In our previous blog post, we discussed how investments in AIOps platforms have been justified on the basis of their ability to decrease mean time to problem resolution and the resultant cost reduction. But one of the challenges most IT Ops teams face is in the approach of deploying AIOps solutions for business-critical use cases: Where do we start? Some use AIOps to analyze unstructured data in order to identify higher-level correlations that traditional IT monitoring tools wouldn’t be capable of. Others end up doing data manipulation or aggregation by setting up models of data ingestion without thinking about the business first. In the 2019 Gartner Market Guide for AIOps Platforms, Gartner recommends taking an incremental approach to AIOps: “When adopting AIOps platforms, start with less-critical applications and apply the following: event categorization, correction, anomaly detection. Ensure that your use cases drive action to improve business outcomes and that the result of AIOps platform output is either a manual next step or the launching of a script or run book to improve the current state.”
“By 2023, 40% of DevOps teams will augment application and infrastructure monitoring tools with artificial intelligence for IT operations (AIOps) platform capabilities.” -2019 Gartner Market Guide for AIOps Platforms
An Incremental Approach to AIOps Adoption
AIOps solutions can access both historical and real-time data. The real differentiation that these tools bring is the ability to continuously learn and optimize function based on real-time data. For instance, if a monitoring tool alerts that the increased CPU usage is due to an increased number of connections, Kubernetes can spin up additional app instances and use load balancing to distribute the users and reduce the load. AIOps capabilities allow you to automate routine DevOps use cases, enabling machine learning models to trigger under certain predefined conditions and identify issues preemptively. The use cases to which AIOps platforms can be applied will depend on their scopes. Some may require more data than would be optimal, and others may require more data science skills than may be available.
Need More Visibility
The success of AIOps or any other automation method relies on the quality of the data. Modern IT operations require visibility across IT entities — breaking down silos, including applications, their relationships, interdependencies and past transformations, to gain insight into the present state of the IT landscape. But AIOps tools by themselves are not service-centric and have no concept of real-time models. They are not designed to tell the user how issues within a given system may affect IT services or applications. Zenoss combines the probabilistic AIOps event correlations with root-cause analysis informed by definitive service-dependency models, eliminating guesswork when investigating IT incidents. It gives you full visibility into IT service relationships and dependencies. For example, Zenoss can augment insufficient log and event data used for AIOps correlation with rich metrics from every system constituting every IT service.
“By 2023, 40% of I&O teams will use AI-augmented automation in large enterprises, resulting in higher IT productivity.” -2019 Gartner Market Guide for AIOps Platforms
The progressive nature of deployment maturity and evolving use cases requires a readiness to ingest a variety of data sources. IT leaders should select AIOps platforms that are capable of ingesting and providing access to a broad range of historical and streaming data types in support of business-critical use cases. Whether you are in the midst of creating a completely new environment for your IT department or reevaluating monitoring tools and the potential of hosted IT services, you need an AIOps solutions like Zenoss, purposefully built for taming the most complex, modern IT environments. To learn more about AIOps, download the 2019 Gartner Market Guide for AIOps Platforms.
