Organizations increasingly adopting multi-cloud architectures face challenges related to agility and scalability. A study commissioned by Dynatrace shows this, and one of the results is that IT teams spend almost half their timekeeping systems.
According to Dynatrace, multi-cloud strategies have led to an increase in complexity. Monitoring and ever-changing managing environments often provide infrastructure teams with too much data. So they would have to spend a lot of time on routine manual tasks – time that is missing to accelerate innovation. This would underscore the need for increased use of AI and automation.
Multi-cloud strategies are critical to keeping up with the rapid pace of digital transformation but teams are struggling to manage the complexities these environments bring, said Bernd Greifeneder, Founder and Chief Technology Officer at Dynatrace. Dependencies are growing exponentially, driven by faster deployment cadence and cloud-native architectures that drive constant change. Open-source technologies complicate this by providing teams with even more data.
Each cloud service or cloud platform has its monitoring solution to make matters worse. Teams must manually extract insights from each key and merge them with data from other dashboards for a complete picture. Companies should support their teams to reduce the time spent on manual tasks, and then they can refocus on strategic functions that deliver new, high-quality services to customers.”
Infrastructure teams need AI-driven solutions that automate as many manual routine tasks as possible,” continues Greifeneder. With automatic, continuous discovery and instrumentation, teams can reduce manual effort while maintaining end-to-end observability across their hybrid, multi-cloud environments. However, observability alone is not enough. You also need access to precise answers to help teams effectively optimize their settings.
Conventional approaches cannot keep up here as they rely heavily on manual processes. Businesses need a more innovative approach that combines AI, automation, and end-to-end observability, giving teams more time to focus on accelerating innovation and optimizing usage.
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