Skip to main content

Cloud Complexity Growing Beyond Human Abilities

Pete Goldin
APMdigest

A widening gap between IT resources and the demands of managing the increasing scale and complexity of enterprise cloud ecosystems is evident, according to Top challenges for CIOs on the road to the AI-driven autonomous cloud, a new report based on a global survey of 800 CIOs conducted by Vanson Bourne and commissioned by Dynatrace.

According to the report, IT leaders around the world are concerned about their ability to support the business effectively, as traditional monitoring solutions and custom-built approaches drown their teams in data and alerts that offer more questions than answers.

Operations teams receive nearly 3,000 alerts from their monitoring and management tools each day

CIO responses in the research indicate that, on average, IT and cloud operations teams receive nearly 3,000 alerts from their monitoring and management tools each day. With such a high volume of alerts, the average IT team spends 15% of its total available time trying to identify which alerts need to be focused on and which are irrelevant. This costs organizations an average of $1.5 million in overhead expense each year. As a result, CIOs are increasingly looking to AI and automation as they seek to maintain control and close the gap between constrained IT resources and the rising scale and complexity of the enterprise cloud.

Too Many Alerts, Not Enough Relevant Information

Traditional monitoring tools were not designed to handle the volume, velocity and variety of data generated by applications running in dynamic, web-scale enterprise clouds. These tools are often siloed and lack the broader context of events taking place across the entire technology stack. As a result, they bombard IT and cloud operations teams with hundreds, if not thousands, of alerts every day. IT is drowning in data as incremental improvements to monitoring tools fail to make a difference.

■ On average, IT and cloud operations teams receive 2,973 alerts from their monitoring and management tools each day, a 19% increase in the last 12 months.

■ 70% of CIOs say their organization is struggling to cope with the number of alerts from monitoring and management tools.

■ 75% of organizations say most of the alerts from monitoring and management tools are irrelevant.

■ On average, just 26% of the alerts organizations receive each day require action.

Traditional monitoring tools only provide data on a narrow selection of components from the technology stack. This forces IT teams to manually integrate and correlate alerts to filter out duplicates and false positives before manually identifying the underlying root cause of issues. As a result, IT teams’ ability to support the business and customers are greatly reduced as they’re faced with more questions than answers.

■ On average, IT teams spend 15% of their time trying to identify which alerts they need to focus on, and which are irrelevant.

■ The time IT teams spend trying to identify which alerts need to be focused on and which are irrelevant costs organizations, on average, $1,530,000 each year.

■ The excessive volume of alerts causes 70% of IT teams to experience problems that should have been prevented.

■ 21 incidents, on average, are experienced by organizations each year that could have been prevented if alerts were seen or acted upon in time.

■ 79% of organizations say the volume of alerts, and the time required to sift through them to identify relevant results, is making it difficult to automate enterprise cloud operations.


Methodology: This report is based on a global survey of 800 CIOs in large enterprises with over 1,000 employees, conducted by Vanson Bourne and commissioned by Dynatrace. The sample included 200 respondents in the US, 100 in the UK, France, Germany and China, and 50 in Australia, Singapore, Brazil and Mexico.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

Cloud Complexity Growing Beyond Human Abilities

Pete Goldin
APMdigest

A widening gap between IT resources and the demands of managing the increasing scale and complexity of enterprise cloud ecosystems is evident, according to Top challenges for CIOs on the road to the AI-driven autonomous cloud, a new report based on a global survey of 800 CIOs conducted by Vanson Bourne and commissioned by Dynatrace.

According to the report, IT leaders around the world are concerned about their ability to support the business effectively, as traditional monitoring solutions and custom-built approaches drown their teams in data and alerts that offer more questions than answers.

Operations teams receive nearly 3,000 alerts from their monitoring and management tools each day

CIO responses in the research indicate that, on average, IT and cloud operations teams receive nearly 3,000 alerts from their monitoring and management tools each day. With such a high volume of alerts, the average IT team spends 15% of its total available time trying to identify which alerts need to be focused on and which are irrelevant. This costs organizations an average of $1.5 million in overhead expense each year. As a result, CIOs are increasingly looking to AI and automation as they seek to maintain control and close the gap between constrained IT resources and the rising scale and complexity of the enterprise cloud.

Too Many Alerts, Not Enough Relevant Information

Traditional monitoring tools were not designed to handle the volume, velocity and variety of data generated by applications running in dynamic, web-scale enterprise clouds. These tools are often siloed and lack the broader context of events taking place across the entire technology stack. As a result, they bombard IT and cloud operations teams with hundreds, if not thousands, of alerts every day. IT is drowning in data as incremental improvements to monitoring tools fail to make a difference.

■ On average, IT and cloud operations teams receive 2,973 alerts from their monitoring and management tools each day, a 19% increase in the last 12 months.

■ 70% of CIOs say their organization is struggling to cope with the number of alerts from monitoring and management tools.

■ 75% of organizations say most of the alerts from monitoring and management tools are irrelevant.

■ On average, just 26% of the alerts organizations receive each day require action.

Traditional monitoring tools only provide data on a narrow selection of components from the technology stack. This forces IT teams to manually integrate and correlate alerts to filter out duplicates and false positives before manually identifying the underlying root cause of issues. As a result, IT teams’ ability to support the business and customers are greatly reduced as they’re faced with more questions than answers.

■ On average, IT teams spend 15% of their time trying to identify which alerts they need to focus on, and which are irrelevant.

■ The time IT teams spend trying to identify which alerts need to be focused on and which are irrelevant costs organizations, on average, $1,530,000 each year.

■ The excessive volume of alerts causes 70% of IT teams to experience problems that should have been prevented.

■ 21 incidents, on average, are experienced by organizations each year that could have been prevented if alerts were seen or acted upon in time.

■ 79% of organizations say the volume of alerts, and the time required to sift through them to identify relevant results, is making it difficult to automate enterprise cloud operations.


Methodology: This report is based on a global survey of 800 CIOs in large enterprises with over 1,000 employees, conducted by Vanson Bourne and commissioned by Dynatrace. The sample included 200 respondents in the US, 100 in the UK, France, Germany and China, and 50 in Australia, Singapore, Brazil and Mexico.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...