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The Greatest Benefit of Observability: Prioritizing and Resolving Issues Faster

The need for real-time, reliable data is increasing, and that data is a necessity to remain competitive in today's business landscape. At the same time, observability has become even more critical with the complexity of a hybrid multi-cloud environment.

"In today's complex hybrid multi-cloud environment, CIOs understand that monitoring of logs, metrics, and traces is no longer sufficient," said Will Schoeppner, Research Director covering application performance management and business intelligence at Enterprise Management Associates (EMA), and author of the a new research report, Driving Observability Through Machine Learning and Predictive Analytics. "Organizations require an observability solution that will provide crucial visibility into the health and performance of the environment and enable predictive solutioning and remediation of critical events prior to impacting customer performance."

To add to the challenges and complexity, the term "observability" has not been clearly defined and can be broad in context. Across the industry, a commonality is that the reach of observability extends well beyond simply the collection of logs, metrics, and traces. Unified observability brings infrastructure monitoring, security, logs, application performance monitoring, and SaaS monitoring into a single platform for complete end-to-end visibility for cross-functional teams, driving streamlined collaboration and faster resolution of issues. Based on this definition, EMA's research explores challenges technology teams face in a complex landscape and how the benefits of observability can have an impact on driving business outcomes and customer success.

This study explored the rapid growth of observability and its critical importance in an organization. It also evaluated how observability that provides predictive analytics developed using machine learning models can make the difference in delivering customer expectations, reducing technology resource cost, and eliminating fatigue within an organization's technology teams.

The research delivered several fascinating key findings detailed throughout the report. Some of these key findings are:

■ 73% of companies indicated they have been data-driven in their decision-making process for three years or more.

■ Only 27% of organizations use the same solution for observability across all IT software development functions.

■ 71% of companies indicated they have been mature in the use of analytics and the use of machine learning in observability for three years or more. However, only 54% of organizations believe their maturity in analytics and the use of machine learning in observability is advanced or superior.

According to respondents, the greatest benefit of observability is being able to prioritize and resolve issues faster, followed by being able to proactively detect issues.

The EMA report was sponsored by Elastic.

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

The Greatest Benefit of Observability: Prioritizing and Resolving Issues Faster

The need for real-time, reliable data is increasing, and that data is a necessity to remain competitive in today's business landscape. At the same time, observability has become even more critical with the complexity of a hybrid multi-cloud environment.

"In today's complex hybrid multi-cloud environment, CIOs understand that monitoring of logs, metrics, and traces is no longer sufficient," said Will Schoeppner, Research Director covering application performance management and business intelligence at Enterprise Management Associates (EMA), and author of the a new research report, Driving Observability Through Machine Learning and Predictive Analytics. "Organizations require an observability solution that will provide crucial visibility into the health and performance of the environment and enable predictive solutioning and remediation of critical events prior to impacting customer performance."

To add to the challenges and complexity, the term "observability" has not been clearly defined and can be broad in context. Across the industry, a commonality is that the reach of observability extends well beyond simply the collection of logs, metrics, and traces. Unified observability brings infrastructure monitoring, security, logs, application performance monitoring, and SaaS monitoring into a single platform for complete end-to-end visibility for cross-functional teams, driving streamlined collaboration and faster resolution of issues. Based on this definition, EMA's research explores challenges technology teams face in a complex landscape and how the benefits of observability can have an impact on driving business outcomes and customer success.

This study explored the rapid growth of observability and its critical importance in an organization. It also evaluated how observability that provides predictive analytics developed using machine learning models can make the difference in delivering customer expectations, reducing technology resource cost, and eliminating fatigue within an organization's technology teams.

The research delivered several fascinating key findings detailed throughout the report. Some of these key findings are:

■ 73% of companies indicated they have been data-driven in their decision-making process for three years or more.

■ Only 27% of organizations use the same solution for observability across all IT software development functions.

■ 71% of companies indicated they have been mature in the use of analytics and the use of machine learning in observability for three years or more. However, only 54% of organizations believe their maturity in analytics and the use of machine learning in observability is advanced or superior.

According to respondents, the greatest benefit of observability is being able to prioritize and resolve issues faster, followed by being able to proactively detect issues.

The EMA report was sponsored by Elastic.

The Latest

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...