In today’s fast-paced digital landscape, organizations are increasingly embracing multi-cloud environments and cloud-native architectures to drive innovation and deliver seamless customer experiences. However, Dynatrace’s State of Observability 2024 report reveals that the explosion of data generated by these complex ecosystems is pushing traditional monitoring and analytics approaches to their limits.
The research, which surveyed 1,300 CIOs and technology leaders from large organizations worldwide, highlights the urgent need for a mature artificial intelligence, 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, but they also 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 in the past 12 months, and 51% expect this trend to continue. The average multi-cloud environment now spans 12 different platforms and services, making it increasingly difficult for teams to effectively monitor and secure applications. In fact, 87% of technology leaders believe the complexity of multi-cloud is hindering their ability to deliver outstanding customer experiences, while 84% say it makes applications more demanding to protect.
Drowning in data
The sheer amount of data generated by stacks of cloud-native technology is also a major pain point, with 86% of technology leaders saying it’s beyond human ability to manage. Organizations currently use an average of 10 different monitoring and observation 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 admitting they cannot keep up 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 their innovation efforts.
To meet these challenges, 72% of organizations have adopted AIOps in an attempt to reduce the complexity of managing their multi-cloud environment. However, 97% of technology leaders believe that traditional AIOps models, which rely on probabilistic machine learning approaches, have limited value due to the manual effort required to obtain reliable insights.
The way forward: advanced artificial intelligence, analytics and automation
As Grabner emphasizes, “To be successful, these teams must ensure that their applications are consistently available and functional across all platforms and services; operational efficiency and effectiveness are paramount; and security is out of the question, given the increased complexity of these environments.” .” It further highlights the urgency for developers, engineers and architects to move beyond traditional AIOs and adopt advanced AI, analytics and automation solutions that provide full visibility and control over their cloud ecosystems.
The key takeaways for technology executives are clear:
- Embrace advanced artificial intelligence and analytics: Organizations must adopt AI-driven observation solutions that can unify disparate 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.
- 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.
- Encourage cooperation and innovation: The data explosion and complexity of a multi-cloud environment can stifle 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.
Conclusion
As the digital landscape continues to evolve, it is imperative for technology executives to invest in advanced monitoring solutions that can help them navigate the complexities of a multi-cloud environment. By embracing AI-driven insights, automation and collaboration, organizations can unlock the full potential of their cloud ecosystems and deliver exceptional user experiences while ensuring the security and resilience of their applications.