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

Related Links

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Related Vendors

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How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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