Why Observability Paired with AI Makes Sense

AI enables teams to use data from their systems to get the insights they need quickly and effectively.

artificial intelligence

Observability, in general, is a way to assess how well a system works based on its outputs. For development teams, observability solutions provide information they can use to evaluate their processes and systems by identifying patterns or properties of the finished product.

Observability is not monitoring, which requires setting parameters in advance and logging relevant data. Conversely, observability tools don’t need teams to set metrics in advance. The tool provides insights on bugs and how to fix them, data that helps diagnose problems, and patterns that identify bottlenecks or barriers to efficient interaction. The tool can even reveal things the team didn’t know needed their attention.

Adam Frank, VP of Product and Design at Moogsoft, points out, however, that an observability tool alone is not enough to produce an effective correlation of actionable alerts—without the noise. Here are his insights on how DevOps teams can truly benefit from observability.

What issues do developers face with observability alone?

Frank: While observability allows developers to monitor and unify even the deepest parts of their systems — like log events, distributed tracing and time-series metrics — the unification of this data is not enough. Sure, it’s better than traditional monitoring, but it means developers quickly run into mass amounts of data they simply don’t have the time or brainpower to sort through and make sense of it. They have the data, but they don’t know what to do with it.

What do development teams need in addition to observability?

Frank: Development teams need artificial intelligence (AI) to make sense of their observability data. Intelligent automation separates the meaningless data from the meaningful—creating correlations and identifying causal relationships—to bring actionability to the chaos. This lights the pathway for development teams to figure out where issues are happening and why and spend their time building a permanent resolution to problems, rather than finding and fixing the same problems over and over again.

AI reduces the number of incident tickets created and delivers deep contextual insights that enable teams to solve IT problems faster than those relying on rules-based or manual approaches. In turn, this helps businesses deliver service to customers and frees up developers’ time to focus on building competitive, innovative new solutions.

What advice can you offer developers that want to strengthen their CI/CD processes?

Frank: Don’t underestimate the power intelligent observability can have when it’s built into the CI/CD pipeline. You’ll get new performance insights like change fail rate, deployment frequency and lead time, not to mention better verification, and gain a new perspective into how the business as a whole is improving processes.

What advice can you offer development teams new to observability?

Frank: Getting started with intelligent observability isn’t as difficult as you might think. All you have to do is identify a few targeted apps and services, pinpoint your data sources, and you’ll be reaping the rewards of truly actionable observability data in no time.

 

For more information, read Adam Frank’s resource, Observability with AIOps For Dummies.

Bernadette Wilson

Bernadette Wilson, a DevPro Journal contributor, has 19 years of experience as a journalist, writer, editor, and B2B marketer.


Bernadette Wilson

Bernadette Wilson, a DevPro Journal contributor, has 19 years of experience as a journalist, writer, editor, and B2B marketer.