[3-minute reading time…]
If your IT support organization is like most, it’s probably stretched to the limits – and beyond. There’s just too much to do and not enough time. Incident backlogs pile up, and there’s the never-ending pressure to do more with less. Digital transformation is making things even worse – with applications and IT infrastructure growing exponentially as companies use technology to transform the way they work.
Let’s look at why L1 and L2 support teams struggle today.
Too Much Noise
For L1 teams, it’s because they’re drowning in noise. Monitoring systems produce a never-ending stream of events, the vast majority of which are either irrelevant, duplicates or secondary symptoms. It’s like drinking from a fire hose. These events have to be triaged, and then turned into incidents when there’s a real issue. In fact, the event volumes are so high that many are ignored – even when they are serious. Often, L1 teams only find out about service issues when users start to scream. That just increases the chaos.
Lack of Business and Service Context
And, when L1 teams actually find time to work on incidents, they don’t have the business context they need to prioritize them – or the clear diagnostic information needed to resolve them. For example, is a server issue affecting the company’s e-commerce website, or is it just marginally affecting the compute capacity of a cluster? Similarly, is that slow application performance due to the application itself, or because of a network, database or storage issue?
Too Many Escalations
What’s the result? L1 teams have limited technical skills, so they end up escalating large numbers of incidents to L2 support. That creates a domino effect. L2 teams end up working on incidents that could have been resolved at L1 – if only the L1 team had the right information. And, when there is a complex service issue, the L2 team faces the same lack of contextual and diagnostic information. It takes hours or even days to hunt down the root cause, manually stitching together disconnected data from multiple monitoring systems. Rather than restoring service quickly, domain experts end up in unproductive war room finger-pointing exercises.
What’s the Answer?
With a modern, best-in-class AIOps platform, you can turbocharge your L1 and L2 support teams. Here’s why:
- Noise Reduction – AIOps dramatically reduces event noise, creating a clean signal of relevant events. It normalizes, filters, deduplicates and correlates data across multiple monitoring systems, reducing millions of events to a few thousand meaningful alerts. Instead of drowning in noise, L1 teams now have a manageable set of incidents to handle.
- Business Context – AIOps platforms enrich these signals with relevant business context, such as the business impact of similar previous events. By drawing information from ITSM systems, it simplifies the task of triaging and prioritizing events, further increasing the accuracy and responsiveness of L1 teams. That means they address service issues more quickly, and focus on the right things.
- Root Cause Analysis – AIOps helps in identification of why a service has failed, instead of just reporting a mass of symptoms. This means that L1 teams can resolve more incidents by themselves, reducing the burden on L2 support. And, when L2 does get involved in complex issues, they have a clear indication of what is wrong, along with comprehensive diagnostic information that makes it much easier to restore service. It’s a win-win – no more war room finger-pointing, and radically better service quality.
- Predictive Analysis – An AIOps system can predict service issues. Using machine learning and big data, it identifies event patterns that typically lead to future outages. Then, when it sees the same pattern, it alerts your support team, giving them advance warning. That means that they can take proactive steps to avoid the outage – fixing the issue before the business is affected.
The Impact of Digital Transformation
We’ve already talked about digital transformation. It’s the reason why an AIOps platform is an absolute necessity, not a nice to have. As enterprises deploy more and more applications and underlying IT infrastructure, the support problem is only going to get worse. Event volumes are going to continue to increase, and we can’t just increase the number of L1 and L2 support staff linearly to cope. It’s not economical, and it’s not scalable.
An AIOps platform provides an immediate increase in support productivity – but it does more than that. By applying machine intelligence to areas such as prioritization, predictive analytics and root cause analysis, it provides a long-term, sustainable way of increasing support capacity. And, by automating tasks such as provisioning monitoring systems and triggering remediation actions, it eliminates human error and makes it much easier to launch and support new services.
The Bottom Line
Today, many IT support organizations struggle to deliver high-quality services to their business. There’s too much noise, not enough insight, and too many time-consuming manual processes. Digitalization is set to make the problem worse, as applications multiply and IT infrastructure becomes more complex.
An AIOps platform is a key part of the solution. By squelching event noise, automatically identifying the root cause of service issues, providing relevant business context, and predicting future service outages, it dramatically increases service quality, increases support productivity – and creates a sustainable foundation for the digital future.