AIOps is one of the current buzzwords (buzz-initialisms?) that is hot in the monitoring space. Everyone seems to be talking about it. How you have to have it, how much better it will make everything if only you just had it, etc. But how much of that is real and how much of that is wishful thinking? Let’s take a look and see if we can separate the buzz from the words.
AIOps was first used by Gartner in 2016 and was defined as: “… an industry category for machine learning analytics technology that enhances IT operations analytics. AIOps is the acronym[sic] of ‘Algorithmic IT Operations.’” As with most things in the “best of intentions” category, everyone looked at AIOps and assumed AI meant artificial intelligence.
Thus, a market was born, with some vendors mistakenly listing the original definition as Artificial Intelligence and not Algorithmic. While this was probably to be expected, it is unfortunate since it has, in many ways, adversely impacted the potential that AIOps could bring to the IT industry. Several challenges exist with the current state of AIOps including:
Every vendor is doing something different with AIOps and defining it differently. Subsequently, having a conversation with multiple vendors, or worse, comparing their AIOps solutions ranges from really difficult to virtually impossible. How can you talk to three different vendors about their AIOps options if each of those options are entirely different? For example, looking at 3 different vendors:
These three examples all exist in some form or another, and all are different, sometimes in major ways. Comparing vendor #1, who provides some data collection but then only looks for anomalies in that data, to vendor #2, who does no data collection but does more analysis, can be done but is very complex. If you choose vendor #1, you get the data collection piece but less of the analysis piece. If you choose vendor #2, you get more of the analysis piece but have to look elsewhere for data collection. And rest assured, those vendors will probably tell you they do some AIOps, as well.
Before we continue, I want to make sure I clarify something. I truly believe that AIOps is the future of IT monitoring. I don’t see any way that down the road everyone (or almost everyone) is leveraging AIOps in some form or another to help them move from being reactive (waiting for someone to report an issue, then manually identifying the root cause, developing and implementing that solution) to being proactive (all that reactive stuff done automatically). So far, we have no viable solutions. Some of the pieces are in place, and organizations should leverage those as best they can, while managing expectations appropriately.
Now back to our regularly scheduled program.
At Netreo, we decided that the best way to leverage AIOps is not trying to solve all our customers’ problems. Sure, being able to do RCA, incident remediation, automation, etc. would be great, and our customers would love us. But the reality is that tools trying to boil the ocean rarely succeed.
Instead, we looked at the different places machine learning (ML) could help and determined that leveraging ML for tuning our tool to ensure it is operating at its peak performance and efficiency was the best place for AIOps today. Is AIOps: Autopilot as sexy as what some other vendors are doing? Nope. Does it work consistently and reliably and solve a real-world problem really, really well? Yup. Think about it, how many FTEs do you have tuning your monitoring solution? A half? A full? Multiple? What if you could get that down to one person spending a few hours a week? That is real time savings and real money being saved.
The good news is that I have some thoughts on what organizations should do:
Make sure you fully understand what you are looking for with AIOps. Do not just start Googling the term AIOps and talking to the vendors that show up on the first page. Develop a plan that covers:
Once you have a plan to deal with AIOps in place, you can move to the next step of identifying vendors. Make sure you keep focused on what your objectives are. Vendors will likely try and sell you everything they can and that may or may not meet your needs/plans.
When choosing a vendor, make sure they are on board with what your expectations are and are willing to commit to your timeline (interestingly, this aspect often comes from the vendor side – they want clearly defined objectives so they can close the deal and in this case you are doing the same thing just from the customer perspective – clearly defined objectives of what the tool will do).
Manage your expectations. If you go into the process assuming you will find a tool that does everything and does it amazingly, you are pretty much guaranteed to be disappointed. If you go into the process with realistic expectations, you are much more likely to succeed.
Finally, if your needs align with leveraging ML for tuning your monitoring solution to deliver peak performance and efficiency, schedule a Netreo demo today!