IT today can be summed up in three words: complex, decentralized, and slow. And the exponential increase in IT complexity brought about by operating from “anywhere” boils down to one thing—the ability to manage complexity requires technology capable of handling complexity.
This is bad news for IT departments still grappling with outdated IT.
IT leaders need tools to manage increasing complexity.
Predicting a slow-forming anomaly before an outage happens—or simply determining the cause of an outage—can take hours, even days, using traditional, siloed IT tools. And when an outage hits, it takes even longer to identify the impacted services and remediate issues.
The implications are serious. Consider the massive internet outage that hit the East Coast in January of this year. At a time when everyone is logging on to get work done, an event of this scale wreaks havoc from the classroom to the boardroom.
To predict is to prepare
Whether it’s a garden variety outage or a global pandemic, IT needs to act now to prepare for the next big event. And the best way to prepare is to predict—which is where predictive AIOps comes in.
AIOps is about applying machine learning techniques to IT operations functions. This allows IT to correlate current and historical data with speed that operators can’t realistically achieve using traditional manual processes and siloed tools. Predictive AIOps allows operations teams to see slow-forming anomalies before they become outages and fix the problem before users are ever impacted.
Put simply, predictive AIOps makes it possible to stop outages before they start.
Now that I have your attention, let’s look at how your organization can make it happen.
The devil’s in the data
Predictive AIOps without historical data is like Bruce Lee fighting blindfolded.
To deliver insights at speed, it draws upon historical incident and change data. It then correlates this data with relevant knowledge base articles in real time.
The key is to bring together IT service management (ITSM), IT operations management (ITOM), and predictive AIOps on a single platform. This allows AIOps to grab data it needs and present it to the operations team, giving them the information they need to understand the root cause of an emerging anomaly or outage and fix it in near real time.
This end result is AI-powered IT service operations, which leaves the manual process of trying to correlate historical data from siloed, independent tools in the dust.
An AI-enabled solution like ServiceNow’s Predictive AIOps can rapidly identify the suggested remediation by mining knowledge base articles and using natural language processing (NLP). When it has processed enough historical data on past successful remediations, it can trigger remediation workflows automatically. This power of prediction frees operations teams to address other priority issues.
[Want to learn more? Check out this webinar on the power of predictive AIOps.]
Happier end users
Now you know how predictive AIOps takes care of known issues before they impact users. But can it also predict blind-spot issues that the operations team didn’t even think to look for?
The short answer is: yes.
Other AIOps solutions require the operations team to establish and set up thresholds for every issue they can conceive of—meaning the team needs to know in advance what could happen. It’s a bit like configuring. a home alarm system. Sure, you’re going to put a sensor at every entry point, but forget the basement window and, whoops! In slips the burglar without a sound.
Predictive AIOps doesn’t need to cover every single metaphorical point of entry to be covered. For example, it learns behavioral patterns from application logs, eventually defining what “normal” is for each application. It can then surface error conditions as soon as the first symptom occurs. Because of this, ServiceNow Predictive AIOps is considered truly predictive, delivering yet another level of machine learning analysis not addressed by traditional AIOps.
The takeaway here is that predictive AIOps can proactively reduce the amount of time needed to detect, diagnose, and fix issues. This makes it easier for IT to deliver experiences that meet and exceed user expectations.
Happier IT teams
Another positive product of predictive AIOps is a reduction in noise from users, services, applications, and cloud events.
Predictive AIOps filter out the noise so your IT team can focus on high-priority issues.
Rather than being barraged by thousands of individual events, IT operators receive a proactive notification with relevant knowledge articles and similar issues that have happened in the past. They are also given a description and timeline of the event, the services impacted, and remediation recommendations. In some cases, the remediation is automated.
This makes life much simpler for IT teams, improving their overall performance and work experience, and freeing them up to tend to more interesting, higher-priority issues or unexpected events. It doesn’t get much better than that.
If there’s one thing we learned from last year, it’s that no one can predict the future and businesses have got to be prepared for anything. With predictive AIOps, you can see far enough in the future to keep your digital services up and running and your organization moving forward—a genuine advantage in an uncertain world.
Lisa Wolfe is a Director of Product Marketing for ServiceNow and has 20+ years of experience as a global marketing leader helping clients get significant value out of