Greg Adams, regional vice-president UK&I at Dynatrace, discusses the key role that automaton can play in multi-cloud infrastructure monitoring
To keep pace with digital transformation, organisations from all industries have increasingly turned to multi-cloud architecture to gain the agility and scalability they need to stay ahead. IDC forecasts that as this trend continues, total worldwide spending on cloud services will surpass $1.3 trillion by 2025.
However, each cloud that’s added makes the task of managing infrastructure more complex, as technology environments span a greater number of platforms. In turn, this creates more work for overstretched IT operations (ITOps) teams, which prevents them from focusing on innovation. Indeed, our research found ITOps teams spend almost half (42%) of their time on manual, routine work ‘just to keep the lights on’ across their infrastructure.
These teams clearly need a more sustainable approach to managing their infrastructure – one that improves observability in multi-cloud environments and automates manual tasks, so they have more time to focus on driving innovation and creating value for their organisations.
Why businesses should embrace multi-cloud
A multi-cloud migraine
If they can’t manage infrastructure performance effectively, ITOps teams will massively struggle to deliver the seamless digital experiences today’s customers and users demand. It’s critical they have clear, end-to-end observability across their multicloud environment. Unfortunately, this insight has become more elusive, and blind spots are creeping in as teams struggle to keep up with their infrastructure.
Multi-cloud environments are by their nature complex and challenging to manage with many existing approaches to infrastructure monitoring. There are several factors behind this.
First, each cloud platform comes with its own native monitoring tool, such as Amazon CloudWatch or Azure Monitor. ITOps teams have therefore gradually found themselves with a growing range of tools they need to layer on top of traditional monitoring solutions to track activity across infrastructure. Our research indicates that on average, organisations rely on seven different monitoring solutions to manage their multi-cloud environments. This forces teams to spend more time manually piecing together insights from various dashboards to identify issues in their digital services, as user journeys traverse multiple clouds.
The Kubernetes conundrum
Another factor making observability more elusive is the frequency of change within multi-cloud environments. While platforms such as Kubernetes enable organisations to scale their multi-cloud infrastructure rapidly to match demand, the constant change makes it difficult for teams to monitor and manage performance effectively. Kubernetes environments also produce large volumes of data, which is impossible for ITOps teams to sift through manually to understand the impact of multi-cloud infrastructure on user experience.
Adding further complexity, in their efforts to alleviate ‘tool sprawl’, teams often adopt a do-it-yourself (DIY) approach to infrastructure monitoring, using open-source observability solutions to stitch together multiple tools. This generates wasted manual effort and is difficult to maintain, which impedes digital transformation, as teams have less time to focus on their more strategic work.
Establishing a strong network monitoring strategy
Automating a course ahead
To overcome these challenges, organisations need to empower IT teams with a new approach to infrastructure monitoring, harnessing AIOps (artificial intelligence for IT operations) to automate as many manual tasks as possible. This eliminates blind spots by continuously discovering and instrumenting multi-cloud infrastructure as the environment changes. As a result, teams can maintain end-to-end observability without needing to invest time and effort in manual monitoring processes.
AIOps also helps automatically triage alerts and query observability data to surface the precise insights that teams need to deliver seamless digital experiences to users and customers. With this approach, AIOps can enable teams to understand the cause of any issues across a multi-cloud infrastructure, and prioritise issues by business impact. This means teams can solve the most critical issues first and then focus their effort on tasks that accelerate an organisation’s digital transformation. However, this is only possible if teams can consolidate observability data in one place. A consolidated view creates a single source of truth that provides the full context needed to drive effective automation.
More time for innovation
As organisations continue their transition to multi-cloud environments, ensuring that infrastructure is running smoothly is becoming more critical to creating seamless digital services and driving customer satisfaction. Implementing infrastructure monitoring strategies that centre on AI and automation reduces the burden of manual tasks on ITOps teams. In turn, teams can focus on accelerating transformation and driving better outcomes for the business.