Here's why AIOps, which combines big data, machine learning and visualization to deliver greater insights, should be on every enterprise's shopping list.

There is a lot of industry buzz around how AIOps will affect change within IT Operations (ITOps). According to Gartner, Inc., the term “AIOps” describes platforms that combine big data and machine learning to support ITOps. This means that the problems being solved aren’t novel, the approach is. In ITOps or any other business unit, there are two primary constraints: time and money.

We’re proud to launch BigPanda’s Future of Monitoring, IT Operations and AIOps survey – a comprehensive, in-depth look at the current state of monitoring and IT Operations, and where the industry is headed.

If you’re part of an IT Ops or NOC team, or if you manage one, you know that overwhelming IT noise is your #1 enemy. Not only does it flood your teams with false-positives, but it also buries critical root-cause events and it makes it hard to proactively detect expensive P1 and P0 outages.

By augmenting operations teams, AIOps enables organizations to preemptively ensure that applications, architectures and infrastructures are ready for rapid digital transformation.

Built-in AI/ML—such as in AppDynamics APM—delivers value by activating the cognitive engine of AIOps to address anomalies.

Proactive Incident Analysis, Diagnosis, and Resolution with Service-Centric AIOps. Alerts define the state of an infrastructure resource, application, or any other IP discoverable device. Organizations take action on alerts based on business impact and priority and ensure that IT service performance meets the required standards for availability, usability, and security.

Quantifying the value of successful AIOps deployment requires tracking subsidiary metrics within the industry default of mean time to resolution (MTTR). This post breaks out the metrics that form MTTR and divides them into two categories: problem and solution.