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.
What Is Observability?
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.
Why Observability Is Not Enough
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:
- Manual work: Although observability tools can automatically correlate and analyze data, they ultimately require manual effort on the part of engineers to react to those insights. In other words, observability won’t automatically solve a problem for you; it will just help you understand the nature of the problem.
- Multiple tools: Observability requires multiple types of data – metrics, logs, traces, and possibly even information from data sources such as ticketing systems. Collecting all of this data requires multiple tools and complex agent orchestration. Even if you do it all with a single observability platform, you will still in effect be using multiple tools within that platform.
- MTTR remains an issue: Observability may help teams resolve issues faster than they could using monitoring tools alone. But because of the lack of automated remediation, observability doesn’t minimize MTTR as much as possible.
- Toil: The manual effort and interpretation that observability requires on the part of engineers means that “toil” remains a challenge. Observability tools may help teams make sense of complex issues, but ultimately, they still leave them sitting in front of dashboards, trying to figure out how to solve problems before they violate an SLA.
- Lack of continuous improvement: Observability tools don’t get better over time on their own. They simply keep doing the same thing: collecting data and helping teams interpret it. Unless you reconfigure your observability tools to improve data collection and analysis, you’re stuck with the same level of insights indefinitely.
Actionability Through AIOps
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.
Conclusion: The Finish Line Is Actionability
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.