AIOPs is a new addition to the AI market and it is sometimes confusing. It is an umbrella term for all solutions that use artificial intelligence (AI) or machine learning (ML) on big data to enhance and automate IT operations and monitoring.
Until now, AIOps (a term introduced in 2017 by Gartner) is present only in some of the most progressive organizations as a new way to manage IT operations and make the most out of the piles of data. However, this will change rapidly in the next few years, as CIOs are starting to consider this option, although most of them still wait for the technology to become mainstream.
The adoption will most likely follow the “Crossing the Chasm” model. The first applications will be in the low-hanging fruit area providing fast insights, followed by more in-depth uses in the upcoming years.
AIOps Use Cases for Fast Results
Although AIOps can have many applications, there are four ways to use it in any organization, which offer fast results to justify the investment and provide a basis for further development.
AIOps for alert triage
AIOps uses machine learning algorithms for alert triage – analyze alerts and prioritize them based on their importance and business impact. This is also a way to weed out many false positives and low-impact notifications that could otherwise require the IT team’s attention. This is a way to save a lot of time and allow the IT team to manage critical operations instead of firefighting minor alerts.
These systems work by retrieving the data, analyzing it, understanding the context, and considering the logs’ source and frequency. The algorithm then performs event correlations and cluster analysis, dividing the problems into classes, and taking action. The system looks at the timestamp, source, and context of alerts and decides if an alert should be dropped (ignored) or escalated. Basically, is looking to see if an alert is a false-positive or true-positive.
Anomaly detection with AIOps
Most processes follow patterns, even if these include cyclical or seasonal variations. Anomaly detection helps IT operations identify either spikes or drops in the activity, which could be the markers of unusual activity. Such action could have natural causes, such as an influx of orders resulting from good publicity, or (just an example) could signal that the system is under a hacker’s attack.
Such systems are beneficial for server management to avoid downtime and bottlenecks. The ML can determine resource allocation by comparing historical patterns with actual demand and reduce the need for manual intervention.
Event correlation
The benefit of using AIOps is associating different alerts with the same underlying cause into a single notification. This is done by looking at the moment of occurrence, the place where it has begun, and the overall context. The machine learning algorithm bundles together the alarms from different systems and only indicates the action to solve the root cause or applies automated solutions if previously defined.
Root cause analysis
Current IT monitoring tools focus on gathering data in logs, collecting metrics, scanning for events, and other similar actions. However, monitoring is an important building block in reaching the ultimate goal – Automated Root Cause Analysis, aiming to reach a high-tech development level to identify the root cause with high accuracy and put in place automated recovery systems. It is necessary to shift from a data collection approach to a data analysis way of working.
Other use cases
Apart from these, several other AIOps applications can contribute significantly to decluttering IT departments’ to-do list. These include incident prediction and automated issue remediation, based on automatic reconfiguring.
How to choose an AIOps vendor?
Since this is still a very new business field, it is always open to newcomers, and this can be very confusing for CIOs and other tech stakeholders striving to find the best solution for their organization.
When looking for your company’s right AIOps partner, you should select one that has these three features.
- Access to relevant data. Although AI and ML algorithms require massive amounts of data for training purposes, it is extremely important for all such systems to give companies the ability to input direct feedback thus improving the accuracy of the model, adjusting it to their own specifics.
- Industry know-how. Always choose a vendor with some experience in your industry since this gives them the advantage of knowing the potential pitfalls.
- 360 degrees solutions. Suppose you can choose a vendor that offers different integrations as well as security and other data services. In that case, it is preferable to select the whole package instead of multiple individual vendors.
Future developments?
Right now, AIOps is still in its development stage, but this development is exponential; and we can expect that in five-ten years, all organizations will use a form, either as native solutions or third-party tools.
Right now, CIOs are still investigating this technology, but soon it will be on the to-do list. The main challenge for them is to select the right vendor for their needs and prepare the adoption organization.
To create a good AIOps implementation, the company needs to access historical data and live feeds from all the systems they use. To make full use of the capacities offered by AIOps through automation, the organizations need to be ready to embrace process reengineering.
If you are thinking about adopting AIOps in your organization, take into consideration training the staff and preparing for some changes. This is a new working paradigm, and teams need to learn to operate in a new way, understanding the platform’s outputs. Working in cross-department teams might be beneficial since the AIOps is an integrative way of solving problems.
Finally, it is mandatory to anticipate associated security risks, make sure the system is compliant with local data regulations, and consider other governance and compliance issues. As with any IT system, it needs to be protected, useful, and thoroughly documented.
As a piece of advice for CIOs, we suggest looking into the AIOps opportunity as soon as possible because pioneers get a competitive advantage, while mass-market adopters only get implementation benefits.
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