It doesn’t take a lot of digging to show that AIOps, particularly within service provider organizations, is a booming industry. Forrester reports 68% of companies surveyed have plans to invest in AIOps-enabled monitoring solutions over the next 12 months. Gartner forecasts that by 2022, 40% of all large enterprises will combine big data and machine learning to support and partially replace monitoring. The list goes on and on.
But, if you look one layer deeper, you can start to piece together a compelling story that while companies see the true value in AIOps, they don’t always know how to realize that value. In a Logicalis report, overall only nine percent of respondents considered AI projects successful, and only 35% of all IT-focused projects were considered successful.
It’s clear that with the rapid adoption of AI there remain challenges that service assurance organizations must address. Conducting a readiness assessment can help to set a solid foundation and clearly defined objectives that will help ensure the future success of AIOps initiatives.
A successful AIOps program has some really attractive outcomes once it’s established and proven out. Because of that, it’s tempting to try to implement a soup-to-nuts program right out of the gate. Stop there.
Trying to lump all of the machine learning needed into one project or phase creates a risk of not meeting your objectives. Don’t be afraid to start small with an iterative approach that defines a more limited strategic goal. Doing so will not only help you prove out your proof-of-concept, but will keep you and your team from “analysis paralysis” that can come from trying to do too much at once.
You do want to outline the overall objective and high-level phases, but then start with smaller projects in a daisy chain — start, perhaps, with event clustering, then add in log clustering, then anomaly detection before getting to incident prediction. This approach lets you focus on the existing project and objective, measure the success of it and make adjustments in future phases.