For the uninitiated, AIOps can simply be expanded as Artificial Intelligence for IT Operations, but more than just an acronym; it’s a loaded term, full of possibilities. AIOps are in fact becoming the next big thing in IT management and it has got a lot to do with the operational efficiencies that it brings along with it. With this article, we take a low-down on everything about AIOps that you should know.
The ‘What’ And ‘Why’ Of AIOps
AIOps can simply be defined as the method of using big data, machine learning, artificial intelligence and analytics to solve issues in information technology. It is basically the convergence of AI into traditional IT operations. It is the next-gen IT Operations Analytics of ITOA.
So why do we need to integrate AI into IT? IT infrastructure generates a lot of data from the disparate layers of the stack, which can come from log files, metrics and monitoring tools, or ticketing systems, among many others. For this data to derive rich insights, it needs to be aggregated, normalised and analysed. With the large volume of data and without AI, it can otherwise prove to be a tedious task to handle.
AI can bring significant impact on system operations and administration by increasing IT efficiency, stepping up service delivery and bring superior user experience. A survey rightly suggests that AIOps may help IT managers differentiate between legitimate signals and inconsequential noise.
Since AIOps leverages big data, ML and analytics, it can drive faster root-cause analysis, bring intelligent automation by automating routine practices, recognise serious issues faster with greater accuracy than humans, streamline interactions between data centre groups, among others.
Without AI-enabled operations, teams must process information by manually sending around data. AIOps is, therefore, playing a key role in enabling new efficiencies for IT Ops teams.
The Background Of AIOps
Interestingly, the term AIOps was coined by Gartner researchers and was originally termed Algorithmic IT Operations. But given the increase in adoption and popularity of AI and the pouring investment in the technology, the term was renamed to Artificial Intelligence for IT Operations. Here’s the official definition,
AIOps platforms utilise big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.
Analyst Colin Fletcher explains in a blog post that while many IT operational leaders over the years have been building incredibly sophisticated big data and advanced analytics systems for business stakeholders, they themselves are using rudimentary and manual steps to help run the infrastructure needed to keep those systems up and running. This and the need to manage the data in a better way called for adopting AI capabilities to enterprise IT operations.
Since then there has been an increase in the number of documented AIOps successes by pioneering IT operation leaders and an increase in AIOps vendors creating new AIOps products or related enhancements.
How Does An AIOps Platform Work?
AIOps uses a conglomeration of various AI strategies including data output, aggregation, analytics, algorithms, automation, ML and visualisation. The AIOps platform architecture consists of functional layers like — visualisation, ML and analytics, data layer and others.
- Data being the key, data layer forms the foundation of the whole architecture, which seamlessly connects to different data sources in the IT landscape to ingest the data they generate. As mentioned earlier, data could come from monitoring tools, social media platforms, application logs, ITSM tools, among others. The data layer continuously observes its surroundings to collect data.
- The big data layer performs the task of managing the data that is ingested, and the AI layer which can be said to be the brain of the platform uses the ingested data to generate actionable insights out of it.
- The machine learning layer accesses the historical as well as real-time data, which helps the AIOps platform to continuously learn and optimise its functioning based on the real-time data. Some of the key capabilities that ML layer offers are that it can draw correlations across the infrastructure layer to the application layer, identify patterns within historical data and make predictions, anomaly detection, among others. The platform can also make use of NLP to make sense of unstructured data and deliver an enhanced level of end-to-end automation.
- The visualisation layer offers an interface through which IT operations team can interact with the platform. It quickly allows to pinpoint the issues and apply corrective actions. It presents data in the most discernible way so that there is minimum wastage of time and effort for data analysis.
How Have IT Operations Been Benefited By AIOps?
Some of the benefits that organisations can have with the adoption of AIOps are:
- Efficient utilisation of resources
- Improved storage management
- Threat detection and analysis
- Capacity planning
- Optimise IT processes and reduce cost
- Increase end-to-end business application assurance and uptime
- Removes noise and distraction
Why Should You Know About AIOps Now?
Because it is an AI platform for the next decade of IT. With the rise of cloud, distributed architectures and microservices, there is a visible data overload. To deal with it, AIOps is not just an option anymore but a necessity for businesses with dynamic and complex IT environment. As the IT landscape is constantly evolving, yesterday’s tools might suffice for today’s roles.
To fit into the infrastructure evolution, defeat the old systems and bring more efficiency, AIOps is the need of the hour. It’s dependency on ML and data science gives IT operations team a real-time understanding of any issues affecting the availability or performance of the systems under their care.
On A Concluding Note
While the adoption of AI in IT operations is gaining a major traction, it is still not quite as rampant as it should be. However, there are many large enterprises that are embracing AIOps in a bit to bring digital transformations and automation. Though it has arrived late but is here to stay as IT operations demand that traditional manual approaches should be replaced with more efficient ways. Reports predict that by 2022, the adoption rate of AI-based approach to IT operations in large enterprises will go up to 40 percent as opposed to the current 5 percent, and we second this thought.
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