By: David Hardman, Sr Principal Product Marketing Manager, AIOps
It’s true that AIOps can help you increase operational efficiency while enabling you to transform customer experience. But take a look at the following spooky myths to make sure you aren’t haunted by ghastly problems after you choose an AIOps solution.
Myth 1: AIOps means monitoring blind spots no longer matter
Monitoring blind spots have long plagued IT operations teams. But, AIOps fixes all that – right? AIOps can apply advanced analytics and machine learning to astounding amounts of data. However, monitoring blind spots are like ghosts in the system and you can’t analyze what you can’t see. These apparitions may show up in ways that are very scary for your customers. You need to shine a light on them before they impact your customers and your business.
It’s best to choose an AIOps solution that eliminates blind spots. Full Stack Observability – from mobile to mainframe and application to network and the ability to monitor all environments while integrating with third party tools enable you to get full visibility to cross correlates every component of your full stack.
Myth 2: Log monitoring is sufficient
Log monitoring is certainly indispensable. Being able to parse and analyze events found in unstructured system, application and network logs is a critical capability to any operations team. But looking at the problem from multiple angles helps predict problems from alternate dimensions and log monitoring alone is insufficient.
Having comprehensive data sets consisting of both structured and unstructured data including metrics, alarms, logs topology, text and API data provides the best data for AIOps. The best AIOps solution is able to provide both metric-driven and event-driven anomaly detection to detect potential issues before they affect your users.
Myth 3: All you need is the right algorithms
Algorithms can be a bit mysterious but are a key element of any AIOps solution. Augmenting human intelligence one goal of the AI in AIOps but if you ingest the wrong thing into your algorithm the result may be more like a zombie than a human!
A primary outcome of AIOps is to determine the root cause of problems. It does this through machine learning that automatically and algorithmically determines root cause. Make sure your algorithms aren’t single-minded or zombie-like (brain!) in what they consume to do their work. Look for an AIOps solution that feeds its algorithms with a healthy dose of the four Ts: topology, time, text and training to correlate from multiple data sources to arrive at root cause, eliminating noise and giving you faster time to repair.
Myth 4: AIOps stops at knowing
It may seem enough to know the root cause. After all – not knowing the root cause can be downright frightening as if a poltergeist has taken over and is trying to wreck your business. However, the point of knowing the root cause is being able to act to resolve the problem.
Intelligent automation uses predictive analytics to drive automation to fix problems before they impact business. A complete AIOps solution will be able to invoke automated workflows and update tickets.
Myth 5: AIOps purpose is to fix problems having the biggest impacts on IT
Ah – this is a tricky (or treat) one. Sounds like a fact on the surface. After all, being able to prioritize based on impact is important – right? Of course, but it’s not only IT impact but it’s paramount to understand business impact.
Choose an AIOps solution that provides service-driven business insights. These are business service-based views used to identify which issue is impacting your KPIs most. The best AIOps solution uses smart service modeling to map issues to associated business services – enabling intelligent prioritization to minimize impact on customer experience and your business.
Selecting the right AIOps solution doesn’t have to be scary
I hope that by debunking the above myths I’ve helped ensure your AIOps journey isn’t as scary as a trip through a Halloween Haunted House.