Cloud-Native Technologies Produce Explosion of Data Beyond Human Ability to Manage

Learn more about Dynatrace

Organizations are continuing to embrace multicloud environments and cloud-native architectures to enable rapid transformation and deliver secure innovation. However, despite the speed, scale, and agility enabled by these modern cloud ecosystems, organizations are struggling to manage the explosion of data they create, according to The state of observability 2024: Overcoming complexity through AI-driven analytics and automation strategies, a report from Dynatrace.


Source: Dynatrace

These research findings underscore the need for a mature AI, analytics, and automation strategy that moves beyond traditional AIOps models to drive lasting business value.

Findings from the research include:

■ 88% of organizations say the complexity of their technology stack has increased in the past 12 months, and 51% say it will continue to increase.

■ The average multicloud environment spans 12 different platforms and services.

■ 87% of technology leaders say multicloud complexity makes it more difficult to deliver outstanding customer experiences, and 84% say it makes applications more difficult to protect.

■ 86% of technology leaders say cloud-native technology stacks produce an explosion of data that is beyond humans’ ability to manage.

■ On average, organizations use 10 different monitoring and observability tools to manage applications, infrastructure, and user experience.

■ 85% of technology leaders say the number of tools, platforms, dashboards, and applications they rely on adds to the complexity of managing a multicloud environment.

"Cloud-native architectures have become mandatory for modern organizations, bringing the speed, scale, and agility they need to deliver innovation," said Bernd Greifeneder, CTO at Dynatrace. "These architectures reflect a growing array of cloud platforms and services to support even the simplest digital transaction. The huge amount of data they produce makes it increasingly difficult to monitor and secure applications. As a result, critical business outcomes like customer experience are suffering, and it is becoming more difficult to protect against advanced cyber threats."

Additional findings include:

■ 81% of technology leaders say manual approaches to log management and analytics cannot keep up with the rate of change in their technology stack and the volumes of data it produces.

■ 81% of technology leaders say the time their teams spend maintaining monitoring tools and preparing data for analysis steals time from innovation.

■ 72% of organizations have adopted AIOps to reduce the complexity of managing their multicloud environment.

■ 97% of technology leaders say probabilistic machine learning approaches have limited the value AIOps delivers due to the manual effort needed to gain reliable insights.

"Without the ability to transform the high volumes of diverse data from cloud-native architectures into real-time, contextually relevant insights, IT, development, security, and business teams struggle to understand what is happening in their environment and lack the answers needed to solve issues quickly and decisively," continued Greifeneder. "While many organizations turn to AIOps, they often encounter limited value due to reliance on probabilistic methods, which can be imprecise and time-consuming to implement. To overcome the complexity of modern technology stacks, organizations require advanced AI, analytics, and automation capabilities. By unifying diverse data, retaining its context, and powering analytics and automation with a hypermodal AI that combines multiple techniques, including causal, predictive, and generative AI, teams can unlock a wealth of insights from their data to drive smarter decision-making, intelligent automation, and more efficient ways of working."

Methodology: This report is based on a global survey conducted by Coleman Parkes and commissioned by Dynatrace of 1,300 CIOs, CTOs, and other senior technology leaders involved in IT operations and DevOps management in large enterprises with more than 1,000 employees. The sample included 200 respondents in the U.S., 100 in Latin America, 600 in Europe, 150 in the Middle East, and 250 in Asia Pacific.

Related Links

Hot Topics

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

Cloud-Native Technologies Produce Explosion of Data Beyond Human Ability to Manage

Learn more about Dynatrace

Organizations are continuing to embrace multicloud environments and cloud-native architectures to enable rapid transformation and deliver secure innovation. However, despite the speed, scale, and agility enabled by these modern cloud ecosystems, organizations are struggling to manage the explosion of data they create, according to The state of observability 2024: Overcoming complexity through AI-driven analytics and automation strategies, a report from Dynatrace.


Source: Dynatrace

These research findings underscore the need for a mature AI, analytics, and automation strategy that moves beyond traditional AIOps models to drive lasting business value.

Findings from the research include:

■ 88% of organizations say the complexity of their technology stack has increased in the past 12 months, and 51% say it will continue to increase.

■ The average multicloud environment spans 12 different platforms and services.

■ 87% of technology leaders say multicloud complexity makes it more difficult to deliver outstanding customer experiences, and 84% say it makes applications more difficult to protect.

■ 86% of technology leaders say cloud-native technology stacks produce an explosion of data that is beyond humans’ ability to manage.

■ On average, organizations use 10 different monitoring and observability tools to manage applications, infrastructure, and user experience.

■ 85% of technology leaders say the number of tools, platforms, dashboards, and applications they rely on adds to the complexity of managing a multicloud environment.

"Cloud-native architectures have become mandatory for modern organizations, bringing the speed, scale, and agility they need to deliver innovation," said Bernd Greifeneder, CTO at Dynatrace. "These architectures reflect a growing array of cloud platforms and services to support even the simplest digital transaction. The huge amount of data they produce makes it increasingly difficult to monitor and secure applications. As a result, critical business outcomes like customer experience are suffering, and it is becoming more difficult to protect against advanced cyber threats."

Additional findings include:

■ 81% of technology leaders say manual approaches to log management and analytics cannot keep up with the rate of change in their technology stack and the volumes of data it produces.

■ 81% of technology leaders say the time their teams spend maintaining monitoring tools and preparing data for analysis steals time from innovation.

■ 72% of organizations have adopted AIOps to reduce the complexity of managing their multicloud environment.

■ 97% of technology leaders say probabilistic machine learning approaches have limited the value AIOps delivers due to the manual effort needed to gain reliable insights.

"Without the ability to transform the high volumes of diverse data from cloud-native architectures into real-time, contextually relevant insights, IT, development, security, and business teams struggle to understand what is happening in their environment and lack the answers needed to solve issues quickly and decisively," continued Greifeneder. "While many organizations turn to AIOps, they often encounter limited value due to reliance on probabilistic methods, which can be imprecise and time-consuming to implement. To overcome the complexity of modern technology stacks, organizations require advanced AI, analytics, and automation capabilities. By unifying diverse data, retaining its context, and powering analytics and automation with a hypermodal AI that combines multiple techniques, including causal, predictive, and generative AI, teams can unlock a wealth of insights from their data to drive smarter decision-making, intelligent automation, and more efficient ways of working."

Methodology: This report is based on a global survey conducted by Coleman Parkes and commissioned by Dynatrace of 1,300 CIOs, CTOs, and other senior technology leaders involved in IT operations and DevOps management in large enterprises with more than 1,000 employees. The sample included 200 respondents in the U.S., 100 in Latin America, 600 in Europe, 150 in the Middle East, and 250 in Asia Pacific.

Related Links

Hot Topics

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...