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Ross Ackerman
Ross Ackerman
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At NetApp, we focus on AIOps because it’s how we enable our customers to save time with their IT operations. Spending time running your business is much more valuable than spending time determining why some piece of technology isn’t working the way you want and then figuring out what to do about it. 

Why would an organization want to move to AIOps?

IT teams are drowning in lakes of network events, system events, alerts, and notifications because there’s an ever-increasing number of network and system checking and alerting tools. Nearly 25% of large enterprises have at least 8 monitoring tools installed, and some support as many as 25 tools, per a recent survey from Enterprise Management Associates. Multiple tools are often needed because individual tools are domain specific, supplying insight into issues within a focused area. Multiple tools are normally needed to provide a complete view. But multiple tools result in multiple unconnected alerts and events for IT support staff to track down and respond to.

What are the key benefits of AIOps?

AIOps is the analysis of alerts and events, with automated IT operational responses, to maintain the health, uptime, and performance of services and solutions. AIOps helps to find false-positive alerts, correlate ecosystem-wide notifications, and predict and prevent outages before they occur. AIOps capabilities are most often seen in the form of predictive analytics with automated proactive action. These capabilities help you identify and avoid issues before they happen to services or entire solutions, which increases IT efficiency and service uptime for the business.

What’s the best way to get started with AIOps?

The first step on the path to AIOps is making data available from the monitoring tools in your environment. Historical data and real-time data are both needed; they’re used to build predictive models that can understand and take proactive actions. Another important step toward AIOps is to document and programmatically capture the known corrective actions that can be taken to respond to disruptions or avoid them before they occur. If you can predict an outage or a disruption, having a known fail-safe method to respond is a great first step.

How difficult is the transition?

Beginning a journey into AIOps can be as simple as identifying a set of heuristics, or known rules, based on past events and automating a response for the future. A natural next step involves applying data science and predictive analytics to learning the event combinations and signals from your environment that indicate a pending issue.

How do AIOps initiatives typically get delayed or derailed?

A common challenge lies in managing event and log data and handling the scale and speed of events and notifications from an ecosystem. Many tools for app management, monitoring, and cloud-based support and analytics provide AIOps as a service or an add-on. Using these tools can be a natural first step into the world of AIOps and gaining IT efficiency.

AIOps for your IT operation, whether it’s with home-grown solutions or out-of-the-box offerings, will provide efficiencies for IT staff. These efficiencies include proactive alerting, automated disruption avoidance, and the ability to manage larger and more complex environments with less time.

At NetApp, we enable AIOps with our customers in mind. With the NetApp® Active IQ® Digital Advisor tool, we automate the analysis of more than 250 billion data points a day. Active IQ lets us detect, predict, and proactively prescribe actions and automation to address 98% of customer support issues. Customers tell us that by using Active IQ AIOps, they individually save at least 4 hours a week of administration time and over 228,000 hours of overall troubleshooting time per year.

To learn more, visit our Active IQ webpage.

Ross Ackerman

Ross Ackerman is currently the head of analytics and enablement of Active IQ at NetApp, where, as part of NetApp’s customer experience office, he leads a global team of data scientists, data engineers, and full-stack software developers building cutting-edge data analytics and AIOps tools to provide automated outcomes for enterprise IT. In this role, Ross leads teams across the company to use data, machine learning, and cross-team collaboration to solve business problems and improve NetApp’s overall customer experience.

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