As multi-cloud complexity grows, AI analytics and automation help manage vast data volumes, enabling smarter decision-making and improved customer experiences.
As organizations continue to embrace multi-cloud environments and cloud-native architectures, they still struggle to manage the explosion of data produced. Credit goes to the complex tech stacks these cloud environments are equipped with.
A survey by Dynatrace revealed that 92% of technology leaders say the number of tools, platforms, dashboards, and applications they rely on adds complexity to managing the multi-cloud environment. Here is where the scope for incorporating AI-driven analytics and automation strategies into systems is. But before that, let’s dig deeper into the challenges involved in handling high data volumes.
What are the challenges in handling loads of data packets in multi-cloud environments?
Although modern cloud ecosystems are scalable, fast and agile, CIOs and technology leaders in large organizations still show concerns about managing the explosion of data loads. Dynatrace’s research team surveyed 1300 global CIOs and technology leaders, out of which 150 respondents were from the Middle East. Here are a few key findings about the hurdles they face:
- Around 92% of them said multi-cloud complexity makes it more difficult to deliver outstanding customer experiences
- While 88% of the respondents showed their concerns about protecting these applications from cyber attacks
- Another group of surveyed technology leaders (97%) said that cloud-native technology stacks produce an explosion of data that is beyond humans’ capability to manage
Adding to his concerns about involved complexities in multi-cloud environments, Bernd Greifeneder, CTO at Dynatrace, said, “A vast array of different cloud platforms and services support even the simplest digital transaction, and the huge amount of data these environments 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.”
Also Read: Taming the Data Deluge: Why Data Orchestration is Critical
How are AI-powered Analytics and Automation Simplifying IT Workloads?
Although 85% of organizations are utilizing AIOps to reduce the complexity of managing their multi-cloud environment, some still struggle to gain valuable insights from their data, thanks to their manual efforts to gather those insights.
This means that probabilistic machine learning approaches limit the value AIOPs deliver if they are not automated. Thus, AI-driven analytics and automation together are ways to overcome the complexity of managing modern tech stacks.
Adding to the advantages of leveraging the capabilities of these methods, Greifeneder said, “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.”
Wrapping it up,
Cloud-native architectures will continue to unveil a firehose of data, challenging IT, development, security, and business teams. Organizations need only adopt advanced AI, analytics, and automation capabilities to overcome the hurdles.
