Expected to be worth more than $40 billion by 2026, AIOps solutions are paving the way for improved IT operations in digitally-driven organizations. A major factor driving this is that traditional operations management models are no longer good enough to deal with a world where distributed work has been normalized. Instead, by leveraging big data, machine learning, artificial intelligence (AI) and other advanced technologies, companies can bring greater automation to enterprise IT infrastructure and service management as well as gain complete visibility of the network infrastructure challenges they face.
Operations management used to deal with various IT tasks at a departmental level in the past. In fact, many solutions today still follow this nitty-gritty approach and perform system analysis in siloes. In doing so, however, business and technology leaders do not have the rich context needed to connect the dots, troubleshoot effectively and quickly empower operations teams with the insights required to focus their efforts in the most impactful areas.
Outside of alarms and notifications, this approach means that very little advanced thinking and analysis is happening in traditional monitoring systems. If anything, these operations teams are overwhelmed by the velocity, variety and volume of data being generated. They cannot observe all metrics, anomalies, events and alerts using a legacy way of thinking, resulting in rapidly cascading challenges that bottleneck the infrastructure environment.
A Sherlock For Network Challenges
Of course, AIOps is not a new concept. First coined by Gartner, Inc. in 2017, AIOps platforms can “enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.
Think of these as a collection of managed services that combine network analytics with machine learning and AI to provide IT teams with instant, actionable insights. The data explosion has made it impossible to program thousands of rules on a system to examine logs, set thresholds, warn of any issues and compare events.
Let’s consider that the developments of the past two years have seen customers embrace digital services at a scale previously unimaginable, from online shopping and banking to video streaming, videoconferencing, work collaboration, networks and IT infrastructure. We also need to recognize how they have become more complex. All of this means that it is simply no longer humanly possible to set up, manage and monitor how the operations management environment deals with rules.
The power of AIOps lies in collecting and analyzing the data generated by a growing ecosystem of IT devices. Whether this comes from edge computing and Internet of Things devices or smartphones and laptops used by remote workers, the secret sauce driving AIOps is normalizing the generated data to provide a uniform picture of the entire system. In turn, machine learning analyzes it and provides IT teams with the insights required to ensure the network infrastructure real estate runs smoothly. This is where the benefit of AIOps reveals itself, as it reinvents this arduous process and better equips organizations to deal with injecting automation into the optimization of their mission-critical network, communications and infrastructure processes.
This ability to unify data from across the entire IT infrastructure of the organization becomes the critical step toward a broad-scale implementation of AI at the enterprise level. It is fueling enhanced productivity and, more importantly, manageability.
Starting The Journey
This looks good on paper, but how can it be implemented in a business? It all starts with data. AIOps cannot exist without data, and data without metadata is meaningless. Whether in the form of key-value pair labels or as tags, metadata is the context that data requires and is one of the most common pitfalls we see in AIOps initiatives. Metadata contextualizes network telemetry, logs, events, flows, routes, alerts, configuration changes, etc. It’s as important as the data itself, and without proper, holistic management of metadata, network and IT telemetry are just raw disconnected points.
Once meta tags and metrics are correctly in place, the next step is collection. Organizations should focus on analysis that leverages metrics from the widest variety of available sources for a clear view of operations. From there, correlating data points from multiple sources can provide the critical operational insights needed to keep systems running. This is where AI and machine learning play a critical role in connecting the dots and should be a starting place for any AIOps initiative. Finally, organizations need to ensure that the resulting analytics and insights can be shared quickly. Operational insights are only valuable if they reach the teams that need them.
We can all argue that based on the efficiency it yields, AIOps is the future of IT operations. It’s the catalyst that enables enterprises to speed up time-to-resolution for incidents and predict issues before they happen. This predictability and insight are essential for modern business, and they can come from the data that the business is already producing.
This next generation of monitoring delivers a paradigm shift in analytics. Even though the core modeling of data remains consistent, it comes down to embracing a more agile way of capturing those insights to improve operations management. That is what AIOps enables.