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The State of Observability 2024: Navigating Complexity With AI-Driven Insights

The report reveals the challenges of complex multi-cloud environments and the need for advanced AI, analytics, and automation.

In today's fast-paced digital landscape, organizations increasingly embrace multi-cloud environments and cloud-native architectures to drive innovation and deliver seamless customer experiences. However, the 2024 State of Observability report from Dynatrace reveals that the explosion of data generated by these complex ecosystems pushes traditional monitoring and analytics approaches to their limits.

The research, which surveyed 1,300 CIOs and technology leaders from large organizations worldwide, highlights the pressing need for a mature AI, analytics, and automation strategy to overcome the challenges posed by modern cloud environments. As Andi Grabner, Dynatrace DevOps Activist, aptly puts it, "'Multicloud environments' and 'cloud-native architectures' are not just buzzwords; they are the reality of today's complex and dynamic IT landscape. They enable developers, engineers, and architects to drive innovation and introduce new challenges."

Complexity on the Rise

One of the most striking findings from the report is that 88% of organizations have seen an increase in the complexity of their technology stack over the past 12 months, with 51% expecting this trend to continue. The average multi-cloud environment now spans 12 different platforms and services, making it increasingly difficult for teams to monitor and secure applications effectively. 87% of technology leaders believe that multi-cloud complexity hinders their ability to deliver outstanding customer experiences, while 84% say it makes applications more challenging to protect.

Drowning in Data

The sheer volume of data generated by cloud-native technology stacks is also a major pain point, with 86% of technology leaders stating that it is beyond humans' ability to manage. Organizations currently use an average of 10 different monitoring and observability tools to monitor applications, infrastructure, and user experience. However, 85% of respondents say that the number of tools, platforms, dashboards, and applications they rely on only adds to the complexity of managing a multi-cloud environment.

Limitations of Traditional Approaches

Manual approaches to log management and analytics are no longer sufficient, with 81% of technology leaders acknowledging that they cannot keep pace with the rate of change in their technology stack and the volumes of data it produces. Furthermore, 81% of respondents say that the time their teams spend maintaining monitoring tools and preparing data for analysis takes away from innovation efforts.

To address these challenges, 72% of organizations have adopted AIOps to reduce the complexity of managing their multi-cloud environment. However, 97% of technology leaders find that traditional AIOps models, which rely on probabilistic machine learning approaches, have limited value due to the manual effort required to gain reliable insights.

The Path Forward: Advanced AI, Analytics, and Automation

Grabner emphasizes, "To be successful, these teams must ensure that their applications are consistently accessible and functional across all platforms and services; operational efficiency and effectiveness are paramount; and security is non-negotiable, given the heightened complexity of these environments." He stresses the urgency for developers, engineers, and architects to move beyond traditional AIOps and adopt advanced AI, analytics, and automation solutions that provide full observability and control over their cloud ecosystems.

The key takeaways for technology executives are clear:

  1. Embrace advanced AI and analytics: Organizations must adopt AI-driven observability solutions to unify diverse data, retain context, and power analytics and automation with hypermodal AI techniques, including causal, predictive, and generative AI. This approach enables teams to unlock valuable insights from their data, drive smarter decision-making, and implement intelligent automation.

  2. Prioritize automation: To keep pace with the complexity of modern cloud environments, organizations must prioritize automation. By leveraging AI-driven insights, teams can automate routine tasks, quickly identify and resolve issues, and optimize application performance and security.

  3. Foster collaboration and innovation: The explosion of data and the complexity of multi-cloud environments can hinder innovation if not managed effectively. By adopting a mature AI, analytics, and automation strategy, organizations can free up their teams to focus on high-value tasks and collaborate more effectively, ultimately driving innovation and business growth.


As the digital landscape continues to evolve, technology executives must invest in advanced observability solutions to help them navigate the complexity of multi-cloud environments. By embracing AI-driven insights, automation, and collaboration, organizations can unlock the full potential of their cloud ecosystems and deliver exceptional customer experiences while ensuring the security and resilience of their applications.


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