Observability is the new standard for visibility. Here are five myths around observability and some key points on how it can benefit your business.
IT, DevOps, and SRE teams seeking to know the health of their apps and services have always faced obstacles that can drain productivity, stifle collaboration, ratchet up the time to resolution, and limit the effectiveness of their collaboration with other parts of the business. As organizations update their IT environments with the latest cloud-native technologies and architectures, teams need to weigh the effectiveness of traditional monitoring vs. modern, observability-based solutions to decide how to solve their existing challenges amid the growing complexity of their dynamic, multi-cloud environments.
Legacy approaches to monitoring give your team piles of siloed data, but sometimes provide only fragments of insight into specific layers of the technology stack without context. They often require painstaking manual processes to piece together an accurate picture and pinpoint the source of a problem.
An approach based on observability, automation, and AI, on the other hand, enables you to know precisely what is happening within your environment based on contextualized insights derived from billions of interdependencies among apps, services, and infrastructure. Dashboards and visualizations display precise real-time answers prioritized by business impact, cutting through alert noise so you can prioritize the most important issues first.
Despite its obvious advantages, there are several misconceptions that keep businesses from implementing a modern AI-driven observability platform. To set the record straight, here are five myths about AI-based observability along with some key points on its full capabilities and benefits.
Myth 1 – Enterprise-scale observability is too difficult to accomplish
Although many IT and DevOps teams would love to be able to immediately identify the root cause of application, infrastructure, and user experience issues, they may be skeptical that this goal is even possible to accomplish. After all, they’re often using multiple monitoring tools to keep track of increasingly complex environments, and most of the time, these tools don’t talk to one another. Or they may think that observability is just a fancier word for traditional monitoring.
Fortunately, automatic and intelligent observability is easily achievable using AI, and offers benefits that extend beyond IT. With a single source of all internal and external observability data, IT teams can zero in on performance issues across the full software stack, from cloud infrastructure, to Kubernetes orchestration, to end-user apps, and see how services interact with each other in real time. DevOps can view the health of all systems with greater clarity and gain a window into the user experience. Meanwhile, business metrics tie everything together with KPIs that automatically connect digital performance with business outcomes. In addition to the benefits of intelligent automation, having a single source of observability data for all teams also means organizations can streamline operations and tool sets, easing the burden on all teams involved and improving cross-team collaboration.
Myth 2 – AI-based observability won’t help
On average, organizations are using 10 monitoring solutions across their technology stacks despite having full observability into just 11% of their application and infrastructure environments. If digital teams already have multiple monitoring tools in place, they may think an AI-based observability solution won’t help. But if you asked them if they need to eliminate blind spots across the scale, complexity, and frequent changes of multi-cloud environments, pinpoint root causes among trillions of dependencies, and reduce noise to prioritize only what matters, they would probably answer with a resounding “yes.”
An automatic and intelligent observability platform with AI at the core delivers on all three of those goals, empowering organizations to realize the benefits of cloud-native technologies while cutting through their complexity. With a single AI-based solution, DevOps teams can streamline complex environments, automate their work, and release better software more quickly.
Myth 3 – Users won’t know the difference
When you rely only on siloed telemetry from back-end applications to make sure your front-end applications, microservices, containers, and infrastructure work as intended, it’s hard to get insight into the actual user experience. A solution that offers true AI-driven observability, by contrast, can show you precisely how your applications and infrastructure are performing in real time. In fact, you can instantly access a session playback recording and see exactly what the user was doing and right where they ran into a problem. From there, you can quickly resolve the issue and deliver a smooth customer experience that keeps revenue flowing.
Myth 4 – An observability solution can’t handle multi-cloud complexity
Even IT, DevOps, and SRE professionals who think observability is technically achievable may still assume that only applications that run on a single cloud platform is within its scope, resulting in only a partial view of their environment. But an AI-powered observability solution that uses continuous automation is ideally suited to monitor multi-cloud environments, enabling businesses to successfully manage cloud complexity and keep pace with rapidly accelerating digital transformation.
Intelligent observability can handle the full scale, volume, and diversity of business data across an organization’s multicloud and hybrid cloud environments, including Kubernetes, serverless apps, and open-source technologies. Deterministic AI can identify the relationships and dependencies among apps, services, and infrastructure based on an automated, interactive real-time map of the environment. With this capability, a solution can deliver precise, context-driven insight to just about any question, workload, or scenario.
Myth 5 – AI-based observability requires too much administrative overhead
Manually instrumenting applications to get observability data means constantly rewriting, retesting, and redeploying each application—and then you have to define performance thresholds for all your infrastructure components and services before monitoring observability data can even begin.
IT teams simply don’t have time for this. As it is, 70% of CIOs say their teams are forced to spend too much time doing manual tasks that could be automated if only they had the means.
Fortunately, there’s a better way. An AI-driven observability solution can automatically set performance parameters derived from a past baseline of normal behavior, which frees your team from the laborious task of having to manually configure each setting. And the best solutions require just a few clicks to deploy a single self-updating agent. With so much maintenance work out of the way, you can focus on innovating and optimizing applications that improve business outcomes and user experiences.
Innovate faster with an all-in-one observability solution
IT and DevOps teams are under pressure to deliver rapid business innovation within increasingly complex environments, all while running on lean resources. Traditional monitoring and siloed observability tools put the burden on these teams to manually troubleshoot performance issues, whereas an AI-driven approach simply presents them with the answers they need. By taking advantage of the full capabilities and benefits of an all-in-one, automatic and intelligent observability platform, organizations can continuously deliver a compelling user experience that delivers results.
For more about observability and how it can assist with your digital transformation strategy, join us for the on-demand video series with Nancy Gohring, Senior Analyst at 451 Research: The Modern Observability Strategy: How and Why to Evolve Your Monitoring.
This syndicated content is provided by Dynatrace and was originally posted at https://www.dynatrace.com/news/blog/5-myths-about-observability/