By Vijeth Shivappa
AIOps which is described as “artificial intelligence for IT operations.” AIOps is the practice of applying AI to automate and optimize IT processes. Leveraging both machine learning and advanced analytics techniques, AIOps proactively identifies, isolates and resolves IT issues.
The ultimate goal of AIOps is to build autonomous IT operations. The key difference between AIOps and conventional IT analytics tools is the automation component. AIOps platforms work by, first, ingesting, consolidating and analysing all IT data into one, centralized platform. This means aggregating both historical and real-time data from dozens of sources including helpdesk systems, multi-cloud environments,containerized applications, storage, databases, events and logs, APIs and SDKs, APM, monitoring, and data streams. The system then applies a series of advanced analytics to this data, ranging from statistical and probabilistic analysis to automated pattern discovery and prediction, unsupervised learning for anomaly detection and topological analysis to any combination of these techniques.
Every enterprise has different needs and accordingly implements AIOps solutions. The focus of AI solutions is to identify and act on real-time issues efficiently. Some core elements of AIOps can help an enterprise to implement AI solutions in IT operations.The four stages of AIOps involves Collect raw data, aggregate it for alerts, analyse the data, then execute an action plan. AIOps or IT Analytics is about finding patterns. With the help of machine learning, we can apply the computational power of machines to discover these patterns in IT data. Key AIOps capabilities that will boost your IT performance are ,Dependency mapping,Event and incident management,Predictive maintenance,capacity management & Automated remediation.
Any changes in usual system behaviour can lead to downtime, a non-responsive system, and a bad customer experience. With AIOps, it’s possible to detect anomalies or any kind of unusual behaviours or activities. Gain Predictive insights with AIOps. AIOps introduces predictability in IT operations. It can help IT staff to be proactive in capturing any problems before they occur, and it will eventually reduce the number of service desk tickets. Automated root cause analysis is another key focus area for AIOps. Driving insights alone is not enough. The enterprise or the IT team should be able to take action as well. In the traditional management environment, IT staff would monitor the systems and take steps as and when required. Due to the increasing volume of IT infrastructure issues, it would be difficult for the staff to manage and resolve the issue on time. It takes a great amount of time to analyse the root cause when multiple systems are involved. With AIOps, the root cause can be done in the background automatically.
AI and ML can be applied at various places in an IT domain. AIOps vendors can be generally categorized as domain-centric and domain-agnostic, as described by Gartner. Domain-centric AIOps vendors are focused on bringing AI-driven decisions to a certain domain, typically in the monitoring spaces like Application Monitoring, Infrastructure Monitoring, Network monitoring, etc.Domain-agnostic AIOps vendors are generally focused on cross-domain IT data, bringing data from IT operation Management, IT Services Management, IT asset management tools and providing aggregate intelligence, cross-domain correlation, bringing context to data, and driving broader autonomous decisions at scale.
Pure play AIOps: (Domain-Agnostic):
These vendors are truly domain-agnostic, operating on IT data from all domains (apps, microservices, infra, incidents, cloud …) and provide aggregate intelligence and augmented decisions taking into consideration a very wide spectrum of IT data, thus yielding better results than purely domain-centric platforms. One major advantage of such platforms is also the notion of understanding the application and business context that allows for driving better ML decisions and reducing false positives and unintended consequences that may be prevalent in machine driven decisions.Example : CloudFabrix, BigPanda, Moogsoft.
Data-Lake centric-AIOps (Domain-Centric):
These types of vendors are primarily known for their capabilities to serve as a massive data store or a data-lake for log data. However, these vendors later expanded to store more types of data i.e time series metrics data, configuration data and more. These vendors started to provide AI/ML on the data they have, primarily around predicting some patterns and providing good visualization and anlaytics on wealth of data they own, but one major gap in these kind of AIOps is they have very little understanding and context of the application stack, topology, serviceability, supportability and how apps are tied to a business or service.Example : Splunk, Elastic, Graylog.
Monitoring-centric AIOps (Domain-Centric)
Observability i.e monitoring tool vendors are now claiming AIOps, but this is localized or domain-centric application of AI. This kind of AIOps might be sufficient if your entire IT estate is being monitored by one or two monitoring tools. However, for large enterprises this is rarely the case. Some large IT organizations have at witnessed least 15+ tools in healthcare, pharma and financial industries. Example: AppDynamics, Dynatrace, NewRelic, Datadog, LogicMonitor, ScienceLogic etc.
IT Services Management centric-AIOps (Domain-Centric) :
Vendors who are originally focused on incident management, have primarily event and incident data, and are now beginning to apply AI/ML to incident specific use cases. However, this is form of AIOps is again, localized to Incidents, which generally are reactive in nature, and cater to service-desk, Network Operation Center and ITOps personnel. Example: ServiceNow, PagerDuty, Cherwell.
AIOps provides a solid way to turn the hype about AI and big data into a reality. From streamlining operations to increasing productivity to improving security , AIOps is the way to help you scale your IT operations to meet future challenges, making Digital Transformation as a strategic enabler of business growth.
– All views expressed are personal