Skip to main content

A Full Picture: Adding Business Context to IT's Puzzle Pieces

Matthew Carr

Think about the first puzzle you ever built. For me, it was a majestic picture of the Big Ben. There were hundreds of pieces scattered around, each with a place for me to find based on the picture on the box. Even with the picture as a guide, it was a daunting task that took hours. Now imagine building that puzzle without having the photo as a guide. It changes the game entirely. Today's IT teams are facing a similar challenge with managing hybrid-centric environments.

As the IT world has become more hybrid-centric, the big picture has gotten lost or is not known holistically by most trying to place their IT puzzle piece. Since hybrid is "the new normal," outdated operations will no longer suffice, and current IT management systems must be modernized to succeed in this new hybrid world where the big picture is always changing.

As machine and IT event data continue to become more complex – and massively abundant – IT departments are trying to manage a plethora of information. In many cases, IT departments – as well as business practice groups – manage IT data by silo, each concerned solely about their particular piece of the puzzle, and not focusing on the whole picture required to understand where their piece fits.

Placing IT Puzzle Pieces Without Context is Guesswork

Over the years, it has been common practice for large organizations to have IT teams working independently, where each individual team uses its own tools to monitor and manage specific data components. In other words, the networking team works independently from the help desk team, not sharing tools or information, for a very insular and antiquated approach.

Compounding the problem, organizations generally have an immense variety of data sources – sometimes as many as a dozen or more – including datacenter, application, end-user monitoring systems, and IT service management system. Each component (and team) – or puzzle piece – exists in its own silo, accessing only a limited-view of a small portion of the enterprise.

There were a variety of tools that worked reasonably well before companies evolved to a hybrid world. For instance, legacy monitoring tools were helpful in examining the components and applications within the datacenter, though were not designed to provide a more comprehensive view. Then came EUEM (End-User Experience Monitoring) tools, which were more proactive, but couldn't effectively provide critical information about the root-cause of issues. The benefit of domain monitoring tools was pinpointing root-cause, but offered limited functionality for other IT needs.

The combination of people working in silos, tools used with limited scope and ability, plus a hybrid environment all resulted in a very challenging equation. Now, companies are faced with increased data across multiple, unconnected platforms. Thus, teams working independently don't have any understanding of where their piece of the puzzle fits into the landscape, and are limited without any big-picture context. And tools that can only serve limited functions are inefficient, ineffective, and make it overly complicated to get results.

Picture an organization whose IT teams are trying to work in different departments, with information buried in different monitoring systems, who are monitoring different components and infrastructures. Using this disjointed approach, even if all monitoring systems report healthy, it is still no guarantee that they actually are. In this scenario, no one has a clear view of the entire enterprise, and no one is seeing significant problems – or potential problems – in any kind of structural context. IT teams are limited in what they can see or do, as they're operating with just pieces of information – a component in a silo – rather seeing the complete picture.

Reassembling an IT Puzzle That Changes Daily is Impossible

How can these IT professionals be expected to excel in this hybrid world when they're working in such compartmentalized and complicated conditions? It's a futile effort.

Instead, enterprises need to shift their approach from the inefficient, compartmentalized systems they're currently using, and become more holistic, comprehensive, and streamlined. Understanding how the IT landscape or picture changes on a daily basis is important to knowing how one's piece of the IT puzzle fits today. Providing real-time updates to the IT landscape so everyone is working to restore the same puzzle is important for achieving service levels required by any business.

Additionally, it is crucial that they adjust their mindset to be more proactive – anticipating and resolving potential issues before they're disruptive – rather than being reactive, and only acting after an end-user reports an issue.

It seems pretty straight-forward to say that proactive, comprehensive, swift, efficient and predictive processes are superior to outdated reactive, slow and compartmentalized systems. Yet, so many organizations are trapped in outdated models, unable to get a firm handle on their processes, let alone the IT puzzle pieces they're attempting to manage. It is all so complicated, making it difficult to navigate the variety of options for new monitoring tools that can handle an organization's IT management needs.

