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Taking Action Against the Data You Have

Adam Frank
Moogsoft

Move fast and break things: A phrase that has been a rallying cry for many SREs and DevOps practitioners. After all, these teams are charged with delivering rapid and unceasing innovation to wow customers and keep pace with competitors.

But today's society doesn't tolerate broken things (aka downtime). So, what if you can move fast and not break things? Or at least, move fast and rapidly identify or even predict broken things?

It's high time to rethink the old rallying cry, and with AI and observability working in tandem, it's possible.

Applying AI to observability data turns mountains of telemetry data, regardless of the relative size of the mountain to your business, into actionable information, playing a critical role in how quickly an organization can innovate. Let's explore why these solutions are so essential.

How AI and Observability Converge to Help

DevOps practitioners strive to provide superior digital experiences by continuously delivering and integrating features, fixes and functionalities for immersive experiences. This constant behind-the-scenes innovation is at odds with customers' expectations for 100% availability. Today's consumer expects to purchase, transact, interact and access on-demand digital services with zero downtime.

SREs and DevOps teams need AI-driven observability to monitor system performance or innovation and productivity plummets. Teams spend entire days managing alerts and fighting fires. And even with an infinite amount of time to shift through data, today's distributed IT systems, virtual computing and ephemeral machines are simply too complex and interdependent for the human mind to monitor manually.

Only automated intelligence can constantly verify and restore digital products and services in modern IT architectures. And only AI and ML can create a continual learning cycle, understanding more from the data gathered across infrastructures, applications and services. These insights build more system reliability, but because nothing can fully protect against outages happening, they also allow IT teams to resolve incidents rapidly when they do occur.

SREs, DevOps Practitioners ... and Astronauts?

When incidents arise and systems fail, the stakes are high for SREs and DevOps practitioners to right the ship — and fast. For every minute of downtime, businesses suffer exponential losses, like tanking stocks, tarnished reputations and disillusioned customers. But teams also need to remain cool under mounting pressure to work efficiently.

How? In one word: knowledge.

I recently read Chirs Hadfield's book An Astronaut's Guide to Life on Earth. Although I only wish people thought of IT teams as superheroes, the author's advice resonated:

"People tend to think astronauts have the courage of a superhero — or maybe the emotional range of a robot. But in order to stay calm in a high-stress, high-stakes situation, all you really need is knowledge."

Under pressure to tackle a challenging system failure, knowledge also allows SREs and DevOps teams to overcome emotions and find solutions. And that's precisely where intelligent observability comes in: it gathers data produced from apps and services, adds context and turns volumes of information into actionable knowledge.

Automate the Cognitive Load

The benefits of automation don't stop at speedy fixes. Automating the toil out of observability provides economic value by freeing teams to accelerate innovation and provide measurable value. Teams can focus more on development and less on ops with little mundane work to accomplish.

Intelligent observability also reduces stress and burnout prevalent among IT teams. AI-driven observability platforms reduce alert noise and focus teams on the incidents that matter for triage and remediation.

And, for businesses, intelligent observability assures the quality of the customer experience, which is ultimately what matters most.

Welcome to the era of move fast and break things infrequently. Although less catchy than the original, it's more reflective of the automated intelligence today's software-defined world needs to deliver superior customer experiences. Every business's revenue, reputation and growth depend on it.

Adam Frank is VP, Product & Design, at Moogsoft

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Taking Action Against the Data You Have

Adam Frank
Moogsoft

Move fast and break things: A phrase that has been a rallying cry for many SREs and DevOps practitioners. After all, these teams are charged with delivering rapid and unceasing innovation to wow customers and keep pace with competitors.

But today's society doesn't tolerate broken things (aka downtime). So, what if you can move fast and not break things? Or at least, move fast and rapidly identify or even predict broken things?

It's high time to rethink the old rallying cry, and with AI and observability working in tandem, it's possible.

Applying AI to observability data turns mountains of telemetry data, regardless of the relative size of the mountain to your business, into actionable information, playing a critical role in how quickly an organization can innovate. Let's explore why these solutions are so essential.

