More data for data’s sake doesn’t help anyone. What organizations need is more information–actionable insight. With data coming from incoming streams of events and alerts, teams don’t have enough time to look at each one. And they struggle to parse and consolidate this data in order to figure out what they need to do next to resolve an incident. Processing this data to make it more usable and helpful during incident response often results in a rote series of manual, repetitive tasks each time an incident occurs, wasting time. It’s no wonder teams are increasingly turning to AIOps and automation for help. AIOps helps teams turn data into information and reduce that manual work. Let’s break down three ways AIOps allows teams to overcome challenges and reduce customer disruption.

Reducing noise for fewer incidents

Not every alert should become an incident. Yet for many organizations, this is what happens. Even if you’re only experiencing one problem, you may receive dozens or hundreds of pings for the same issue. This is distracting and bogs responders down. Noise should be your first thing to focus on because eliminating it:

• Gives responders back time when they don’t need to filter out what’s important from what’s irrelevant.
• Decreases the cognitive load that responders carry. Responders don’t need to think about 63 different alerts. They can focus on the one that matters. This reduces this on-call anxiety.
• Reduces the distractions that get in a responder’s way during an incident. This helps responders focus on getting a fix in place faster.

To reduce noise, you can analyze the noisiest incidents you’re facing. Which ones are the same incident? Take a look at the alerts you’re receiving and see if there’s a way to group them based on event data that you gather from your monitoring tools. What’s loudest? This is an opportunity to fine tune your monitoring tools so they’re only sending you what’s most valuable. Keep in mind that this often requires routine maintenance. Monitoring tools become messy, especially when data is scattered across vendors. You’ll want to gut check this whenever you notice noise levels are increasing.

PagerDuty AIOps makes it easier to reduce alert noise within a single tool. Users can set PagerDuty to ingest and deduplicate events from those disparate signals. Then PagerDuty AIOps groups the events into an existing incident. This suppresses a new incident from being created. Teams have access to event data in the form of alerts without extra notifications. The result is that teams can better weather alert storms by bringing focus to what’s needed. 

Gaining context for better triage

Technically, all the information a responder needs to resolve an incident exists. But, it’s buried within multiple disparate streams of data. Humans alone cannot condense all this data into succinct actionable insights. This means teams spend a long time looking for answers to questions that they can leverage machine learning (ML) to find instead. ML can look at both historical event data and human interaction. Then ML translates the analyzed data into actionable insights. With ML, teams can answer key questions such as:

• Where should my team look first?
• Are other teams working on the same problem?
• Is this a common incident or completely new?
• Have we seen this before; how was it resolved?
• Any relevant changes occur before this incident?

But developing your own ML can be a daunting task. It requires time and resources such as headcount. Many organizations choose to partner with a vendor instead.

PagerDuty AIOps ML algorithms help surface critical information such as:

• Probable Origin: determines probable cause based on previous incidents affecting your service.
• Related Incidents: shares if a current incident is affecting your service.
• Outlier Incidents: whether this incident happens frequently, rarely, or is a total anomaly.
• Past Incidents: look at the incident details and see how responders resolved it in the past.
• Change Correlation: connects with your change integrations to show changes to your service, then leverages ML to correlate patterns between change events and incidents.

Each time this information is surfaced for your team without having to manually dig, you get to resolve the incident faster. That decreased MTTR provides you with more time to focus on value-add initiatives.

Self-healing by crafting auto-remediation

One initiative you can focus on to spend less time firefighting is automation. This is where you can orchestrate a fix and self-heal before the problem even becomes an incident. It’s resolved before it hits a responder. Now someone gets to sleep through the night instead of responding to a notification. But this initiative can seem very intimidating. The reality is that starting small and tackling low-hanging fruit can make self-healing easier than you may expect.

You can identify well-understood resolution scenarios where you can automate the response. These may be scenarios that your team would classify as frequent, or ones where the resolution is straight-forward. Teams can then create automation to resolve these without human intervention. Then, as that automation starts to take effect, your teams will start to free up time to work on new automation initiatives.

PagerDuty’s Event Orchestration  helps teams create automation that spans the entire technical ecosystem. Event Orchestration enriches and routes events, then kicks off automation to self-heal. This feature allows users to trigger remediations for well understood incidents via webhook. For more complex issues where auto-remediation might not be a possibility, teams can also leverage automation to kick off diagnostics. This builds upon the triage information responders have when they first view their incident.

Looking to get started with AIOps?

AIOps can help teams see fewer incidents and faster resolution. PagerDuty can help you achieve this, and more, with PagerDuty AIOps. See PagerDuty AIOps in action by requesting a trial or taking our product tour. In the market for AIOps? Read our buyer’s guide.