How is the AIOps market evolving? The answer in five words is: "Toward increasing levels of diversity."
In the EMA Radar Report "AIOps: A Guide for Investing in Innovation," EMA examined 17 vendors with cross-domain AIOps capabilities, along with doing 31 deployment interviews, and discovered a high degree of variety in design, functionality and purpose. The report has just been posted in our library. But initial work began in February of this year. It was, in essence, a seven-month project.
Listen to EMA's Dennis Drogseth on the AI+ITOPS Podcast
Critical Criteria - What is AIOps?
When EMA first examined this area in 2012, we looked at 22 vendors. We didn't call it "AIOps" as the term didn't exist then, we called it instead "Advanced Performance Analytics."
On the other hand, EMA's core criteria for assessing AIOps (by whatever name) has been relatively consistent throughout. This includes:
■ Assimilation of critical data types across multiple domains, e.g., events, time series data, logs, flow, configuration data, etc.
■ Capabilities for self-learning to deliver predictive and/or prescriptive and/or if/then actionable insights.
■ Use of a wide range of advanced heuristics, such as multivariate analysis, machine learning, streaming data, tiered analytics, cognitive analytics, etc.
■ Support for private cloud and public cloud, as well as hybrid/legacy environments.
■ The ability to address multiple use cases.
■ Automation in play either directly through the platform itself, or through third-party integrations.
■ Awareness at some level of topology and or dependency mapping.
■ Use as a strategic overlay that may assimilate or consolidate multiple monitoring and/or other toolset investments.
EMA's notion of "overlay" was fundamental in 2012. We saw vendors, primarily frameworks and management suites, assimilating data from a growing range of third-party toolsets. This has continued, with fewer than 10 at the low end, and more than 100 at the high end, in the current crop of 17 vendors examined in our 2020 Radar.
This, among other things, differentiates AIOps from big data, as it is more of a tiered system, importing correlated insights from other management tools to accelerate the use of AI/ML across a larger, collective repository or set of repositories.
What's Changed?
Over the last eight years, the biggest change is the diversity of approaches and design seen among the 17 vendors examined in 2020. This diversity was underscored, but not limited to, the three top use-case categories explored in the report. These are:
■ Incident, performance, and availability managementis focused on optimizing the resiliency of critical application and business services — including microservices, VoIP, and rich media — in cloud (public/private) as well as non-cloud environments with a strong focus on triage, diagnostics, roles supported, self-learning capabilities, and associated automation.
■ Change impact and capacity optimization are admittedly two use cases combined into one. But they share requirements for understanding interdependencies across the application/service infrastructure as changes are made, configuration issues arise, volumes increase, and automated actions are required.
■ Business impact and IT-to-business alignment includes user experience, customer experience, and customer management, business process impacts, and other relevant data, with an eye to supporting business initiatives, such as digital transformation through superior IT-to-business alignment.
The Radar also looked at DevOps, SecOps, and IoT support, which could play to each, or all, of the use cases depending on the platform's design and the vendor's focus.
Two Real-World Perspectives on Classic AIOPs Benefits
A single pane of glass: Our collaboration across IT has improved dramatically because we have one place to get information. Different teams customize the dashboard for what they need, and all the information is there in one place. We are moving to replace all the point solutions in the environment with the AIOps toolset. This has the added benefit of saving us money on licenses as we eliminate unneeded, overlapping tools.
Some dramatic statistics: We have already achieved some excellent success in 2019. Some of these successes include:
■ 60% reduction in the time required to bring new customers on board
■ 50% reduction in the number of incidents during non-business hours
■ 21% reduction in the time required for incident resolution
■ 70% improvement in our own OpEx efficiencies
■ 60% reduction in service-level agreement breaches
■ An estimated one million US dollar savings in our annual operational expense
■ Overall improved customer experience and service quality
In Passing …
AIOps can and should be transformative in enabling more effective decision-making, data sharing, and analytics-driven automation. But buyers should consider their own realities, and then begin a search for the AIOps platform that most fits their requirements.
Which vendor can most effectively address your top prioritized long-term goals?
Which vendor is a most natural fit for your current technology environment?
Which vendor is likely to bring you the fastest near-term wins?
The answer could be any one of the seventeen presented in EMA's Radar. It is in the details of the report that you can best find the solution most appropriate for you.
