As Deloitte has put it, data is “the new gold.” Innovative IoT (internet of things) devices seem to arrive on the market daily, and the amount of data generated by these devices is exploding. Data holds massive power, and when utilized correctly, it can be extremely valuable for enterprises—both for improving business operations, and for improving IT operations. However, getting to that place where data is useful is a journey.
We see AI all around us and interact with it daily. As more and more enterprises figure out how to harness the data in their systems, the process is becoming increasingly easier and simpler. Data collection is the first part of the journey and is reasonably straightforward. But once we have collected all of the data, what do we do with it? How do we make sense of it all? How do you locate the specific information you are looking for in a data pile that rises as high as the sky?
Generative AI promises to make life dramatically easier on all of these fronts, across the enterprise. I’ll focus here on what genAI can do for observability, devops, and IT teams.
Overwhelming amounts of cryptic data
Deloitte predicts that by 2025 our global data volume will reach 175 zettabytes, an increase of 55 zettabytes from where we currently stand. These overwhelming numbers can cause significant headaches for IT leaders as machine data can be cryptic and challenging to sift through.
Unfortunately, parsing this data is not as easy as reading a textbook, a magazine, or an article written by a human being. Often, when attempting to analyze machine-generated operations data, IT teams are faced with many unknowns—keywords, acronyms, numbers, codes—and need help knowing where to begin. I call these situations knowledge gaps. Like most people looking for answers, developers will turn to Google or other search engines to fill these knowledge gaps, which is time-consuming and unreliable.
Imagine how much better it would be if these knowledge gaps were quickly filled using generative AI. Generative AI has the potential to reduce toil for IT professionals by simplifying data and making it easily consumable.
How generative AI fills knowledge gaps
Another word for generative AI should be “simplification” because that is what it’s all about. However, for generative AI to work its magic, it must be set up for success. Enterprises must strategically utilize generative AI within their systems; it cannot be overbearing or scary. I believe the best way to use generative AI is by keeping it as simple as possible and invisible to the end user. When implemented correctly, genAI should seamlessly blend into the workflow. The goal is for generative AI to reduce toil, not add additional stress, so making it easy to navigate is imperative.
When working with generative AI, context must be provided. Without context, AI is useless—similar to receiving ChatGPT information that only dates back to 2021. It’s great to have access to mountains of data, but if AI does not have the proper context to sift through the data and find what you need, then the data will be useless and the AI will be irrelevant.
With the relevant context, generative AI can fill knowledge gaps in minutes, sift through hundreds of zettabytes in seconds, and provide fundamental information for IT and operations teams.
Generative AI in the real world
We see generative AI used in the observability space throughout many industries, especially regarding compliance. Let’s look at healthcare, an industry where you must comply with HIPAA. You are dealing with sensitive information, generating tons of data from multiple servers, and you must annotate the data with compliance tags. An IT team might see a tag that says, “X is impacting 10.5.34 from GDPR…” The IT team may not even know what 10.5.34 means. This is a knowledge gap—something that can very quickly be fulfilled by having generative AI right there to quickly tell you, “X event happened, and the GDPR compliance that you’re trying to meet by detecting this event is Y…” Now, the previously unknown data has turned into something that is human readable.
Another use case is transportation. Imagine you’re running an application that’s gathering information about flights coming into an airport. A machine-generated view of that will include flight codes and airport codes. Now let’s say you want to understand what a flight code means or what an airport code means. Traditionally, you would use a search engine to inquire about specific flight or airport codes. Which city is the flight coming from? Where is the flight going next? These machine attributes are hard to read for a developer wanting to build a system that gathers all of this machine data using these machine tags. It is challenging to understand acronyms and numbers. Generative AI converts these acronyms and numbers into human-readable information that anybody can understand, making these systems more valuable for the average user.
These examples show the kinds of toil traditionally solved using search engines, knowledge boards, or repositories, taking hours to sort through large amounts of information. They are now solved with generative AI in a fraction of the time. This is a huge win for most enterprises, enabling self-service access to complex systems within the organization. This is empowering for organizations and their IT teams.
A more intelligent approach to data
Generative AI is still evolving at a rapid pace, and enterprises are still learning how to implement it into their data management systems. At Apica, we recently rolled out a generative AI assistant because, like most enterprises, our customers were looking to reduce the time and energy spent managing the massive amounts of incoming data.
While I currently believe that a generative AI assistant is the best way to use AI within data management, I’m not going to make any bets that this is the only way to do it. One thing I know for sure is that generative AI will not replace humans, but it will most definitely replace human toil.
Ranjan Parthasarathy is chief strategy officer for Apica, where he explores how generative AI can enhance observability, specifically using contextualized data to transform how devops and IT ops teams interact with their data. He was the founder of Logiq.ai, recently acquired by Apica.
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