In 2017, McAfee found that an average enterprise uses 464 custom applications. A large enterprise — a company with over 50,000 employees — uses 788 custom apps! The more applications you have, the more complex your application environment is. This means that you are more susceptible to outages.
So, the tolerance for downtime is impossibly low. Mission-critical applications must be available at all times.
In 2021 the enterprise landscape has changed dramatically and become more complex – as a result, outages are commonplace. To manage the complex technology landscape, predict and prevent outages before even they occur, enterprises need a robust AIOps tool. In this blog post, we discuss what constitutes a good AIOps tool. We identify the eight key capabilities your AIOps tool needs to have.
#1 Complex data processing
You might have a wide range of applications, cloud environments, networks, and so on. All of them would have metric data, logs, topology, alerts, and workload data. A good AIOps tool should have the capability to gather, process, and glean insights from data across these sources.
#2 Eliminating false positives
Most AIOps tools are set up to be safe. So, they identify every slight deviation from normal as an anomaly. This results in alert storms, inundating IT ops teams with notifications. However, a vast majority of these alerts don’t turn out to be incidents at all. A good AIOps tool should be able to tell the difference. It needs to have the capability to identify false positives and suppress them proactively.
#3 Workload correlation
Dynamic thresholding and proactive alerting work great for system metrics in isolation. But system behavior is a direct function of the workload. For instance, during a Black Friday sale, e-commerce systems will process far more workload than average.
So, your AIOps tool needs to be intelligent enough to understand the expected/natural increase in workload and react accordingly. It needs to have situational awareness to understand operating context, including seasonality, contending transactions, user journeys, etc.
#4 Root-cause analysis
Most AIOps tools stop at identifying anomalies and raising alerts. Once the ITOps teams see the alerts, they spend hours manually correlating multiple data points to determine the root cause of the problem. This is not only a waste of time but also ultimately unscalable.
A robust AIOps tool needs to have the capability to extract forensic or diagnostic data based on the nature of anomalous KPIs, log data, code snapshots are taken via instrumentation, and database deep-dive to identify root-cause automatically. It also must learn from past events and predict the recurrence of problems before they occur.
#5 Dynamic baselining
As the business grows, applications mature, and users interact more often, the CPU/memory utilization, etc., evolves over time. Therefore, your AIOps tools must have the capability to create dynamic baselines that adapt to changing workloads.
#6 Preventive healing
ITOps have traditionally set goals like mean-time-to-resolve. This is a post-facto approach to IT incidents, focusing on troubleshooting and resolution instead of prevention.
Good AIOps should do the opposite. It must have advanced predictive capabilities to forecast potential issues. It must set goals for the number of issues averted. It should raise proactive alerts by applying dynamic thresholds learned via AI/ML techniques. It must effectively predict as well as prevent an issue from occurring.
#7 Product evolution
The enterprise application landscape is evolving rapidly, not only adding more tools but also complex tools implemented in sophisticated cloud platforms. Your AIOps tool must effortlessly scale with your needs. It must be easy to add new products, monitoring tools, data sources, etc., without additional implementation efforts.
As enterprise application landscape becomes more and more complex, it will become nearly impossible to analyze every anomaly, address every alert, identify root-cause, and resolve them. IT teams need robust, intelligent, and automated solutions that can predict, prevent. and autonomously resolve issues. To do that, your AIOps tool needs to go beyond today’s needs and be a sustainable and evolving solution.