A survey of 504 DevOps practitioners finds 63% working for organizations that will be making additional investments in observability over the next two years, with 21% describing those investments as significant.
Conducted by Techstrong Research, an arm of Techstrong Group, the survey finds that 48% of respondents work for organizations that already practice observability on a regular basis.
While observability has always been a core tenet of any set of best DevOps practices, software engineering teams are moving beyond simply tracking a set of pre-defined metrics. Modern observability platforms make it possible to analyze the logs, metrics and traces created by an application environment in a way that enables DevOps to launch queries to help identify the root cause of an application performance issue.
Mitch Ashley, principal analyst for Techstrong Research, noted that as the complexity of application environments increases, observability technologies have improved resilience by making it easier to gracefully manage unexpected events or failures without having to take applications offline.
The challenge, of course, has been first finding the funding for observability initiatives, followed then by the issues that arise as DevOps teams move to consolidate tooling. Many software engineers naturally become attached to a particular monitoring tool. Convincing them to swap it out for another platform requires effort and, most importantly, training. Each organization will individually decide to what degree they may want to drive tool consolidation, but in many cases, the cost of acquiring an observability platform assumes savings will be generated by eliminating the need for other tools.
Ultimately, the need to apply artificial intelligence (AI) to manage DevOps workflows at scale is likely to force a transition to an observability platform. Machine learning algorithms embedded in observability are already being used to make it simpler to proactively identify application issues before they impact the business. The next wave of generative AI models, however, needs to be trained using data collected from across the application environment. Much of that data is, naturally, going to have been aggregated already by an observability platform.
In the meantime, application environments are only becoming more distributed, creating dependencies between traditional monolithic applications and emerging microservices-based applications that are becoming too complex for IT teams to manage without the aid of some type of observability platform infused with AI capabilities. While there is no silver AI bullet for achieving and maintaining observability there will soon come a day when software engineers are not going to want to work for organizations that don’t provide them access to AI tools. The odds of being successful, coupled with the likely level of stress and toil to be encountered, will simply be too high.
Of course, transitioning to an observability platform is a journey that starts first with being able to collect the telemetry data that DevOps teams need to analyze. Unfortunately, there are still large numbers of legacy applications that have never been instrumented but, hopefully, as more sources of telemetry data are added to an application environment the smoother that transition will become.
For more information, download a copy of the DevOps Next report here.
