AIOps (artificial intelligence for IT ops) refers to the automation of IT operations through AI and machine learning. With every organization working rapidly toward the ultimate goal of digital transformation, there is a natural place for AIOps within service management as a means to automate processes and automatically spot and react to issues in real-time. But what exactly is AIOps, and how does it remove the noise from service management?
AIOps: Taming the Growing Complexity of IT
Digital transformation has resulted in IT infrastructure complexity growing at an astronomical rate. The resultant increases in infrastructure data and alarms at the service desk far exceed the capacity of any human to meaningfully read, analyze and respond to them. The infrastructure itself is also constantly morphing and changing, yet service desks are still expected to resolve requests, incidents, and performance issues in seconds – an impossible task, given the volume of data.
AIOps provides the necessary firepower to address this exponential rise in data, using artificial intelligence to continually monitor and analyze changes in the IT infrastructure in real-time. By combining these insights with service desk automation, organizations can benefit from a closed loop of discovery, analysis, detection, prediction and automation which, combined, bring us closer to the enviable goal of self-healing IT.
Two Phases of AIOps Implementation
The implementation of AIOps falls into two phases: discovery and analysis.
The discovery phase involves uncovering all of the compute, network and storage entities across your ever-changing IT environment and ensuring your configuration management database (CMDB) is always kept up to date. But just knowing what is on your network is only the first step – you then need to determine how each asset relates to, or depends on, each other. This dependency mapping stage requires tracking of dynamic, multilayered relationships between applications and hybrid infrastructure, and creating rich, visual topology maps so that you can immediately see where dependencies lie within the network, and, therefore, where potential bottlenecks or risks reside.
With your CMDB now kept up to date with automated discovery and dependency mapping, you now need to make sense of this data. After all, data is useless if you can’t use it to make informed decisions. By applying machine learning algorithms to analyze this data, AIOps can identify patterns, spot anomalies and predict future outages.
Why AIOps in Service Management?
AIOps has many benefits within the larger service management context. AIOps can:
- Support, not hinder, rapid digital transformation – with automatic discovery and dependency mapping, AIOps allow the business to continue to invest in new IT services without upsetting those in operations that need to keep the lights on.
- Reduce downtime – with automatic reporting of anomalies, operations can address potential issues before they result in a service outage.
- Automatically update the CMDB – An accurate CMDB is the foundation of successful IT operations. If your CMDB is out of date, then you are left trying to solve problems blindfolded. Just make sure your AIOps supports hybrid environments, so it can discover all IT assets from physical to virtual infrastructure, on-premises to public cloud, etc.
- Remove noise from the service desk – AIOps helps the service desk to overcome alarm noise and determine which events need their attention by eliminating false positives, performing advanced event correlation, reviewing time-series event playbacks and determining probable root cause.
- Actualize self-healing IT – no longer a myth or an IT operator’s dream, self-healing IT can be realized through AIOps. By automatically triggering actions based on the findings of the machine learning engine, AIOps can quickly fix issues or prevent them from happening, whether it’s a network link that’s gone down, an over-utilized disk or a service that simply requires a restart.
Operations is not known for being a calm and relaxing environment in which to work. When your role is primarily a reactive one, there is rarely time to be proactive. You are bombarded with alarm bells and error reports all day long. How can you tell the difference between a false alarm, an isolated incident or the early indicators of a bigger problem? AIOps, leveraging machine learning, has finally provided IT operations with the tools needed to make sense of this growing barrage of data.