AIOps myths and how to avoid them
Gartner coined the term AIOps in 2016 to refer to the combining of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.” In the five years since, AIOps has grown leaps and bounds — last year, AIOps was at the peak of the Gartner hype cycle. Given that the ‘trough of disillusionment’ is soon to follow, in this blog post, we talk about what are the common misconceptions about AIOps and how enterprises can leverage the technology to accelerate it to the ‘plateau of productivity’.
Myth #1 AIOps is just better monitoring
Enterprises who already have a complex IT operations monitoring setup believe that AIOps is just another way to do the same thing. The biggest objection as hear is: “But we’re already doing thorough monitoring.” AIOps is a lot more than that.
Monitoring enables visibility; AIOps empowers teams with autonomous remediation. Monitoring tools capture data and, at best, identify anomalies and flag them for the attention of the operations teams. On the other hand, AIOps tools can:
- Make complex correlations
- Identify root cause
- Set dynamic baselines
- Predict incidents
- Implement autonomous fixes
- Prioritize alerts for operations teams to focus on
Myth #2 AIOps implementation needs a complete IT overhaul
Not at all. The fundamental goal of AIOps is in collecting data from across sources and making meaning to prevent future incidents. The better the data, the better the insights and predictions. A good AIOps tool will be able to ingest data from across sources, formats and types. Going above and beyond just metric data, it should bring together logs, topology, past alerts and, most importantly, workload data to make meaningful correlations to enable autonomous remediation.
To do this, the AIOps tool must be able to integrate with a wide range of data sources. This can be done using agents/bots who mimic human action/processes to gather data from all relevant sources. Or, you can also use connectors, if your application landscape is mature and can support API-driven data ingestions.
Myth #3 AIOps is a setup and sit back solution
Well, yes and no. Most AIOps tools today are advanced and feature rich. Once you implement them and integrate them, your operations burden is likely to reduce almost immediately. However, like all artificial intelligence, AIOps too gets better with data and experience. The incremental ROI that enterprises can drive from their AIOps solution can be significant.
Myth #4 AIOps will replace IT teams
At least not for a long time to come. In our view, what AIOps is transforming is not what humans are bad at, but what’s impossible for humans to do. For instance, going through every single piece of data about application performance to identify anomalies is not a task for the human brain. Neither is processing every single alert to see if they would cause an incident in the future. AIOps is great at this large scale data-crunching and pattern recognition. It is also effective at predicting incidents and running remediation.
However, long-term IT operations strategy, experimentation, innovation etc. are still the forte of humans. What AIOps does is eases the IT team’s burden from data processing to innovate. Enterprises can free their teams off mundane tasks to build innovative solutions for their customers. In a way, AIOps can play a crucial role in taking IT to the business table.
Myth #5 AIOps adoption will mean hiring new data scientists and AI/ML engineers
The AIOps market today has several fully functional products, once implemented, can work independently. Especially if you choose SaaS-based solutions, the implementation, maintenance, upgrades etc. are entirely managed by the AIOps provider themselves. This means that the enterprise does not need data science, artificial intelligence or machine learning skills in-house to make AIOps a success.
Myth #6 AIOps is good-to-have
In 2022, we believe that this will be the most damaging misconception. As the digital world expands rapidly, the scale and complexity of the IT landscape will make it impossible for operations teams to handle everything on their own. A combination of myriad monitoring tools adds to the stress of alerts. Ops teams end up spending inordinate amounts of time evaluating false alarms, often making the situation seem bleaker than it really is. This also means that some important alerts go unnoticed — alert fatigue is a thing, of course — doing precious little to prevent/remediate the performance issues these monitoring tools were set up to do.
To run a robust and high-functional IT system, enterprises need AIOps. You need the scale of an intelligent solution to process big data, identify patterns, predict problems and resolve them autonomously. You need systems that can go beyond basic pattern recognition to perform advanced workload-behavior correlation to get to the root of real issues. You need AIOps to navigate the uncertain and demanding future.