You’ve heard of observability, which has fast become one of the IT industry’s buzzwords du jour.
But what about actionability, or the ability to translate observability into meaningful action? The latter term may not be a trending buzzword (not yet) – indeed, “actionability” perhaps sounds almost boring – but it’s just as essential as observability in managing complex, cloud-native environments.
Here’s what actionability means, how it’s different from observability, and what it takes to achieve actionability in practice.
In a nutshell, observability is the practice of understanding what is happening internally within a system based on external outputs.
Observability builds upon, but does not replace, Application Performance Management (APM). Whereas APM focuses on collecting metrics, observability takes things to the next level by helping to correlate monitoring data and identify root causes.
In this way, observability not only tells you whether an application or service is up and working, but also what the nature of the problem is when something goes wrong. Observability can also help teams assess the impact of an issue by determining what the problem’s consequences are for the business.
Observability is a key part of any modern performance management strategy. But on its own, it doesn’t deliver the actionable insights that teams need in order to optimize performance to the fullest extent possible.
Observability alone falls short for several reasons:
Actionability, which teams can achieve by pairing observability tools with AIOps tools that use AI to inform IT operations, makes it possible to overcome the shortcomings of observability and achieve a higher level of optimization.
With AIOps, the data gleaned from observability can drive automated remediations. Instead of waiting on human engineers to interpret observability data and take action to resolve a problem, AIOps tools can automate remediation for them, leading to MTTR that is as fast as possible.
At the same time, because AIOps can be applied to core ITSM workflows to achieve functionality such as automated ticket management and enrichment, it directly reduces the toil that engineers experience. Instead of sorting through tickets or staring at dashboards trying to decide what to do next, IT teams can leverage AIOps to minimize the time required to take actionable steps toward issue resolution.
AIOps also simplifies the observability tooling landscape. By ingesting data from multiple sources and making decisions based on that data, AIOps tools allow teams to centralize their observability strategy around a single platform.
Finally, AIOps tools are capable of self-learning. By studying remediation outcomes, AIOps platforms continuously improve their ability to react to similar situations. In this way, they are able to achieve even faster MTTR and automate the remediation of increasingly complex problems without requiring human intervention.
Ultimately, AIOps helps IT teams make practical use of AI in order to implement “self-driving” solutions. The result is ITSM workflows where routine problems are automatically remediated by AIOps tools. And even when truly complex problems arise that require manual response, AIOps provides engineers with the enriched data they need to act as quickly, and with as little toil, as possible.
Observability is great. But like APM, observability is just one step toward a fully modern performance management strategy. To achieve reliable performance with the fastest recovery time, teams should strive for AIOps-powered actionability within their monitoring and performance management workflows.
Watch this short video to see how the ability to achieve actionable insights is a key element you’ll want as part of your AIOps solution.