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Why Visibility is Critical for DevOps Teams

Michael Segal

According to recent reports, the majority of businesses now use cloud computing in one form or another. Innovation and agility are key to success in today's fast-moving, competitive environment, and with many legacy systems no longer able to keep up with the demands of digital transformation, it's little surprise that more than two thirds of enterprise workloads are now reported to be in the cloud.

As businesses look to capitalize on the benefits offered by the cloud, we've seen the rise of the DevOps practice which, in common with the cloud, offers businesses the advantages of greater agility, speed, quality and efficiency.

However, achieving this agility requires end-to-end visibility based on continuous monitoring of the developed applications as part of the software development life cycle (SDLC) in order to achieve a common situational awareness; without which, DevOps teams can find themselves hindered, causing innovation to stall.

Reaching Maturity

In simple terms, the role of DevOps is to produce new software, based on business needs, at very high speed, and of the highest possible quality of user experience given the constraints under which they operate. A continuous delivery pipeline, for example, could mean as many as several releases a day, each of which requires code to be built, tested, and integrated before being deployed, and each of which must deliver a responsive, reliable service with virtually no downtime.

The functionality of a DevOps team can be impacted by the level of its maturity, however, which can be influenced by two factors. The first of these is the cultural dimension; the team's ability to collaborate effectively, owning the overall DevOps mission as opposed to meeting specific objectives of the individual teams that comprise the whole, such as Operations or QA.

Before mastering this aspect, developers tend to be focused on the speed of software delivery, QA tends to focus on testing predefined use cases, while Operations concentrates on monitoring the production environment. Each team is focused on its own domain and is often siloed off from the others, without utilizing an effective feedback loop and establishing a common situational awareness.

At this stage of organizational maturity, the DevOps team will be focused more on accelerating and optimizing the effectiveness of its individual domains using technologies such as version control management, continuous integration, automated testing, automated deployment and configuration management. Increasing DevOps maturity relies on additional technologies for continuous monitoring, improved visibility, telemetry, feedback loops, and situational awareness. Achieving this, however, can prove challenging.

Visibility and Insights

Consider a situation in which developers build the code for an application, QA tests it based on common use cases, and then the release manager oversees its integration into the mainline and its subsequent deployment. At this point, Operations might find a problem that only manifests at scale, requiring Dev teams to quickly pinpoint the issue and rectify it by developing new code that functions correctly in the product environment.

It's here, then, that visibility is most crucial, providing all parties with common situational awareness. Rather than relying on Ops to highlight issues, in this example Dev teams are able instead to look on the system and see the same situation themselves, and thereby better understand the parameters within which they need to work. Doing so will save time and create more effective feedback loops which would enable to adjust the development and QA processes to detect similar issues early on in the SDLC or even prevent them from occurring altogether.

Achieving this level of visibility requires the use of smart data – metadata based on processing and organizing wire data at the point of collection, and optimizing it for analytics at the highest speed and quality. By analyzing every IP packet that traverses the network during a development cycle and beyond – in real time – smart data delivers meaningful and actionable insights, creating a common situational awareness for all teams. This then enables those teams, from developers through QA to IT Operations, to work together within constantly evolving parameters, avoiding any bottlenecks in the feedback loop.

Opportunity for Innovation

Digital transformation, and the role of the cloud within it, are integral to an organization's innovation. With more applications and services being migrated to the cloud, however, a host of new, unprecedented challenges are emerging.

This is particularly true for DevOps teams, charged with producing quality code at speed. To reach the level of maturity at which they can function most efficiently and effectively requires siloes of work to be broken down across the organization to foster a culture of collaboration and continuous communication. The visibility, insight and common situational awareness offered by smart data can help achieve this, freeing up the potential of DevOps, and affording organizations a greater opportunity for innovation.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Why Visibility is Critical for DevOps Teams

Michael Segal

According to recent reports, the majority of businesses now use cloud computing in one form or another. Innovation and agility are key to success in today's fast-moving, competitive environment, and with many legacy systems no longer able to keep up with the demands of digital transformation, it's little surprise that more than two thirds of enterprise workloads are now reported to be in the cloud.

As businesses look to capitalize on the benefits offered by the cloud, we've seen the rise of the DevOps practice which, in common with the cloud, offers businesses the advantages of greater agility, speed, quality and efficiency.

However, achieving this agility requires end-to-end visibility based on continuous monitoring of the developed applications as part of the software development life cycle (SDLC) in order to achieve a common situational awareness; without which, DevOps teams can find themselves hindered, causing innovation to stall.

Reaching Maturity

In simple terms, the role of DevOps is to produce new software, based on business needs, at very high speed, and of the highest possible quality of user experience given the constraints under which they operate. A continuous delivery pipeline, for example, could mean as many as several releases a day, each of which requires code to be built, tested, and integrated before being deployed, and each of which must deliver a responsive, reliable service with virtually no downtime.

The functionality of a DevOps team can be impacted by the level of its maturity, however, which can be influenced by two factors. The first of these is the cultural dimension; the team's ability to collaborate effectively, owning the overall DevOps mission as opposed to meeting specific objectives of the individual teams that comprise the whole, such as Operations or QA.

Before mastering this aspect, developers tend to be focused on the speed of software delivery, QA tends to focus on testing predefined use cases, while Operations concentrates on monitoring the production environment. Each team is focused on its own domain and is often siloed off from the others, without utilizing an effective feedback loop and establishing a common situational awareness.

At this stage of organizational maturity, the DevOps team will be focused more on accelerating and optimizing the effectiveness of its individual domains using technologies such as version control management, continuous integration, automated testing, automated deployment and configuration management. Increasing DevOps maturity relies on additional technologies for continuous monitoring, improved visibility, telemetry, feedback loops, and situational awareness. Achieving this, however, can prove challenging.

Visibility and Insights

Consider a situation in which developers build the code for an application, QA tests it based on common use cases, and then the release manager oversees its integration into the mainline and its subsequent deployment. At this point, Operations might find a problem that only manifests at scale, requiring Dev teams to quickly pinpoint the issue and rectify it by developing new code that functions correctly in the product environment.

It's here, then, that visibility is most crucial, providing all parties with common situational awareness. Rather than relying on Ops to highlight issues, in this example Dev teams are able instead to look on the system and see the same situation themselves, and thereby better understand the parameters within which they need to work. Doing so will save time and create more effective feedback loops which would enable to adjust the development and QA processes to detect similar issues early on in the SDLC or even prevent them from occurring altogether.

Achieving this level of visibility requires the use of smart data – metadata based on processing and organizing wire data at the point of collection, and optimizing it for analytics at the highest speed and quality. By analyzing every IP packet that traverses the network during a development cycle and beyond – in real time – smart data delivers meaningful and actionable insights, creating a common situational awareness for all teams. This then enables those teams, from developers through QA to IT Operations, to work together within constantly evolving parameters, avoiding any bottlenecks in the feedback loop.

Opportunity for Innovation

Digital transformation, and the role of the cloud within it, are integral to an organization's innovation. With more applications and services being migrated to the cloud, however, a host of new, unprecedented challenges are emerging.

This is particularly true for DevOps teams, charged with producing quality code at speed. To reach the level of maturity at which they can function most efficiently and effectively requires siloes of work to be broken down across the organization to foster a culture of collaboration and continuous communication. The visibility, insight and common situational awareness offered by smart data can help achieve this, freeing up the potential of DevOps, and affording organizations a greater opportunity for innovation.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...