The issue of data volume and management complexity confronting organisations that utilise cloud technologies has been highlighted in a new global survey from Dynatrace. Companies are grappling with the challenge of taming the explosion of data resulting from the usage of multicloud environments. Traditional Artificial Intelligence for IT Operations (AIOps) models, once thought to be effective, are proving insufficient, according to the study.

In the report, 88% of organisations confessed that the complexity of their technology stack has escalated in the previous year, and half of them foresee it surging further. Moreover, an eye-opening 86% of technology leaders believ that cloud-native technology stacks generate a deluge of data surpassing human-management capacity. Further compounding the issue, the average multicloud environment is now spread across 12 different platforms and services, elevating the complexity of data handling and management.

Alarmingly, this exponential increase in data and complexity has severe knock-on effects, with 81% of technology chiefs admitting that time spent maintaining monitoring tools and priming data for analysis detracts from innovation efforts. Even more notably, an overwhelming 97% of these leaders find that traditional AIOps models offer limited assistance, leaving teams poorly equipped to wrangle the data avalanche.

"Cloud-native architectures have become mandatory for modern organisations, bringing the speed, scale, and agility they need to deliver innovation," said Bernd Greifeneder, CTO at Dynatrace. However, these very architectures are producing a huge amount of data, making it increasingly difficult to monitor and secure applications, thereby impacting critical outcomes.

In grappling with this complexity, organisations are resorting to more tools, with an average entity using 10 different monitoring and observability tools. However, quantity does not equate to quality, as 85% of technology leaders agree that the sheer volume of tools, platforms, dashboards, and applications increases their management burden.

The study further illustrates the limitations of traditional approaches to data management. Manual methods fall flat, unable to keep pace with the rapid changes in technology stacks and the resulting data heaps. Echoing this sentiment, two-fifths of technology leaders conceded that the time their teams invest on maintaining tools and readying data proves a drain on innovation.

The need for advancements in AI, analytics, and automation is increasingly apparent, with 72% of organisations adopting AIOps to undercut the complexity of managing their environments. Regrettably, 97% of technology leaders state that the value AIOps delivers is constricted due to the substantial manual effort needed to glean meaningful insights. "To overcome the complexity of modern technology stacks, organisations require advanced AI, analytics, and automation capabilities," concluded Greifeneder.

The data used in this study was collected via a wide-ranging survey of 1,300 senior technology leaders hailing from large enterprises.