As time progresses and competition grows, being “good enough” means that you may be falling behind. Engineers will discover new ways to solve problems, which will enable rapid increases in availability and scalability. With these increases comes more complexity and the generation of more data. Rather than just monitoring the new data and letting the old data sit there collecting dust, you should consider using it to gain maximum insights into your environment.
What Is AIOps and What Can It Do for You?
An AIOps strategy can include monitoring dynamic environments with anomaly detection, time-series forecasting, predicting and preventing outages, and using other statistical methods to reduce MTTR, which in turn increases availability. These insights will serve as the building blocks of an observability platform that your operations center can use to streamline operations and reduce the need for manual correlation to identify root cause.
Data can be thought of as the oil that drives the machine. Data goes through a pipeline and can be routed, transformed, and eventually stored in its final destination until it’s ready to be used. Without the underlying data, you won’t have any insights, and you’ll be left guessing. The quality and volume of data will be the biggest drivers when it comes to accurately generating insights from an AIOps strategy.
As companies grow and evolve, they depend on tools that are often managed by different teams working in silos – which creates challenges. Luckily, it’s possible to collect this data from a diverse set of sources, standardize the datasets, and use them to develop a model and gain insights using a central logging tool.
Taking Optimal Advantage of AIOps
It’s one thing to identify these insights, but another to act on them in order to gain the value they provide. Let’s look into a few ways to quantify the value of these insights.
Mean Time to Resolution (MTTR) is a metric that’s commonly used to quantify how fast people are resolving problems within the environment. This can be thought of as the time difference between the start of the impact and the end of the impact. To reduce MTTR, you should include some level of automation in the identification and resolution of problems. This includes reducing noise by correlating tickets and rolling them up into parent tickets or automated recommendations based on similarities between what happened in the past and what is happening during the present incident.
Another strategy would be to pass common performance metrics through a layer of anomaly detection to standardize their output and identify how abnormal they are relative to the time of impact. When used across multiple metrics and entities, this strategy can be an excellent indicator of problems as well as a great label for building a supervised, predictive machine learning model.
Creating an end-to-end observability platform that maximizes transparency is critical for any operations center, as it enables everyone to understand the health of the environment and removes silos. This observability platform should be available in a single pane of glass that does not require any scrolling, and it should take no more than three drill-downs to get the finest granularity. This observability platform should show all the major components that represent the environment and make it easier to understand the root cause of problems. This approach allows L1 and L2 operators to reduce their dependency on developers and engineers who should be focusing on their own work instead.
Predictive insights are the holy grail of AIOps that everyone wants to achieve. It allows you to predict the future with a high degree of accuracy and to identify problems before they impact end-users. You can greatly reduce downtime by using predictive insights, and you can also gain a leg up on the competition by advertising that you have this capability.
Another advantage is that predictive analysis can be applied to changes and code releases in production. Predictive analytics relies on matching patterns and understanding normalcy, so when a new change is introduced to the environment, the predictive model can quickly identify problems or point out performance defects that can hurt overall throughput.
Conclusion: How AIOps Enables Companies to Continuously Improve
You can think of AIOps as a collection of tools that offers an inexpensive way to minimize downtime and reduce the need for manually detecting and correlating problems. A good AIOps strategy will help streamline infrastructure in complex environments while enabling a healthy service delivery and boosting customer experience. Before you begin your AIOps journey, make sure that you have enough clean, quality data – then start small and dream big!