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I spoke with Ian Smith, Field CTO at Chronosphere, about cloud native observability, an emerging tech that’s gaining a lot of attention in the enterprise. Smith spoke in-depth about trends and best practices in observability.
An observability solution monitors a company’s IT infrastructure by constantly monitoring its outputs. In most cases, the most important outputs are those that track the performance of the core applications that enable the company to keep running.
Historically, this monitoring task has been handled by humans, and still is, largely. Yet as tech infrastructure grows ever more complex, humans need ever more help to keep up. Hence the growth of observability solutions.
In fact, observability is increasingly important as IT infrastructure grows increasingly complex – and companies know that.
Chronosphere focuses on cloud native observability, which is well-suited to contemporary IT deployments that include elements like microservices and multicloud deployments.
See below for a podcast and video version of the interview.
What’s Driving Observability Adoption
As recently as 2021, just 61 percent of companies had a centralized observability solution. By 2023, that number had risen to about 70 percent. So certainly observability has room for growth – and yet there’s still some skepticism about this emerging tech.
As Smith noted, the doubting companies ask, “‘well, what am I getting beyond the high-level marketing message?’”
The answer, ultimately, is that observability is far more than cobbling together an array of interoperating tools. What drives companies to adopt this technology is how observability can assist engineering to facilitate what a business needs, and also – especially – observability’s ability to help control costs, including the expense of multicloud computing.
It’s also about growth. Smith has heard companies say, “’When we previously settled on our observability tooling, we were a much smaller company. We had a really big focus on observability, used by our most senior resources – and they drove the evaluation. Now we have maybe 10, 15 times more engineers and it’s a very broad spread of experiences.”
Observability Strategy
Everything in enterprise IT requires planning, but observability, due to its complexity, requires truly deliberate planning.
A company needs to understand, Smith noted, “Who’s using [the observability tool], what are they using it for, how much are they utilizing it? And be able to compare across those data sets.”
For instance, “Maybe you have some data that’s only used once every three months for a capacity plan, but it’s very, very small in its footprint. But there’s data over here, it’s used every day, for instance for [important] investigations.” It’s essential to know where your data is – and what data is needed at all time to answer essential business questions.
The most important element of creating an observability strategy is deciding precisely what your goal is – and agreeing on that goal across the company.
So ask, “What is the problem we’re actually trying to solve?” Smith said. Some companies aren’t all on the same page. “And so how can you possibly buy something thinking it’s a solution for a problem if you haven’t actually completely defined that [data] problem upfront?
“Maybe it is, for instance, that we need to direct a large portion of observability data into some other area, maybe a data lake because we’ve been abusing our observability tooling. And these are all strategic initiatives that come out of really stepping back and looking at that bigger picture.”
AI and the Future of Observability
There is, in Smith’s view, a major industry hope that that AI can simply swoop down and answer all of the thorny issues involved with monitoring IT infrastructure. While that belief is unrealistic, certainly the growth of AI has major ramifications for IT – particularly due to AI’s assistance with communication between humans and the system.
In short, the future of observability will enable IT admins to simply talk to their systems. “Wouldn’t it be great to be able, in natural language, to really just ask what is going on with this particular part of the system? Then having a way for the observability system to distill down, ‘these are things you should be focusing on, and here’s an explanation for why.’”
This process is a realistic expectation for observability users. “It’s rooted in data and it’s rooted in building up [data] over time and understanding a model of what these things mean.”
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