How AI and Observability Converge to Help

DevOps practitioners strive to provide superior digital experiences by continuously delivering and integrating features, fixes and functionalities for immersive experiences. This constant behind-the-scenes innovation is at odds with customers' expectations for 100% availability. Today's consumer expects to purchase, transact, interact and access on-demand digital services with zero downtime.

SREs and DevOps teams need AI-driven observability to monitor system performance or innovation and productivity plummets. Teams spend entire days managing alerts and fighting fires. And even with an infinite amount of time to shift through data, today's distributed IT systems, virtual computing and ephemeral machines are simply too complex and interdependent for the human mind to monitor manually.

Only automated intelligence can constantly verify and restore digital products and services in modern IT architectures. And only AI and ML can create a continual learning cycle, understanding more from the data gathered across infrastructures, applications and services. These insights build more system reliability, but because nothing can fully protect against outages happening, they also allow IT teams to resolve incidents rapidly when they do occur.

SREs, DevOps Practitioners ... and Astronauts?

When incidents arise and systems fail, the stakes are high for SREs and DevOps practitioners to right the ship — and fast. For every minute of downtime, businesses suffer exponential losses, like tanking stocks, tarnished reputations and disillusioned customers. But teams also need to remain cool under mounting pressure to work efficiently.

How? In one word: knowledge.

I recently read Chirs Hadfield's book An Astronaut's Guide to Life on Earth. Although I only wish people thought of IT teams as superheroes, the author's advice resonated:

"People tend to think astronauts have the courage of a superhero — or maybe the emotional range of a robot. But in order to stay calm in a high-stress, high-stakes situation, all you really need is knowledge."

Under pressure to tackle a challenging system failure, knowledge also allows SREs and DevOps teams to overcome emotions and find solutions. And that's precisely where intelligent observability comes in: it gathers data produced from apps and services, adds context and turns volumes of information into actionable knowledge.

Automate the Cognitive Load

The benefits of automation don't stop at speedy fixes. Automating the toil out of observability provides economic value by freeing teams to accelerate innovation and provide measurable value. Teams can focus more on development and less on ops with little mundane work to accomplish.

Intelligent observability also reduces stress and burnout prevalent among IT teams. AI-driven observability platforms reduce alert noise and focus teams on the incidents that matter for triage and remediation.

And, for businesses, intelligent observability assures the quality of the customer experience, which is ultimately what matters most.

Welcome to the era of move fast and break things infrequently. Although less catchy than the original, it's more reflective of the automated intelligence today's software-defined world needs to deliver superior customer experiences. Every business's revenue, reputation and growth depend on it.

Adam Frank is VP, Product & Design, at Moogsoft

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Industry experts offer predictions on how AI will evolve and impact technology and business in 2025. Part 3 covers AI's impact on employees and their roles ...

Industry experts offer predictions on how AI will evolve and impact technology and business in 2025. Part 2 covers the challenges presented by AI, as well as solutions to those problems ...

In the final part of APMdigest's 2025 Predictions Series, industry experts offer predictions on how AI will evolve and impact technology and business in 2025 ...

E-commerce is set to skyrocket with a 9% rise over the next few years ... To thrive in this competitive environment, retailers must identify digital resilience as their top priority. In a world where savvy shoppers expect 24/7 access to online deals and experiences, any unexpected downtime to digital services can lead to significant financial losses, damage to brand reputation, abandoned carts with designer shoes, and additional issues ...

Efficiency is a highly-desirable objective in business ... We're seeing this scenario play out in enterprises around the world as they continue to struggle with infrastructures and remote work models with an eye toward operational efficiencies. In contrast to that goal, a recent Broadcom survey of global IT and network professionals found widespread adoption of these strategies is making the network more complex and hampering observability, leading to uptime, performance and security issues. Let's look more closely at these challenges ...

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The pressure on IT teams has never been greater. As data environments grow increasingly complex, resource shortages are emerging as a major obstacle for IT leaders striving to meet the demands of modern infrastructure management ... According to DataStrike's newly released 2025 Data Infrastructure Survey Report, more than half (54%) of IT leaders cite resource limitations as a top challenge, highlighting a growing trend toward outsourcing as a solution ...

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