No matter where you are in your AIOps journey, you’re probably already seeking to elevate your AIOps tools to new levels of efficiency. After all, optimization and efficiency are core values of adopting AIOps. The fastest path to getting more from your existing tooling is to incorporate an analysis engine that leverages unsupervised machine learning to unify all your data streams – including any non-technical data you might possess.
AIOps in Observability and Service Management
A quick web search shows the terms Artificial Intelligence (AI) and Machine Learning (ML) being used to describe a vast array of business tools, particularly in IT Management, spanning from security management to application observability. We often get asked why a more general AIOps platform is necessary when most specialized tools offer some level of ML or AI assistance.
Before we address this, here’s a quick AI refresher:
- Not everything labeled ML can actually learn
- Not everything tagged as AI is truly intelligent
There are lots of ways to incorporate “AI” into a solution, and most don’t involve applying intelligence at all. Keep this in mind as we progress.
What are AIOps Tools?
AIOps tools leverage artificial intelligence and machine learning to automate and enhance IT operations processes.
They collect and analyze massive amounts of data from various sources, including logs, metrics, and events, to identify patterns, anomalies, and potential issues. By automating routine tasks, providing intelligent insights, and enabling proactive problem resolution, AIOps tools help organizations improve IT efficiency, reduce downtime, and enhance overall service quality.
Why Do I Need an AIOps Tool / Platform?
A common misconception is that running a collection of point AIOps tools will lead to reaping the full benefits of AIOps. While you’re undoubtedly better off than not using automated and/or intelligent tools, point tools remain limited in scope. This Observability vs. AIOps blog post delves into concepts like anomaly prioritization and criticality, but that’s just one small example.
Even with point tools, SREs, DevOps, and DevSecOps tend to specialize their work, tasks, and goals, such as application performance, infrastructure efficiency, and intrusion detection. Each of their specific areas of focus revolves around the data they obtain from their central tool (application performance, end-user experience, IP logs, etc.).
Regardless of how advanced or “SMART” a specialty AIOps tool like Observability or Security Management might be, each operates within its own silo. Ironically, some tools talk about breaking down silos, but they struggle to do so within their confined domain. They lack a holistic view of how systems (beyond their specific focus) interact.
What remains are broad swathes of operations, data, processes, incidents, and more – that don’t present a cohesive narrative. These parts are still in silos – in some cases, MUCH larger ones. Depending on the tools you have, these silos can shrink or expand.
Crossing the [Data] Streams for AIOps Success
In Ghostbusters, we learned that Crossing the Streams was bad. That is, until crossing the streams became the solution to the bigger problem.
It’s the same with your company’s AIOps Data Streams.
Note that I said “Company”, not “IT Organization” – also notice that “AIOps” is crossed out.
At any given time, an organization is creating, processing, storing, and analyzing information from numerous sources, including IT operations, finance, fulfillment, sales, and marketing. Within IT, there can be even more granular data – infrastructure, cloud, APM, Observability, Security, Logs, Digital Experience, Google Analytics, etc.
And overarching all of them is a unifying element: the Unified Intelligence Engine.
Only unsupervised machine learning platforms like InsightFinder can break through siloed data to identify the root cause of any situation, in any environment.
Don’t Cross the Streams Without Unsupervised Machine Learning for AIOps
No, it won’t create “positronic reversal” – but you can’t simply feed multiple data streams into any analytics engine. If your machine learning requires supervision, it won’t break through the silos, even if you’re feeding it more data.
What is Supervised Machine Learning in AIOps?
Supervised machine learning involves establishing a baseline of rules based on a known set of data that your AI system understands. In other words, it means that the Observability ML engines know that their ingested data pertains to latency, load, performance, errors, end-users, etc. They also recognize that there’s a tech stack and a distributed service map that must be considered when making decisions.
Put simply, your observability ML won’t be helpful when trying to answer whether sales are down the first three days of the holiday season, nor WHY they’re down. Similarly, marketing analysis tools won’t identify your new cloud servers causing a 30% longer response time compared to a year ago (honestly, they probably won’t even be AWARE that response time is slower).
Supervised ML is excellent for point management tools (Observability, security, etc.), but it keeps this data in their silos, making it unable to analyze across the entire system.
What is Unsupervised Machine Learning for AIOps?
In contrast, unsupervised machine learning doesn’t require prior knowledge of the meaning of data. It simply analyzes data streams, identifies patterns and anomalies, and breaks down all the different data streams to find causality.
Realizing the Full Power of AIOps Tools
The classic IT management tool analogy involves identifying that elusive problem – the needle in the haystack. But is that truly the problem? No! The real challenge is to discover how the needle got there in the first place.
When you remove artificial limitations on what you’re analyzing, you can uncover some amazing correlated and causal results. You’ll not only be able to break through technical management silos, but the entire world of business analytics can become accessible.
Example: Imagine figuring out that your finance team’s decision to change the limit for individual purchasing decisions from $500 to $250 would result in 20% lower retail sales – because the performance of a few specific transactions is impacted by slightly older infrastructure that would normally be replaced ad hoc by empowered infrastructure operators.
You might even discover that the most successful updates occur on eclair day in the cafeteria. REALLY – ANYTHING is possible!
Top 10 AIOps Tools to Consider
When evaluating AIOps tools, prioritize those that offer unsupervised machine learning capabilities and seamless integration with your existing data sources. Some leading options include:
- InsightFinder: Known for its powerful unsupervised machine learning engine and ability to correlate data from diverse sources.
- Moogsoft: Offers AIOps solutions for incident management and event correlation.
- BigPanda: Specializes in alert correlation and noise reduction.
- Dynatrace: Provides full-stack observability with AI-powered insights.
- AppDynamics: Focuses on application performance monitoring and AIOps for troubleshooting.
- New Relic: Offers a comprehensive observability platform with AIOps capabilities.
- Splunk: Provides IT operations analytics and AIOps features.
- Elastic: Offers a search and analytics engine with AIOps functionalities.
- IBM Watson AIOps: Leverages AI to automate IT operations tasks and provide insights.
- ServiceNow ITOM: Offers IT operations management solutions with AIOps capabilities.
Remember that the best AIOps tool for your organization will depend on your specific needs and existing technology stack. Conduct thorough research and consider trials or demos before making a decision.
