Annual Global CIO Report Reveals Cloud-Native Technologies Produce Explosion of Data Beyond Humans' Ability to Manage
97% of technology leaders find traditional AIOps models are unable to tackle the data overload
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
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
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