Zenoss Recognized in
Zenoss Recognized in
At a high level, artificial intelligence for IT operations (AIOps) refers to the way in which an IT team collects, manages and analyzes data. As the sheer volume of information continues to increase exponentially, it can become next to impossible for any human to analyze. AIOps tools aim to solve this issue by using artificial intelligence to accelerate problem resolution in any IT environment.
The original (Generation 1) AIOps strategy does this by performing analysis on mass quantities of event data and inferring potential root causes based on data analytics of similar previous issues. Generation 1 AIOps use cases are limited, as they were not designed to tell you how issues with a given system affect IT services or applications. Instead, these tools solely rely on processed event data, leaving blind spots due to limited visibility.
A Generation 2 AIOps platform like Zenoss feeds machine learning algorithms a more robust data set, including real-time IT service mapping. This means eliminating the number one problem AIOps tools have experienced thus far — limited visibility and context due to the lack of cardinality in the data they're analyzing. Zenoss delivers a new level of analytics capabilities because it is processing all data types, including metrics, dependency data, events, logs and streaming data. This provides unprecedented context and unprecedented acceleration of problem resolution.
Zenoss is a Generation 2 AIOps solution that combines full-stack monitoring with machine learning analytics to ensure you get the most out of big data. That means eliminating the critical flaws in Generation 1 AIOps tools — a lack of IT service topology and limited visibility and context due to the lack of cardinality in the data they're analyzing.
Zenoss delivers an unprecedented level of AIOps capability by using full-stack monitoring and machine learning to process all of your data sources, including metrics, dependency data, events, logs and streaming data. The platform feeds dynamic topology data to the machine learning algorithms, providing needed context to automatically root cause problems.
Observability is a term originating in control theory that refers to the measure of how well the internal states of a system can be inferred based on the knowledge of its external outputs, according to Wikipedia.
In the context of IT operations, some Generation 1 AIOps software vendors may intentionally misuse this term. They want to convince you that observability means that you can understand exactly what's happening on the inside of a system by simply looking at its external components. But why do they want you to believe this?
Collecting machine data can be difficult, and this complexity is only compounded by the dynamic nature of modern IT systems. On the other hand, collecting events is easy. In an ideal world, an AIOps solution waits for the anomaly detection tool to send incident reports, analyzes them, and then magically spits out actionable insights to resolve the issue.
If this process sounds like a dream, that's because it has no basis in reality. This is akin to saying you'll know exactly how to fix your car based on a check engine light. Just because you can observe the outside of the system, doesn't mean you'll know what the problem is. Apply this to software applications, and we can see how these Generation 1 "solutions" have unsurprisingly failed to produce results.
Zenoss combines the power of monitoring with observability to derive the precise insights needed in today's complex IT environments.
Already have an AIOps tool? That's OK! Zenoss integrates with Generation 1 AIOps vendors to fill the gaps and provide you with the much-needed rich data sets that other solutions can't collect on their own. The net result is enriched event data analytics from existing tools with our dynamic IT service modeling.