5 Use Cases to Demonstrate the True Value of AIOps
Did your systems get simpler over the past year? Are you getting less operational data out of them? We all wish the answer to both were “yes,” but we know that’s not the case.
As the adoption of hybrid cloud environments and leading-edge technologies continues to grow, it’s getting even more challenging for IT operations teams to keep pace with the complexity and sheer volume of data that digital systems generate.
Today customers are looking to ingest millions of metrics per second from multiple tools. Without the power of AIOps, there would be no practical way to scale operations teams to effectively handle that volume of data.
The stakes continue to rise, too. A single hour of downtime can cost an organization more than $300,000, according to a vast majority (91%) of respondents to ITIC’s 2022 Global Server Hardware Security survey. Forty-four percent of respondents said a single hour of downtime costs them over $1 million.
No wonder AIOps is such a hot topic. Organizations that implement it right will free up skilled staffers to work on innovative projects, while AI/ML-powered software handles the increasing volume of metrics, events and logs while ensuring that the business continues to operate smoothly.
Like all enterprise software efforts, rolling out AIOps without a plan is not a recommended path for success. How do you succeed? Start with these five use cases:
How it works: AI-powered advanced anomaly detection finds outliers in the data, which helps to dynamically baseline services — the behavior of the system automatically sets thresholds for the generation of events.
Advanced anomaly detection typically involves multivariate algorithms and can adjust automatically for system behaviors that it learns over time. With the resulting insights, you can monitor your systems more intelligently with alerting thresholds automatically adapted to the normal behavioral characteristics of systems.
How it works: AIOps reduce the noise of myriad events across an environment. It breaks down data silos, ingesting data in the form of logs, events, traces and metrics.
Advanced AIOps technologies can correlate events along multiple dimensions of time, text and topology, helping eliminate noise such as duplicate and dependent events, and aggregating multiple underlying events into higher-level situations.
Root Cause Isolation
How it works: Understanding the root cause of an issue requires an accurate view of the relationships between different elements in your environment. Leveraging topology-enhanced, knowledge-graph-based AI/ML, root causes can be identified more accurately, thus reducing the time to detect the source of a problem.
By applying this type of advanced analysis to operational metrics across infrastructure and applications, AIOps can zero in on the true problem, saving IT teams time and energy that could be spent better elsewhere and reducing operational costs to the business.
Intelligent Automation and Remediation
How it works: While reducing event noise and finding the root cause of issues is valuable, ultimately it comes down to taking the remediation action to fix the problem. Modern AIOps solutions can support automated remediation actions to be taken in response to issues — ideally being able to integrate with a broad range of automation platforms and tools.
As operations teams become comfortable with automations based on historical success of remediation, they can define policies so those actions are automatically taken based on the root cause detected. Over time, AIOps can learn how successful automation has been in different situations to proactively recommend automation opportunities.
How it works: Ideally, AIOps can take IT operations to the next level by looking ahead to predict potential issues and take corrective actions before they happen. This includes identifying resource saturation and capacity limit situations by projecting organic growth of a system and learning from past behavior. Operations teams can then identify actions including provisioning additional capacity or resources before it becomes a problem.
AIOps systems can also look at historical patterns in data and identify where a system failure or degradation in performance is expected to happen. This type of real-time predictive alerting saves IT from potentially reacting to a problem and instead enables the business to prevent service outages from happening at all.
Businesses cannot deliver digital experiences on the frontend without also putting the right tools in place to digitally transform the backend. AIOps will enable IT operations to support increasingly digital businesses by intelligently analyzing large volumes of data, learning system behaviors and automatically recommending actions both proactively to prevent system failures as well as in response to rapid root cause isolation of issues that could not be prevented.
Focusing on these five use cases will enable organizations to embrace new application architectures and increasingly complex, hybrid ecosystems while ensuring that IT operations keep pace with the needs of the business and evolving customer demands.