11Dec - by aiuniverse - 0 - In Artificial Intelligence


How can IT organizations make sure they’re equipped to resolve operational issues quickly and drive better business outcomes? With the increasingly vast amounts of data generated by infrastructure and business applications, and teams often working in disconnected silos, managing and improving operations through automation — including monitoring and service desk processes — is essential.

That’s where artificial intelligence for IT operations, or AIOps, comes in. A term coined by Gartner in 2016, AIOps uses analytics and machine learning to aggregate and leverage historical data from a variety of IT operations tools. AIOps platforms can react to issues in real-time, providing intelligent insights that help teams continuously improve core IT functions and prevent future errors.

Most IT operations, particularly since the pandemic, have moved to online processes that gather exponentially increasing data, such as performance monitoring. Any infrastructure problems, at the same time, need to be addressed at ever-increasing speeds. That means that today’s IT environments far exceed human scale and require automation.

AIOps platforms offer IT organizations essential operational agility, as today’s infrastructure challenges need to be handled at the speed of business. By moving data out of silos, IT operations can become more agile – this is particularly beneficial to complex global service and logistics operations with massive data sets.

In addition, AIOps platforms lower costs by reducing dependence on maintaining multiple on-premise solutions, as well as eliminating outsourcing costs. The technology allows organizations to scale infrastructure seamlessly, helping the entire service delivery ecosystem and boosting the customer experience. After all, disruptions in manufacturing production or distribution centers could be devastating, both for customer service and the bottom line. By intelligently automating operations, organizations can boost accuracy, predictability, and ultimately, customer retention.

Taking optimal advantage of AIOps

At the heart of an AIOps platform is big data. That means there is a significant amount of preparation necessary to put the pieces of the AIOps puzzle together to take optimal advantage of the platform. Here are the most important steps:

1. Collect extensive and diverse data. Data serves as the foundation of implementing a successful AIOps effort, so it is essential to understand how the data can be brought together and used effectively. The IT organization needs to collect data from various sources, including on-premise systems, cloud platforms, and applications. Ultimately, the data is stored in a centralized data “lake.” An AIOps platform does just that, enabling better decision-making and more meaningful analysis that is quick and thorough, thanks to the use of AI.

2. Segregate data into meaningful categories. As data is ingested, it needs to be restructured based on the organization’s operational needs for the right AIOps use case. This is important both for historical data as well as real-time ingested data. Depending on the type of use case, categories can be defined that align with business rules. For example, for a pharmaceutical device manufacturing company, meaningful categories include equipment health data, device efficiency data, and environmental factors.

3. Apply AIOps machine learning to initial big data test cases. Any transformation initiative benefits from starting small. The same is true for AIOps efforts: begin by capturing knowledge, applying its machine learning capabilities to limited test cases, and iterating from there.

4. Improve prediction accuracy with measurement and feedback. Once the AIOps platform knows the data pattern, it can intelligently predict what comes next, including from real-time data. The organization can test and measure, supplying feedback to the model, to improve predictions. The AIOps platform can apply logic to the segregated data and design, to define the next best action. It uses historical data and learns from new data to continuously improve and achieve more accurate decision-making. Ultimately, it’s about building a continuous feedback and improvement cycle.

The Journey to AIOps platforms

Gartner predicts that large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023. That trend is only escalating post-pandemic, as the IT landscape moves swiftly towards online services and operations. Analyzing data and intelligently automating operations based on that data will help organizations achieve their next level of success.

We are having a variety of conversations with clients who all want to know whether it takes a great deal of work to enable AIOps. The answer is no: IT organizations already have the data and can extract it. It simply requires the right AIOps platform and development partner as well as a defined use case based on a desired business benefit.

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