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Using AIOps to solve the top problems of IT operations

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The year 2020 has forced all organizations to upgrade their way of working and become more digital when it comes to IT operations. Some were ready for this step, while others used ad-hoc solutions that caused a lot of headaches.

The remote way of working means there is no way of helping colleagues or clients directly; everything has to be done via a platform or a software product. IT teams are under constant pressure to troubleshoot different problems.

They are always firefighting instead of planning. Even when they find the item that caused the issue, most problems require a complex, multi-discipline approach, thus slowing down the process. Last but not least, this way of working leaves very little time for disruptive innovation.

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However, recent advancements of AI and machine learning algorithms can help IT teams react on the spot or even anticipate issues and optimize systems. After the processes become more well-tuned and automated, the team can even have enough time to optimize and innovate, bringing additional value to the client or developing new products and services.

Pareto’s Rule states that 80% of problems are caused by 20% of all possible factors. Using this reasoning it follows that we can drastically improve a system’s performance by focusing on a small number of problems. Here we choose to highlight four.

IT troubleshooting requires lots of data

We are moving towards a cloud-based way of operating. Even on-premise systems have lots of inputs and moving parts, which each constitute a degree of freedom and a potential threat to smooth operations.

To add to the complexity, the end-user only sees the effect of a malfunction. The underlying cause is usually a few steps up the operating pipeline.

IT specialists need to dive deep into the software tools’ logs to identify errors and suggest solutions, but this is time-consuming and sometimes requires novice users’ assistance. Also, since most systems are interconnected, the specialist must look at logs – metrics or events at other systems as well.

An AIOps solution learns from millions of data points from logs and sensors (see also – Siscale Elasticsearch for more information). It can identify patterns that categorize the problems as they appear or even before based on precursor patterns.

There is no way a human operator could scan through all the information in due time. This is a new way of bringing additional value to the customer: preventing that long and dreadful call to the service center. AIOps is an unsung hero in this case, but cutting down the number of service tickets and the duration of support calls are relevant KPIs.

IT Operations management is time-consuming

If you are not using AI, manual troubleshooting takes a long time, and it is prone to fake leads, which adds to the total time to solve the underlying problem. Even a simple error can take anywhere from minutes to days that were not included in the initial project plan.

Even highly efficient, agile teams have a hard time putting a number on how much time they spend troubleshooting. This usually happens when the user doesn’t report a specific well-documented error code but just a manifestation of the problem, which is not included in the guidebook.

Instead of launching a crusade when a problem is reported, an AI solution permanently scans the system, comparing actual values with each indicator’s estimated ones.

The algorithm has already learned the expected values depending on context (day, hour, point of access, etc.).  If something is off track, not only does it know within minutes, it also indicates the source which triggered the problem, cutting down the troubleshooting time with hours or days.

Different ways to solve the problem

In traditional IT troubleshooting, different specialists could have different opinions about what caused the error and how to solve it. Choosing one requires thinking, negotiating, and even voting an option. It is sometimes a trial and error process that takes time and can even cause internal frictions in the team.

Switching to an AI-powered solution means that the algorithm automatically selects the best solution based on correlations between all the signals. The AIOps’ decision is chosen statistically, with a pre-set confidence degree (usually 95% or more).

AIOps to boost innovation

Although IT teams are excited about fostering innovation in their organizations, they are usually stuck in mundane troubleshooting tasks. The specialists don’t have the necessary time and relaxation to explore ways of making the most of the infrastructure they have.

However, the new massive work-from-home paradigm has changed priorities, and now there are other variables to take into account.

Choosing an AIOps solution comes with the advantage of saving time and removing the guesswork from the process. Having an algorithm in place means that you can evaluate every process according to a benchmark and see how good it performs. The great advantage of AIOps is that it can take care of background tasks and let the IT teams focus on new solutions.

What are the options?

IT systems are the backbone of modern organizations, and keeping them running smoothly is not only a competitive advantage; sometimes, it is the entire business.

The extensive data influxes require new ways of handling data. The visual dashboards display the analytics in a C-suite format, which also offers drill-down opportunities.

AIOps solutions can help with automated anomaly detection to prevent bottlenecks or frauds. Any abnormal behavior of the system can reflect either a change in the service level caused by external factors or an attack indication. AIOps also includes predictive analytics, estimating future volumes of operations by comparing the current activity with historical trends.

The platform also provides troubleshooting suggestions or even implements these automatically. The difference from simple automated systems is that the AIOps can make educated decisions based on dynamic rules, not predefined thresholds.

Event clustering, which means grouping together correlated events, forms the foundation for automated root cause analysis.

In conclusion, adopting AIOps in your organization can lead to a disruptive IT transformation. However, since this is a new technology that can have a tricky learning curve, it is best to start small with a pilot project and expand towards the entire department or organization.