By Vijeth Shivappa
AIOps described as “artificial intelligence for IT operations.” AIOps is the practice of applying AI to automate and optimize IT processes. Using 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 main difference between AIOps and conventional IT analytics tools is the automation component. AIOps platforms work by, first, ingesting, aggregating and analyzing all IT data on a single, centralized platform. This means combining 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 SDK, APM, tracking, and data streams. The system then applies a series of advanced analytics to this data, from statistical and probabilistic analysis to automatic pattern detection and prediction, unsupervised learning for anomaly detection and topology analysis in any combination of these techniques.
Every business has different needs and implements AIOps solutions accordingly. The focus of AI solutions is to identify and act on real-time issues efficiently. A few key elements of AIOps can help an enterprise implement AI solutions in IT operations. The four phases of AIOps include Collect raw data, aggregate it for alerts, analyze the data, then execute an action plan. AIOps or IT Analytics is all 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 and Automated remediation.
Any changes to standard system behavior can lead to downtime, an unresponsive system, and a bad customer experience. In AIOps, it is possible to detect anomalies or any kind of unusual behavior or activity. Gain predictive insights with AIOps. AIOps introduces predictability to IT operations. This will help IT staff be proactive in catching any problems before they happen, and 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 must also act. In a traditional management environment, IT staff will monitor systems and take action as needed. Due to increasing IT infrastructure issues, it will be difficult for the staff to manage and resolve the issue in time. It takes a good amount of time to analyze the root cause when multiple systems are involved. In AIOps, the root cause can be done automatically in the background.
AI and ML can be applied in different areas in an IT domain. AIOps vendors can be broadly categorized as domain-centric and domain-agnostic, as described by Gartner. Domain-centric AIOps vendors focus on bringing AI-driven decisions to a specific domain, typically in monitoring spaces such as Application Monitoring, Infrastructure Monitoring, Network Monitoring, etc. Domain-agnostic AIOps vendors typically focus on cross-domain IT data, bringing data from IT operation Management, IT Services Management, IT asset management tools and providing integrated intelligence, cross-domain correlation , bringing context to data, and driving more autonomous decisions at scale.
Pure play AIOps: (Domain-Agnostic):
These vendors are truly domain-agnostic, operate on IT data from all domains (apps, microservices, infra, incidents, cloud …) and provide integrated intelligence and augmented decisions that take into account consider a very broad spectrum of IT data, thus yielding better results than purely domain-centric platforms. A key 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 could 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 ie time series metrics data, configuration data and more. These vendors have started to provide AI/ML on the data they have, mainly about predicting some patterns and providing good visualization and analytics on the wealth of data they own, but a big gap in these types of AIOps 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 ie monitoring tool vendors are now claiming AIOps, but this is a localized or domain-centric application of AI. This type of AIOs may be sufficient if your entire IT estate is monitored by one or two monitoring tools. However, for large enterprises this rarely happens. Some large IT organizations witness at 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 that originally focused on incident management, have key event and incident data, and are now starting to apply AI/ML to specific incident use cases. However, this form of AIOps is again, localized to Incidents, which is generally reactive in nature, and responds to service-desk, Network Operation Center and ITOps personnel. Example: ServiceNow, PagerDuty, Cherwell.
AIOps provides a solid way to turn the hype around AI and big data into 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 it a strategic enabler of growth Digital Transformation is business.
– All views expressed are personal
Advertisement
