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When new intelligence meets new learning

When new intelligence meets new learning

Credit: ID 127010046 © Monkey Business Images | Dreamstime.com

It is a familiar story – new technology creates opportunities for improvement in how operations are done, but it also adds new complexities and pressures that impact IT’s ability to maintain existing operations, recognise and control future developments (and potential downsides), and monitor outcomes for optimum benefit.

Data warehouses become data lakes, clouds (hybrid or otherwise) become standard and multiply, services proliferate, communications spread far and wide (on and off premises), and customers and stakeholders demand more.

IT operations teams are under nonstop pressure to work faster, deliver more, and achieve better results within tight budgets for IT needs, often with reduced IT teams to carry out the work.

Just as there are issues raised by technology, there is probably a technological fix to resolve these issues. But in the real world, all of these are larger business issues, and technology is simply the tool to achieve the best business results.

“IT operations is challenged by the rapid growth in data volumes, generated by IT infrastructure and applications, that must be captured, analysed and acted on,” says Padraig Byrne, Senior Director Analyst at Gartner. “Coupled with the reality that IT operations teams often work in disconnected silos, this makes it challenging to ensure that the most urgent incident at any given time is being addressed.”

As enterprise software expert, Tej Redkar, says: “If you have a team of only data scientists who are working on algorithms or have engineers only trying to build a data lake, you are not going to achieve your goals. You need a combination of the right people who understand the right business problem to solve. I constantly see organisations driving initiatives tied to buzzwords instead of a real business problem.”

IT operations teams need an intelligent monitoring platform that can cut through the noise and provide meaningful insights into the performance of their IT infrastructure to surface issues and proactively resolve them before they negatively impact the business.

Redkar is chief product officer with infrastructure monitoring platform provider LogicMonitor, and he cites the newest way to enhance primary IT functions – including identifying, troubleshooting, resolving availability and performance issues and enabling incident prevention – AIOps.

Gartner describes it as “the application of machine learning (ML) and data science to IT operations problems. AIOps platforms combine big data and ML functionality to enhance and partially replace all primary IT operations functions, including availability and performance monitoring, event correlation and analysis, and IT service management and automation.”

The industry analysis company predicts that large enterprise use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023. By including both large and medium-sized enterprises, you have a burgeoning take-up of 40% adopting AI in just a couple of years.

“The long-term impact of AIOps on IT operations will be transformative,” says Byrne.

Central functions of AIOps include:

●      Ingesting data from multiple sources, agnostic to source or vendor

●      Performing real-time analysis at the point of ingestion

●      Performing historical analysis of stored data

●      Leveraging machine learning

●      Initiating an action or next step based on insights and analytics.

The real power of AIOps lies in its ability to consume and analyse the ever-increasing data generated by IT, and presenting that data in a practical, actionable way. Such data includes infrastructure and application data (e.g. monitoring systems and logs); IT service management (tickets, change controls, asset information); and business systems (robotics process automation).

By automating analysis, AIOps provides the data-validated insight IT teams need to make smarter, faster decisions. AIOps has and will increasingly enable enterprise organisations to reduce costs, optimise resource utilisation and capacity, identify threats and performance anomalies sooner, resolve issues faster and, in general, better understand and act on operational challenges.

As more and more organisations adopt AIOps, its capabilities will evolve into areas such as enhanced prescriptive and predictive functionality, more effective security analytics, and enhanced employee experience.

When it comes to the future of AIOps, Redkar says he’s on the lookout for these trends in 2021:

●      AIOps moving from one data type to multiple data type algorithms;

●      Remote work driving more technology platforms to deploy AI towards detecting problems;

●      AIOps becoming more embedded in observability platforms; and

●      Security and IT operations being better integrated.

The result will be that AIOps platforms’ time-to-value will decrease, with more efficient use of time and budgets to detect richer patterns with ML algorithms trained on vast amounts of data.

“IT leaders are enthusiastic about the promise of applying AI to IT operations,” Bryne says, “but as with moving a large object, it will be necessary to overcome inertia to build velocity. The good news is that AI capabilities are advancing, and more real solutions are becoming available every day.”

Further details on LogicMonitor’s approach to supporting the IT operations landscape can be found at logicmonitor.com/infrastructure-monitoring.

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