Did you know that for traditional AIOps to work – you first have to tell it every possible thing that has gone wrong in the past in your IT environment and that it should watch out for these historical issues. You are effectively telling the AIOps to look in the rearview mirror. Your IT Operations team or your data scientists have to establish thresholds or patterns and then tell the AI – “hey if you see one of these things then something has gone wrong”. In a hybrid containerized world IT is just no longer predictable. No organization today can afford to base it’s business or it’s future on telling AIOps to look backward – what about unexpected, unplanned for and unknown issues ? The kind of unprecedented instability we are seeing in the world at large is what your AIOps needs to be ready for because your IT is likely experiencing more complexity than ever before.
Assuming you’ve already moved workloads to the cloud, are in the process of shifting to Agile methodologies your Central IT is no longer the center of your IT universe. You now have IT teams, Dev teams across your business units delivering digital products several times a day – when just a few years ago that would have been several times a month or even a year.
Here is a great analogy on how traditional AIOps does not help you be ready for unknown issues. Say you were installing a home alarm system and for that alarm system to work you had to put a sensor at every point of entry – at every door and window – for the alarm to go off. But what about the points of entry you did not think about – like a large dog door, once someone gets through that dog door the alarm is not going off.
In today’s complex IT environments that span on-premises data centers stamped across the globe, multiple clouds, and containers traditional AIOps solutions are not much help to let you know about the issues that you don’t even know could be issues.
ServiceNow’s Predictive AIOps changes all of this – it does not have to be told in advance all the known issues that can go wrong, it does not have to have thresholds set or data scientists establishing patterns to look out for.
It works like this – the ServiceNow Predictive AIOps engine looks at all sources of data, and it continuously does something a person would have done, only at software speed and accuracy. The engine starts looking for anomalies, and for opportunities to correlate behaviors from logs metrics and events.
The ServiceNow Predictive AIOps engine is able to search for anomalies and correlations at scale, and review all parts of the application, infrastructure and data sources in near-real time and look for correlations, any correlation between any part of the data that could indicate that the anomaly or anomalies are influencing each other. Do you realize how much work that would be for a human being to figure out? And remember, we are not telling the engine what to look for. The bar used to be very high, as I mentioned you use to need data analysts who wrote R functions and scripts and even then a human being ended up deciding if the data meant anything. Let's assume an army of humans did mine the mountain of data to surface an issue - is that the end goal? No! You want to fix the issue too. Only with ServiceNow can you both predict the anomaly and take action to resolve it - we call it ServiceOps."
Why not let software solve this question?
Once the ServiceNow Predictive AIOps engine is deployed in an environment it is a matter of just a couple of weeks for it to learn what a normal IT environment looks like – so basically it learns what’s normal. After a couple of hours it will start raising alerts, but the more it learns the more effective it gets, and you can also contribute by training the model in a simple “thumbs up, thumbs down” feedback.
ServiceNow’s Predictive AIOps helps your business scale, prevent outages and gain predictable performance without being told what to look for by looking in the rearview mirror – this gets you ready for whatever could come next in an increasingly complex IT environment and world.
