Insights from AIOps Evolution Weekly | Episode 3
It’s always fun to discuss and debate the “top 10” list of anything. In this week’s episode of AIOps Evolution Weekly, Sean and Bill tackle the conversation around the top AIOps platforms on the market currently, why they are leading AIOps platforms, and who could be the runner-up. Also in this episode, we got a “crash course” on some AIOps fundamentals. What exactly is AIOps? Beyond a technology that uses artificial intelligence to improve processes and incident responses, Sean and Bill maintain that it’s more than a product or a platform.
Let’s take a deeper dive into the article topics and key takeaways from Bill and Sean.
TL; DR?
Catch the full episode on YouTube!
Top 10 AIOps Platforms
AIOps as a service and workflow efficiency model is still a relatively new concept to IT Operations teams. When deployed with strategic thought and purpose, it promises magnified data delivery and management; frictionless technology workflows, as well as reduced MTTR through anomaly detection and mitigation. In his article “Top 10 AIOps Platforms”, Peter Wayner lists 10 of the leading AIOps tools simplifying the job of keeping enterprise IT infrastructure on the ball.
They include the following:
- AppDynamics
- BigPanda
- Datadog
- Dynatrace
- Github CoPilot – works in pre-prod during dev
- IBM Watson Cloudpak for AIOps
- LogicMonitor
- Moogsoft
- New Relic One
- Splunk
These are some of the big players in the industry right now that are leading the charge for AIOps’ potential to equip IT departments like never before.
With the expanding complexities of the IT environment, IT teams cannot keep pace with the sheer volume of data, anomalies, and demands from end users for flawless technical experiences.
Here’s what Sean and Bill had to say about Wayner’s list.
Leading AIOps Platforms for now…Newcomers in the peripheral
While this top AIOps platforms list is comprehensive and these are the “big dog” players in the market currently, it is not a set list of forever AIOps leaders. As AIOps continues gaining traction, there are newcomers entering the market. Furthermore, the market is projected to grow two and a half times within the next 5 years. That leaves a lot of space for new platforms and service providers to change the current market leaderboard.
AIOps Decisions are about business needs, not the product
When it comes to AIOps deployments and implementations, decisions about platform or service should depend on business needs and goals. In that respect, AIOps is a strategy that requires thought and a roadmap to successful returns on investment year-over-year.
Overall selection criteria should include the following:
- Data – strength in data collection
- Context – ability to model and create context for your stack
- Algorithms – approaches to anomaly detection
- Handling learning/feedback from operator
- Workflow and visualization support
- Performance/reliability
- Cloud/SaaS v. On-Premises
Equipped with these criteria, IT leaders can make decisions about their overall direction for AIOps, take steps to create an AIOps strategy, and analyze the capabilities of different AIOps platforms and services against overall goals.
Sean and Bill predict that the demand for AIOps will only grow and eventually become a necessity for IT environments that want to keep pace with the expansion of digital experiences and services across enterprises.
As for the selection of “leading AIOps platforms”, none of them are bad selections in general. But at the end of the day, AIOps investments are about your current investments, technology stack direction, and organizational goals.
AI for IT Operations 101
What is AI for IT Operations (AIOps)? This question still stumps people in the industry, so Bill and Sean dove into the topic and parsed it down into AIOps 101. Beyond a term coined by Gartner in 2016, AIOps is a practice that promises optimized data-driven services and incidents.
According to Ben Linders’ keynote speech at DevOpsCon Berlin 2021, AIOps utilizes deep learning, data streaming processing, and domain knowledge to analyze infrastructure data from internal and external sources to automate operations and detect unusual system behaviors.
Linders boils down AIOps into 3 steps: anomaly detection, root cause analysis, and decision-making remediation.
Data is the keyword here. As enterprises digitize processes and functions, such as internal workflows or customer experiences, data is a driving force behind successful delivery of those services. A business that relies heavily on data streams needs to have acute visibility into that environment. More data means more anomalies, margins for error, and in turn more work for the IT department.
In fact, these environments have become so complex, that it is humanly impossible to manage the billions (even trillions) of data points. Sifting through almost infinite data for minute issues is tedious and time-consuming. Something that takes days for a meticulous human can take a second for AI to pinpoint and resolve.
That benefit alone is a big factor driving IT leaders to look at AIOps as an option for optimizing their environments to free teams up for jobs that help serve the business’s bottom line.
Are companies positioned to take advantage of AIOps?
The biggest takeaway from Sean and Bill was discussing whether or not companies would be positioned to integrate AI successfully. According to Sean, it all depends on the strategic approach.
More companies will adopt AIOps within the next 2-3 years. Both agree with Linders that it’s going to happen, but whether AIOps adoption happens intentionally or out of necessity is to be seen. Will companies be prepared to take it on? The key is to strategize correctly for the long-term AIOps investment goals and gains.
Catch the full details from AIOps Evolution Weekly.
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