge and how they actually differ.
How Do You Make it All Real?
What's currently afoot in operationalizing advanced analytics for IT?
This is the main focus for our research, and it will also help to inform on the first question — what people are actually doing when they champion AIOps, or big data, etc.
Some areas of focus include:
■ Use cases: Here we are expanding on capacity, security and end-user experience to include cross-domain application/infrastructure availability and performance, DevOps/agile, cost management (including hybrid and multi-cloud), change management, and IoT.
■ Leadership: Who's leading in investments in advanced IT analytics, and who's leading in overseeing and actually delivering on deployments? What are their objectives, and how are they going about it?
■ Best practices: Are there any consistent best practices that emerge from the usual laundry list when advanced analytics are being deployed and used? If so, what are they? And how effective are they?
■ Integrations: How much are investments in advanced analytics being used to assimilate and optimize other toolsets?
■ Automation: What are the current priorities for integrated automation, where AI and machine learning can help to intelligently and adaptively drive more automated outcomes?
■ AI bots: Along with general automation priorities, we are looking at AI bot strategies to see how they converge (or don't) with AIOps and other analytics investments.
■ Technology and data sources: What data sets are IT organizations most hungry for when it comes to advanced analytics? What heuristics do they feel are most critical now, and in the future? How is service modeling and dependency mapping playing in the advanced IT analytics arena?
■ Roadblocks and benefits: What are the major obstacles remaining in 2018 to effective advanced IT analytics deployments? And what are the more prevalent benefits achieved?
These are admittedly a lot of areas for examination, and once again, the list is not complete. Moreover, we plan to investigate the answers we receive for all these questions from various perspectives, including company size, vertical, geography, roles (what do IT executives think versus more hands-on stakeholders?), success rates and other factors.
Finally, we'll be looking for trends based on the research done in two prior reports: Advanced IT Analytics: A Look at Real-World Adoptions in the Real World March 2016, and The Many Faces of Advanced Operations Analytics September 2014.
What I'm hoping we'll see in September is continued growth toward a more mature, more business-aligned, and more IT-unifying approach to advanced analytics deployments, with a growing number of stakeholders and benefits. I'm also hoping for a more definitive set of AIA profiles, as operations analytics continues to redefine itself away from just "big data," and as the need for more evolved, holistic and dynamic multi-use-case AIA platforms becomes more pronounced.
But it's too soon to tell. The data is still coming in. Nevertheless, I should know soon. In a follow-up blog in the first-half of September I'll be able to present some real news.
Distributed tracing has been growing in popularity as a primary tool for investigating performance issues in microservices systems. Our recent DevOps Pulse survey shows a 38% increase year-over-year in organizations' tracing use. Furthermore, 64% of those respondents who are not yet using tracing indicated plans to adopt it in the next two years ...
Businesses are embracing artificial intelligence (AI) technologies to improve network performance and security, according to a new State of AIOps Study, conducted by ZK Research and Masergy ...
What may have appeared to be a stopgap solution in the spring of 2020 is now clearly our new workplace reality: It's impossible to walk back so many of the developments in workflow we've seen since then. The question is no longer when we'll all get back to the office, but how the companies that are lagging in their technological ability to facilitate remote work can catch up ...
The pandemic accelerated organizations' journey to the cloud to enable agile, on-demand, flexible access to resources, helping them align with a digital business's dynamic needs. We heard from many of our customers at the start of lockdown last year, saying they had to shift to a remote work environment, seemingly overnight, and this effort was heavily cloud-reliant. However, blindly forging ahead can backfire ...
SmartBear recently released the results of its 2021 State of Software Quality | Testing survey. I doubt you'll be surprised to hear that a "lack of time" was reported as the number one challenge to doing more testing, especially as release frequencies continue to increase. However, it was disheartening to see that a lack of time was also the number one response when we asked people to identify the biggest blocker to professional development ...
The role of the CIO is evolving with an increased focus on unlocking customer connections through service innovation, according to the 2021 Global CIO Survey. The study reveals the shift in the role of the CIO with the majority of CIO respondents stating innovation, operational efficiency, and customer experience as their top priorities ...
The perception of IT support has dramatically improved thanks to the successful response of service desks to the pandemic, lockdowns and working from home, according to new research from the Service Desk Institute (SDI), sponsored by Sunrise Software ...
Is your company trying to use artificial intelligence (AI) for business purposes like sales and marketing, finance or customer experience? If not, why not? If so, has it struggled to start AI projects and get them to work effectively? ...
As remote work persists, and organizations take advantage of hire-from-anywhere models — in addition to facing other challenges like extreme weather events — companies across industries are continuing to re-evaluate the effectiveness of their tech stack. Today's increasingly distributed workforce has put a much greater emphasis on network availability across more endpoints as well as increased the bandwidth required for voice and video. For many, this has posed the question of whether to switch to a new network monitoring system ...
When a website or app fails or falters, the standard operating procedure is to assemble a sizable team to quickly "divide and conquer" to find a solution. The details of the problem can usually be found somewhere among millions of log events and metrics, leading to slow and painstaking searches that can take hours and often involve handoffs between experts in different areas of the software. The immediate goal in these situations is not to be comprehensive, but rather to troubleshoot until you find a solution that remedies the symptom, even if the underlying root cause is not addressed ...