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.
