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What is AIOps

What is AIOps?

According to Gartner, Artificial Intelligence for IT operations (AIOps) is a broad category that comprises the use of big data, machine learning, and analytic insights to automate and accelerate the identification and resolution of IT issues. Today, the systems and applications within organizations generate massive volumes of data—with some organizations experiencing millions of events per day. At this scale, it is no longer viable for humans to manually parse through all that data to detect and remediate issues. The cognitive load is worsened by the fact that organizations often have dozens of tools monitoring thousands of services—any one event that emanates from these tools may be meaningless on its own. Such phenomena have created mission-critical needs for automation, machine learning, and predictive capabilities.

Essentially, AIOps solutions provides similar functionality to existing event management solutions, but add capabilities required for complex, modern environments such as machine learning, flexible data collection and ingestion, powerful visualizations, and more.

How does AiOps work?

AIOps works by essentially bringing together data from a variety of sources across an environment and consolidating it while keeping the data intact and consumable. It collects items such as:

  • Historical data
  • Logs and metrics
  • Incident and document-based data
  • Network and packet data

And that’s not all. It then separates significant events, called signals, from non-impactful data, called noise. This is important because it removes the need for manual sifting of events and incidents, and hyper focused only on what’s important and requires attention. This automated process identifies the cause of incidents and proposes solutions, in some cases even kicking on real-time resolutions to problems.Whereas manual tracking would take a great deal of time and manpower, AIOps automates the process and is able to not only track incidents, but find the root cause, fix the problem (or suggest how to), and predict similar future issues. Its efficacy is found in its ability to learn and problem solve for itself, taking the human component out of the most tedious elements of the process. It can reach across a variety of data sources quickly, efficiently and reliably, and deliver information and data that teams can count on.

AiOps Key capabilities

As you can see, AIOps can have quite the impact on IT teams everywhere, and it already is where it’s been successfully implemented. Some of the key capabilities of AIOps are as follows:

  • Advanced event automation to route events to the right services and teams
  • Intelligent noise reduction that automatically clusters events across different systems
  • Proactive detection of serious issues to identify causal relationships and support root cause analysis, as well as preventative remediation
  • Incident context enrichment with notes, runbooks, historical remediation details, and more

In essence, it detects, informs, resolves, and then prevents. It does the work of an entire team, just by itself. As you can see, it has a wide range of benefits. Let’s delve into some of them more specifically. 

Benefits of AiOps

The most important benefit of AIOps is its ability to save time, energy and manpower by resolving issues and outages at a much faster rate than is possible manually. While AIOps platforms typically require time to implement and train, they can help technical staff spend far less time manually eyeballing or taking action on redundant issues and alerts.

Here are some other key benefits to keep in mind:

  • Does the heavy lifting for IT teams. AIOps platforms integrate with monitoring systems or with the endpoint directly, so that they can proactively detect issues around the clock, correlating and clustering related issues across systems into objects that are far easier to triage and which are much more manageable by humans. This empowers IT staff to spend more of their time on mission-critical, business-differentiating work instead of mundane tasks. Another goal of AIOps is to accelerate root cause analysis and remediation, which is growing increasingly crucial given the rise of highly complex, unpredictable black swan disruptions in IT.
  • Speeds up analysis and remediation. This is growing increasingly crucial given the rise of highly complex, unpredictable black swan disruptions in IT.
  • Provides faster mean time to resolution (MTTR). This drastically improves incident response and gets resolution to end users more quickly.
  • Predicts issues before they happen. Because it can learn and adapt so quickly, it can not only detect issues but also figure out what caused them and how they can be prevented in the future.

AiOps Challenges

With this rich potential for data science to improve your operational efficiency, your team happiness, and your organizational communication — you have to make the choice to build or buy a solution. How do you mitigate risks as well as costs in your AIOps deployment?

Building a solution is tempting — but be wary of the promise of a custom-built solution. Data science is an evolving field — what you design for your system now is almost guaranteed to be out of date by the time you deploy it. The right system requires research and experimentation that can take a while and eat up your resources in implementation and training. And if it is not being powered by enough data, it will not yield accurate insights.

Some challenges to consider when implementing AiOps:

  • Amount of data. For this to be successful, you need enough data to be read, scanned and analyzed. If there isn’t enough data, there won’t be a successful outcome.
  • Bad quality data. Similarly, if the data isn’t good quality, machine learning can only go so far. 
  • Too much human interaction. To do its thing, AiOps has to be given the room to analyze and learn. Too much human interaction can gum up the works, so to speak, and harm the process. 
  • Improper integrations. All legacy tools have to be modern enough that they integrate successfully with AiOps, otherwise data won’t be able to be read and used. 

Consider buying a solution that fits into your current infrastructure and operational strategy — that is purpose-built based on a long history of data and experience, designed to follow best practices and adapt to your unique environment and needs. The sooner you can get something usable and working, the sooner any data science solution will be able to start learning and adapting, gathering data and delivering value.

AiOps Use Cases

AIOps isn’t just good for software development. It’s also useful in many different organizational structures, and for a variety of reasons. 

  • Going digital. AIOps is a great tool for completing an organization’s digital transformation according to its strategic business plan.
  • Using the cloud. AIOps provides a level of transparency that reduces the risks associated with the hybrid cloud approach, or with migration. 
  • DevOps. AIOps automates the DevOps process and thus reduces the need for management on the IT end. 
  • Business health. With AIOps, you can get a more holistic look at the company’s health across all areas of IT and business services, rather than relying on piecemeal data.

Ensure AIOps adoption and success

A data science solution is only as good as the data going into it, and that depends on how closely integrated the solution is to your operations as well as consistent and engaged use by your teams. A solution that has access to the full breadth of your infrastructure data will perform better than an overly-specialized tool. Look at user experience, implementation complexity, the integration catalog, and the system’s training methods to ensure your employees will successfully adopt and use the solution.

Be aware of systems that rely on extensive configuration — these often require constant management and tending. Look for solutions that will bootstrap the solution from the data you have versus start from scratch or rely on your team to define what they actually do. People make mistakes and systems change too fast to rely on a set of rules in order to understand which symptoms are correlated to other symptoms. The nature of any problem is that it’s probably too new to have a rule written about it.

How to get the most out of AIOps

AIOps is only as good as the algorithms it is taught and, more importantly, the amount and richness of the data on which it is trained. Implementing, training, and managing the solution requires very significant investments, and when deployed in-house, often takes months or even years before yielding accurate insights. Furthermore, because incident response typically takes place in other tools, existing AIOps solutions lack human response patterns and can’t surface critical context such as how teams solved related issues in the past.

PagerDuty Event Intelligence is a new approach to event management and AIOps that meets the needs of modern, agile teams. PagerDuty is the only platform that gets you maximum value out of both your system and people data, and automatically learns and adapts to changing infrastructure—so you and your team can work smarter, not harder. Try it out now for yourself with a free 14-day trial.

We detail the specifics and customer benefits of the Event Intelligence approach in this free eBook: Next-Gen Event Management and AIOps for Any Team.

We hope these resources outline helpful best practices and strategies you can take away to immediately gain value from machine learning-driven correlation and insights.