Artificial Intelligence and AIOps may be the key to understanding today’s world of exploding data

The Library of Babel — a short story by Jorge Luis Borges published in 1941 — might not be on everyone’s reading list. Nor is it likely to make the bestseller chart any time soon. But it does have a bit of a following, especially among those interested in computer science and information theory.

For those not familiar with the story, it’s set in a vast and infinite library that contains every possible combination of letters and punctuation marks. 

As such, it contains a library of unimaginable size — stacked with every possible book possible with a near-infinite combination — and includes those filled with gibberish to those containing every meaningful piece of literature ever written. 

Faced with so much information, it’s up to the librarians to find the book that contains life's true and complete meaning. Faced with so much information, it’s no wonder the librarians of Babel started to go mad.

While the story raises questions about the pursuit of knowledge, the nature of truth, and the human desire to find meaning in an overwhelming and chaotic world, it holds something more cerebral for computer scientists. 

For them, the Library of Babel touches on concepts such as infinite data storage, algorithmic search and retrieval, data compression, and meaningful data's importance. 

Sorting and managing data is a life’s work

If you think the link is a tad tenuous, imagine if the letters and punctuation in the library were, instead, an ever-expanding collection of ones and zeros — the language not of words, but of computing. 

Each unique combination would represent something different — an application, a photograph, a song, a contract, a book. Think of it that way and the combinations are endless.

That’s why computer scientists hold the short story in such high regard. Faced with this never-ending flow of data, how do they — computer scientists, not librarians — manage, process and store such a mind-boggling amount of information every day? Where do they even start? 

Thankfully, unlike Babel’s characters painstakingly going through each book by hand, at least computer scientists can use tools to help them get the job done. And in today’s world, increasingly that means using artificial intelligence (AI) and machine learning (ML).

How AI and ML are transforming IT

AI and ML are powerful solutions helping to transform how IT professionals manage and analyse data to optimise performance, improve business outcomes, and mitigate security risks. By automating such tasks, these technologies are able to crunch massive amounts of data that would otherwise be impossible to process by hand. 

And when you consider real-world computing environments, the need for AI and ML becomes even more acute since many computer systems run in multiple clouds and rely on hundreds of applications to get work done. 

In the real world, it is only with AI and ML doing the heavy lifting that these systems can predict and prevent application or system crashes or outages. How? By engaging in constant system surveillance and automatically analysing key performance metrics. 

Again, there is an analogy here with the library. Imagine being one of the librarians tasked with reading all the books to find that one elusive tome. Now, imagine the difference it would make to your workload if you had a virtual assistant that was able to read and analyse all the books for you. 

This would free you up to look at only those books that were flagged to be of interest. And if the automation of the process left you twiddling your thumbs, you could decide to do something else. Such as redecorating the breakroom. Or simply leave the confines of the bibliotheque and, instead, enjoy a walk in the sunshine. 

AI and ML on their own are not enough

Of course, AI and ML aren’t a solution in their own right. Despite their ability to sift through and make sense of huge amounts of data, they need to operate within a framework to make them operational. This is where Artificial Intelligence for IT Operations — or AIOps — comes in. 

It’s a technology practice that combines AI and ML with traditional IT operations to enhance and automate various aspects of managing and monitoring IT systems and infrastructure. Although it’s a relatively new term, the role of AIOps is to improve the efficiency, agility, and reliability of IT operations. How? By leveraging AI and ML by analysing data, detecting patterns, making predictions, and automating routine tasks.

AIOps is particularly valuable in complex and dynamic IT environments, such as cloud-based systems, microservices architectures, and hybrid infrastructures, where traditional monitoring and management approaches may struggle to keep pace with the scale and complexity of modern technology ecosystems. 

It also helps IT teams streamline their operations, reduce downtime, and deliver a more reliable and responsive IT service to users and customers.

AIOps enables IT teams to gain end-to-end visibility – regardless of a company’s infrastructure or where they may be on their digital transformation journey – and reduce the time spent troubleshooting while improving system reliability.

And that’s important because the explosion of data presents significant challenges for IT pros related to managing and analysing today’s complicated IT environments. 

The good news, though, is that AI, ML, and AIOps are transforming how IT professionals work, enabling them to automate tasks, detect security threats, optimise performance, and make better decisions based on data analysis.

However, the use of AI, ML and AIOps is not a green light for complete and total automation. Organisations looking to implement these tools must make sure that someone — a person — is able to set the parameters and provide the necessary oversight. 

Touch in on the scenario posed in the Library of Babel, although AI tools could search through every possible book far faster than a human could, they still wouldn’t be able to “think” for themselves like humans can – a critically important element.

AIOps isn’t a replacement for the work needing to be done. It’s an aid to understanding the barrage of information we receive each and every day.