Dynatrace research reveals how investments in DevOps automation are improving software quality and reducing costs. However, this solution is still in its infancy for more than half of companies. The survey was conducted among 450 IT professionals responsible for DevOps automation and security in large organizations. Between advantages most significant: a 61% improvement in software quality; -57% release failure and -55% IT costs.
In most organizations, however, DevOps automation practices are still in the early stages of maturity. The absence of a defined strategy for DevOps automation, the prevalence of toolchain complexity, and difficulties in analyzing observability and security data prevent companies from realizing the full impact of their investments.
The search results
This research highlights the need for data- and AI-driven automation practices that enable organizations to be more responsive to business needs.
Over the next 12 months, organizations will invest in DevOps automation for several reasons. For example, supporting security and compliance management (55%), infrastructure provisioning and management (52%), and performance optimization (51%). However, only 38% have a clearly defined DevOps automation strategy to guide these investments. DevOps Automation On average, organizations have managed to automate just over half (56%) of their end-to-end DevOps lifecycle. The average organization relies on more than seven different tools for DevOps automation. Among the main ones obstacles to automate new DevOps use cases: security concerns (54%), difficulty operationalizing data (54%), toolchain complexity (53%).
A fragmented approach
Bernd Greifeneder, Chief Technology Officer di Dynatrace
As more organizations adopt cloud-native software delivery, DevOps automation has evolved into a strategic imperative. The prevalence of Kubernetes architectures and technology stacks that have surpassed human capabilities to manage are driving the need for automated ecosystem orchestration and protection. Organizations are trying to meet this need by building and maintaining automation scripts using a growing set of open source tools combined with do-it-yourself approaches and manual efforts.However, this fragmented approach is starting to present challenges crepe. Teams are stuck in data silos, isolated pockets of automation, reactive and manual operations, and security efforts. They urgently need a unified approach to DevOps automation supported by AI, otherwise it will be impossible to accelerate innovation while maintaining software quality and security.
DevOps automation, new research explores the state of the art
Further research findings highlight:
71% of organizations use observability data and insights to drive automation decisions and improvements in DevOps workflows. However, 85% face challenges in using observability and security data to drive DevOps automation. The top three challenges organizations face include inaccessible data (51%), siled data (43%), and the need for data to flow through many systems to be analyzed (41%).
Three main advantages
54% of organizations are investing in platforms to enable easier tool integration and collaboration between teams involved in automation projects. 59% expect large language models (LLMs), such as ChatGPT and Bard, to have a significant impact on their DevOps automation capabilities. With the top three benefits of increasing productivity and reducing manual effort (57%), improving collaboration between development, security and operations (56%) and enabling teams to generate code automatically (48%).
Maximize the value of your data
Bernd Greifeneder, Chief Technology Officer di Dynatrace
Data-driven automation is the key to unlocking innovation and meeting customer expectations in the cloud-native era. This requires a platform that can handle the enormous volume and variety of data generated by cloud-native stacks and use AI to provide accurate, actionable insights for DevOps automation.Traditional AI techniques are limited in terms of scope and applicability. While platforms that combine predictive, causal, and generative techniques can excel in specific capabilities to address different DevOps automation use cases. This way, teams maximize the value of their data, eliminate data silos, and can automate DevOps processes with confidence.
Research methodology
The report is based on a global survey of 450 IT professionals responsible for DevOps automation and security in large organizations. Of these 150 in the United States, 150 in the EMEA area and 150 in the Asia-Pacific area. The research was conducted by Coleman Parkes and commissioned by Dynatrace.