As more organizations transition to distributed services, IT teams are experiencing the limitations of traditional monitoring tools, which were designed for yesterday’s monolithic architectures. In its report “Innovation Insight for Observability¹,” global research and advisory firm Gartner describes the advantages of observability for cloud monitoring as organizations navigate this shift. As the new standard of monitoring, observability enables I&O, DevOps, and SRE teams alike to gain critical insights into the performance of today’s complex cloud-native environments.
Gartner defines observability as the characteristic of software and systems that allows administrators to collect external- and internal-state data about networked assets so they can answer questions about their behavior. I&O, DevOps, and SRE teams can then leverage that data to investigate anomalies, engage in observability-driven development, and improve system performance and up-time.
While observability is in its early stages with less than 10% of enterprises adopting it as of 2020, Gartner predicts 30% of companies with cloud-based architectures will be employing observability techniques by 2024.
Where traditional monitoring falls flat
Organizations are investing in observability because of growing frustration with existing monitoring tools and their inability to scale with cloud environments. According to the report, traditional monitoring tools don’t offer meaningful insight as to how performance issues with apps and services impact the customer experience or business KPIs.
While conventional monitoring does enable teams to create dashboards that flag problem scenarios, these dashboards often don’t scale or self-adjust under fluctuating loads to reflect the “unknown unknowns”. Such blind spots leave DevOps teams with so-called “watermelon dashboards:” all metrics read green for good system health, even as their systems deliver red for bad user experiences that generate customer complaints. These outcomes can damage an organization’s reputation and its bottom line.
The case for observability
As Gartner notes, observability is not just the result of implementing advanced tools, but an inbuilt property of an application and its supporting infrastructure. The architects and developers who create the software must design it to be observed. Then teams can leverage and interpret the observable data.
Gartner characterizes observability as the evolution of traditional monitoring capabilities in response to the demands of cloud-native technologies. Unlike conventional approaches to visibility, observability means looking at the complete software stack — no matter how broad or complex — to track every function and request in the full context of its underlying infrastructure and business uses.
By providing this enhanced context, observability can offer relevant insights to many different teams in an organization. For I&O teams, this translates to improved application uptime and performance, reduced time to identify and resolve issues, lowered infrastructure costs, and improved coverage of modern architectures. Development teams, meanwhile, will enjoy tighter alignment and integration with operations teams using a single source of truth — one unified body of analysis based on metrics, traces, and logs— to inform their work toward a common goal: an improved user experience. From the end-user perspective, all of this could mean a potential problem eliminated before it ever occurs, or an improvement made before it’s demanded. Together, these benefits add up to better business outcomes for the forward-thinking enterprise.
Achieving meaningful observability
The sheer volume of a modern enterprise’s data presents a challenge to those tasked with monitoring it, says Gartner. Achieving the necessary visibility to find anomalies and reliably identify their ultimate effects can be a task well beyond human ability. This is why the report frames artificial intelligence for IT operations (AIOps) as a crucial enabler of observability within today’s massive cloud-native architectures that increasingly rely on microservices and containerized environments. An AI-powered solution can rapidly establish and adjust performance baselines and automatically detect anomalies across distributed systems.
Dynatrace’s software intelligence platform enables I&O, DevOps, and SRE teams to collaborate on a broad set of purpose-built use cases in a single solution. Dynatrace is the only observability platform that places applications and services in real-time context with the environments they run on, providing distributed tracing enriched with code-level analysis and digital experience data. In fact, in the 2020 Gartner Magic Quadrant for Application Performance Monitoring², Dynatrace was named a Leader, ranking best for completeness of vision. Dynatrace also scored highest in 5 of 6 critical capabilities, demonstrating its usefulness as the solution for drawing key insights from observability-optimized environments, in Gartner’s 2020 Critical Capabilities for Application Performance Monitoring³ evaluation.
One source of automatic and intelligent observability means teams can spend less time manually instrumenting and monitoring assets and more time delivering features and insights that will benefit customers and drive revenue.
For additional insights into the role of observability in cloud monitoring and beyond, read Gartner’s full report and recommendations¹.
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¹Gartner Innovation Insight for Observability, 28th September 2020, Padraig Byrne, Josh Chessman
²Gartner Magic Quadrant for Application Performance Monitoring, 22 April 2020, Charley Rich and Federico De Silva
³Gartner Critical Capabilities for Application Performance Monitoring, 22 April 2020, Charley Rich and Federico De Silva