Beyond Observability: Putting Intelligence in Modern Monitoring
By applying AI to observability data, dev teams can separate the signals and important inferences from the telemetry.
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If you’re paying attention to anything that’s happening in the development world, you’re likely familiar with the term “observability.” We’re seeing more and more monitoring companies from all different backgrounds jumping on the term to describe their solutions, many claiming their observability tool to be the factor that will take businesses to the next level.
Growing out-of-control system engineering, observability allows dev teams to unify and study the behaviors of various IT systems through the external outputs of the internal systems. In the case of software, that’s log events, distributed tracing, and time-series metrics. By unifying the data streaming through today’s complex IT environments, it certainly gives SREs and DevOps practitioners a leg up from traditional monitoring. But the data alone is no longer enough.
The Problem With Observability
Common observability tools, gathering mass amounts of monitoring data, hinder the success of DevOps practitioners and SREs. Information from these tools is low level, redundant and noisy. And it’s way too much for the human mind to make sense of. An SRE could spend their entire day simply filtering the noise from the meaningful data. Not to mention, DevOps practitioners and SREs simply don’t have the insight to interpret and understand what the data is telling them and how to respond once they’ve separated the meaningful data from the noise.
Not only is this causing them to miss issues that could quickly turn into customer-impacting downtime or glitches, but it’s also creating a sense of overwhelming burnout as they spin their wheels on meaningless noise. After all, what good is observability data if you can’t figure out what to do with it? It’s like having a pile of remote controls in a room full of blaring TVs, but not knowing which remote control goes to which TV.
Intelligence’s Role in Observability
With AI, though, observability data has totally new potential. Together, they provide a new level of agility and the ability to improve the DevOps process and ultimately the customer value. By applying AI to observability data, dev teams can separate the signals and important inferences from the telemetry, identifying what system data is actually meaningful to the improvement of the business processes. As the relevant data surfaces, this new intelligence creates correlations and identifies causal relationships, lighting a path forward to resolution by identifying where problems are happening, why they’re taking place and how they can be resolved. At the end of the day, there is no true observability without added AI.
This new wave of observability — observability with AI — will separate the leaders from the pack, creating better business outcomes at machine speed. Not only will it create a path forward for improvement for current systems, but also frees SREs and DevOps practitioners from the toil they’re currently so overloaded with, allowing them to spend more time on building competitive, rewarding new innovations.
Taking Action With Intelligent Observability
As a developer, there are a few practical steps to take before you can start reaping the benefits of intelligent observability. First, set the scene for actionability. Identify several services that can act as your test bed for intelligent observability, instrument those services to produce some meaningful metrics, logs and or traces, that will be combined with the infrastructure metrics and logs. Once you have your services instrumented and the data flowing, it’s time to focus on the people and processes behind the technology.
Create streamlined, automated workflows by connecting your intelligent observability platform to existing tools like Slack or Jira, and start socializing the new workflow across your team. As your team realizes the power of automation and actionability handled by intelligent observability, you’ll begin to quickly transition your focus from toilsome tasks to new, rewarding innovation.
Investing in modern technology that combines the power of both observability and AI will propel companies into the modern, digital-first era. As dev teams are enabled to make smarter and faster decisions through automated detection, diagnosis and remediation of problems, they’ll see a new level of agility and the ability to deliver bigger, better customer experiences.
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