Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
How can IT organizations make sure they’re equipped to resolve operational issues quickly and drive better business outcomes? That’s where artificial intelligence for IT operations, or AIOps, comes in. 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.
AIOps, a term coined by Gartner Research in 2016, refers to the use of 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 coronavirus pandemic, have migrated to online processes (for example, performance monitoring) that gather ever-increasing amounts of data. At the same time, organizations are under mounting pressure to immediately address any infrastructure problems that arise. In a nutshell, the demands and requirements of today’s IT environments far exceed humans’ abilities to identify, act on information and react to issues at scale and speed. To ensure success, these processes require automation.
AIOps platforms offer IT organizations this essential operational agility. By moving data out of silos, increasing scalability and speed, 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 multiple on-premise solutions, as well as eliminating outsourcing costs. The technology allows organizations to scale infrastructure seamlessly, helping the entire service delivery ecosystem run more effectively and efficiently, and thus improving 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.
How to Take 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 so your organization can best take advantage of the platform. Here are the most important steps:
Collect extensive and diverse data. Data serves as the foundation of implementing a successful AIOps effort, so it is essential to understand how data can be brought together and used effectively. IT organizations should collect data from various sources, including on-premise systems, cloud platforms and applications. Ultimately, the data should be 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.
Segregate data into meaningful categories. As data is ingested, it needs to be restructured based on the organization’s operational needs. This is important both for historical data as well as real-time ingested data. Depending on the AIOps use case, you should define categories that align with business rules. For example, for a pharmaceutical device manufacturing company, meaningful categories could include equipment health data, device efficiency data and environmental factors.
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 machine learning capabilities to limited test cases and iterating at greater scale from there.
Improve prediction accuracy with measurement and feedback. Once the AIOps platform knows the data pattern(s), it can intelligently predict what comes next, including extrapolation from real-time data. The organization can test and measure; supplying feedback to the model to improve predictions. The AIOps platform can then apply logic to the segregated data and design to define the next, best action. AIOps platforms use historical data, as well as learning from new data, to continuously improve and achieve more accurate decision-making. Ultimately, AIOps is about building a continuous feedback and improvement cycle.
The Journey to AIOps platforms
Gartner predicts that large enterprises’ use of AIOps, and other 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 migrates even more to online services and operations. Analyzing data, and intelligently automating operations based on that data, will help organizations achieve greater success.
Finally, it’s important to understand that it doesn’t take a lot of work to successfully enable AIOps. IT organizations already have the data and the means to extract it. A successful AIOps initiative simply requires the right AIOps platform, an experienced development partner and a defined use case based on a desired business benefit.
