According to Future Market Insights (IMF), the demand recorded in the AIOps market is expected to increase by approximately 25.4% between 2022 and 2032, and reach the valuation of approximately $8.3 billion by the end of 2022. AIOps facilitates IT operations by intelligently analyzing large volumes of data, learning system behaviors and automatically recommending effective actions.
As hybrid cloud environments proliferate and new cutting-edge technologies continue to emerge, it is becoming increasingly difficult for IT operations teams to cope with the increasing complexity of digital systems and the amount of data they generate.
Today’s customers are looking to mine statistics from many different tools at the rate of millions of digits per second. Without the performance of AIOps, IT operations teams would have no practical way to process such a volume of data.
And the stakes continue to rise: just one hour of downtime can cost a business more than $300,000, according to the vast majority (91%) of organizations surveyed in the global survey. carried out by ITIC on the security of server equipment in 2022. The cost of such an hour of downtime could even reach one million dollars for 44% of the companies questioned.
No wonder AIOps is on the rise.
In addition to allowing companies to free up qualified personnel to work on innovative projects, this method helps them to ensure the smooth running of their activities by acquiring software that exploits artificial intelligence (AI) and machine learning (ML ) to handle a growing volume of statistics, events, and logs.
As with all enterprise software, it is not recommended to deploy AIOps without prior planning. Before getting started, companies should be aware of the five main advantages of this approach.
Anomaly detection
Advanced AI-powered anomaly detection helps spot outliers in data to dynamically define a standard service profile. The behavior of the system thus automatically sets thresholds determining the generation of events.
This approach often uses multivariate algorithms and can automatically adapt to system behaviors learned over time. Thanks to the information obtained, it is possible to monitor these systems in a more intelligent way, with alert thresholds automatically adapted to their normal behavioral characteristics.
Event Correlation
AIOps reduces the noise associated with a large number of events in a given environment. This approach links different isolated data sources by integrating them in the form of logs, traces and measurements, for example.
Advanced AIOps technologies identify event correlations across multiple time, text, and topology dimensions, eliminating some of the noise (e.g., duplicate events or secondary events) and allowing multiple events to be aggregated underlying as a higher level phenomenon.
Identification of root causes
To understand the root cause of an incident, it is necessary to have a precise vision of the relationships between the different elements of an environment. AI and ML based on knowledge graphs, enhanced by topological analysis, make this possible.
By applying this kind of advanced analysis to infrastructure and application operational metrics, AIOps can isolate the real issues. This allows IT teams to focus their time and energy on tasks that are more relevant to the business, thereby reducing its operating costs.
Automation and smart fixes
While it is important to separate noise from events and to identify the root cause of problems, it is still essential to be able to take the corrective measures required to resolve the incidents encountered. Modern AIOps solutions are capable of performing automated remediation actions in response to incidents, ideally by integrating with a wide range of platforms and automation tools.
Once operations teams are familiar with automations based on remediation history and success rate, they can set policies so that desired actions are performed without requiring manual intervention as soon as a root cause is detected. Proactive AIOps systems evaluate the results of all the automations put in place in order to recommend, if necessary, their extension to additional domains.
Predictive analytics
Ideally, AIOps systems can take IT operations to the next level by anticipating potential incidents and taking corrective action early. It is thus possible for them, for example, to predict situations of resource saturation or capacity limits by making projections on the organic growth of systems while drawing lessons from past behavior. Operations teams can then identify actions to take, including provisioning additional capacity or resources, before issues arise.
AIOps systems can also examine historical data trends and identify risk points where system failure or performance degradation is likely to occur. This type of real-time, predictive alerting prevents teams from encountering unforeseen difficulties and protects companies from service interruptions.
Businesses won’t be able to deliver digital experiences on their front-end if they don’t equip themselves with the necessary tools to drive digital transformation on their back-end. AIOps will enable IT operations teams to stay efficient in increasingly digitalized enterprises by intelligently analyzing large volumes of data, learning system behaviors and automatically recommending actions, whether proactively to prevent process failures or by quickly identifying the root causes of problems that could not be avoided.
By focusing on these five use cases, enterprises will be able to adopt new application architectures and increasingly complex hybrid ecosystems, while ensuring that their IT operations teams can remain effective in the face of new needs. of their business and the changing expectations of their customers.