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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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Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...