FAQ: PagerDuty on How to Make AIOps More Efficient
Against a backdrop of recession concerns, we’ve heard many organizations using the phrase “do more with less.” Hiring freezes, headcount reductions and belt tightening has meant IT and development teams are under even more pressure to complete work, but with the same or fewer resources. To accomplish this, we need to get more efficient in our jobs.
One way to improve efficiency is by using AIOps to help automate and accelerate the identification and resolution of IT issues. AIOps is central to improving reliability, minimizing toil, reducing noise and helping to optimize ITOps and site reliability engineering (SRE) teams.
Q: What Kind of Challenges Do ITOps and SRE Teams Face Today?
Data volumes are on the rise, passing the burden of noise and false alarms onto ITOps and SRE teams. Data from PagerDuty found that event data is now growing at nearly 70% year over year. This rate of growth is outpacing technical teams’ ability to manually process this event data.
Tech stack complexity is also increasing. Today’s digital services rely on hundreds or thousands of interdependencies. This complexity makes it much harder to diagnose problems and ultimately resolve them. Ongoing changes to these services and complexity of systems increase the risk of downtime. These costs represent more than just lost revenue — there is also an innovation trade-off to contend with, as well as loss of customer trust.
In the past, the natural way to solve the problem of more incidents was to hire more people to both fight fires and to create long-term fixes so that incidents are less frequent. But it’s no longer feasible to add more people to the mix due to tightening purse strings. Teams end up being caught between a rock and a hard place.
Q: Are Organizations Effectively Leveraging Their Monitoring Data?
Organizations have more data than ever but less information to make the right decisions. ITOps and SRE teams lack a way to drive consistency across the business, and have limited visibility into incidents that cascade across technical ecosystems. In a study published by Information and Software Technology, 90% of responders identified the lack of standardization and the over-proliferation of monitoring solutions as clear problems leading to monitoring mishaps and misuse.
Disparate monitoring and observability tools mean that data is dispersed across teams and services. This makes it extremely difficult to consolidate data and drive decision-making during the moments that matter most: resolving incidents that affect customers. As such, the root cause can often be buried under layers of data that has to be manually parsed by ITOps and SRE teams. When every second counts, this is hardly the ideal scenario.
Q: What Is AIOps?
AIOps is a valuable approach that allows organizations to use machine learning to correlate data into actionable insights and automate incident response, diagnosis and resolution.
AIOps helps to overcome the deluge of data by consolidating it across increasingly complex environments to help drive rapid, data-led decision-making. This data could include machine data like logs, metrics and network/packet data, as well as human data like response actions during similar past incidents. Individually, these data sets are difficult to derive meaning from. But AIOps brings it all together to provide context and drive actions. Such an action could be anything from advanced event automation to route events to the right people, to proactive detection of issues and even auto-remediation.
It’s perhaps no surprise that customer demand for AIOps solutions continues to grow. A recent study shows that 90% of organizations expect to spend as much or more on AI and machine learning in 2023 as they did in 2022.
Q: How Can AIOps Reduce Noise and Speed up Resolution?
With events growing at an unprecedented rate, teams risk being overwhelmed by a flood of alerts, making it impossible to focus on what is important. This results in longer mean time to resolution (MTTR).
Teams need a way to cut through the noise. By using machine learning to automatically group events from different tools and systems during an incident, teams are better able to focus on the job at hand and figure out what’s important and how to address it.
AIOps can help with resolution too. Previously, teams were bogged down in data, trying to identify the root cause and how to fix it. But AIOps harnesses machine learning to only surface the most important, actionable information for respondents. AIOps identifies where teams should look first for probable root cause, any relevant changes that occurred before the incidents and other potentially related incidents that teams are working on. Better still, AIOps can determine if an incident is new or similar to a previous incident and provide historical details for how it was resolved. Finally, AIOps can “act” and make informed changes to close and resolve the issues, which when you think about it, is the most important part.
With AIOps, when an incident occurs, teams can quickly discover the cause and work on a resolution instead of dealing with noise and having to manually sift through alerts, logs and other data to figure it out themselves.
Q: How Can AIOps Help Teams Create End-to-End Automation?
The incident response life cycle consists of many manual tasks. These tasks occur for nearly every incident and are commonly things that machines could accomplish without human intervention. The time it takes to deal with these manual processes can feel like death by a thousand papercuts. Each action could only take minutes, but when they occur across every incident, and sometimes for each service, the effect on the organization and customers can be sizeable.
AIOps helps organizations eliminate toil for responders by standardizing and scaling automation across the organization. With AIOps, teams can set up automated incident response processes that can act as a force multiplier across an entire ecosystem. These processes could include:
- Automated incident workflows that can mobilize a response, add responders, spin up collaboration tools and escalate as needed to subject-matter experts.
- Automated diagnostics that can trigger basic diagnosis actions such as checking the memory health of a CPU and gathering error logs.
- Enable human-in-the-loop automation for proactively providing business stakeholders and customers with real-time status updates.
- Automatically enrich and normalize event data and intelligently route it to the right team, no matter how complex the conditions are.
Automating incident response processes end-to-end enables ITOps and SRE teams to reduce toil and eliminate costly manual processes across all teams and services for improved efficiency.
Doing More with Less
With ITOps and SRE teams often acting as first responders for incidents, it’s vital that they have access to context and systemwide visibility. Without these insights, teams cannot make informed decisions and take the next best action. This can have a major impact. It increases the cost of operations, reduces productivity and takes technical teams away from innovative value-add work.
In a resource-constrained world, teams can’t wait for monthslong implementations. They need help today. AIOps provides teams with a solution that has fast time-to-value and provides rapid return on investment by automating and accelerating the identification and resolution of incidents.
Not only does AIOps help reduce toil and noise, it optimizes ITOps and SRE teams to truly do more with less at a time when they and their organizations need it most.