Self-Updating IT Landscape Pictures Keep Every Silo's Puzzle Piece in Place

Fortunately, more advanced IT environment discovery and monitoring solutions are becoming available. Savvy businesses are deploying systems that are change-aware and automatically discover and update the "big picture" of their inter-dependent IT environment, providing each team with a transparent view into how their piece contributes to the overall business. Moreover, they're implementing unified processes, investing in 360-degree views that unite their silos, and using the appropriate tools to analyze and manage their IT puzzle pieces. IT and business teams must work more holistically and collaboratively across departments so they're all building the same – ever changing – picture comprised of hundreds of puzzle pieces.

Now, thankfully, there are modern solutions that help organizations transition from the silo approach to a holistic environment no matter how complex or distributed one's hybrid environment becomes. Think about providing every person on your team with an always up-to-date picture for their IT landscape puzzle with a guide depicting each piece's exact place in that puzzle. This is available today. Using these solutions allows companies to elevate their systems, processes, and approaches to operate in a more productive, efficient, innovative, and successful manner.

Matthew Carr is Business Development Manager at Savision.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.

A Full Picture: Adding Business Context to IT's Puzzle Pieces

Matthew Carr

Think about the first puzzle you ever built. For me, it was a majestic picture of the Big Ben. There were hundreds of pieces scattered around, each with a place for me to find based on the picture on the box. Even with the picture as a guide, it was a daunting task that took hours. Now imagine building that puzzle without having the photo as a guide. It changes the game entirely. Today's IT teams are facing a similar challenge with managing hybrid-centric environments.

As the IT world has become more hybrid-centric, the big picture has gotten lost or is not known holistically by most trying to place their IT puzzle piece. Since hybrid is "the new normal," outdated operations will no longer suffice, and current IT management systems must be modernized to succeed in this new hybrid world where the big picture is always changing.

As machine and IT event data continue to become more complex – and massively abundant – IT departments are trying to manage a plethora of information. In many cases, IT departments – as well as business practice groups – manage IT data by silo, each concerned solely about their particular piece of the puzzle, and not focusing on the whole picture required to understand where their piece fits.

Placing IT Puzzle Pieces Without Context is Guesswork

Over the years, it has been common practice for large organizations to have IT teams working independently, where each individual team uses its own tools to monitor and manage specific data components. In other words, the networking team works independently from the help desk team, not sharing tools or information, for a very insular and antiquated approach.

Compounding the problem, organizations generally have an immense variety of data sources – sometimes as many as a dozen or more – including datacenter, application, end-user monitoring systems, and IT service management system. Each component (and team) – or puzzle piece – exists in its own silo, accessing only a limited-view of a small portion of the enterprise.

There were a variety of tools that worked reasonably well before companies evolved to a hybrid world. For instance, legacy monitoring tools were helpful in examining the components and applications within the datacenter, though were not designed to provide a more comprehensive view. Then came EUEM (End-User Experience Monitoring) tools, which were more proactive, but couldn't effectively provide critical information about the root-cause of issues. The benefit of domain monitoring tools was pinpointing root-cause, but offered limited functionality for other IT needs.

The combination of people working in silos, tools used with limited scope and ability, plus a hybrid environment all resulted in a very challenging equation. Now, companies are faced with increased data across multiple, unconnected platforms. Thus, teams working independently don't have any understanding of where their piece of the puzzle fits into the landscape, and are limited without any big-picture context. And tools that can only serve limited functions are inefficient, ineffective, and make it overly complicated to get results.

Picture an organization whose IT teams are trying to work in different departments, with information buried in different monitoring systems, who are monitoring different components and infrastructures. Using this disjointed approach, even if all monitoring systems report healthy, it is still no guarantee that they actually are. In this scenario, no one has a clear view of the entire enterprise, and no one is seeing significant problems – or potential problems – in any kind of structural context. IT teams are limited in what they can see or do, as they're operating with just pieces of information – a component in a silo – rather seeing the complete picture.

Reassembling an IT Puzzle That Changes Daily is Impossible

How can these IT professionals be expected to excel in this hybrid world when they're working in such compartmentalized and complicated conditions? It's a futile effort.

Instead, enterprises need to shift their approach from the inefficient, compartmentalized systems they're currently using, and become more holistic, comprehensive, and streamlined. Understanding how the IT landscape or picture changes on a daily basis is important to knowing how one's piece of the IT puzzle fits today. Providing real-time updates to the IT landscape so everyone is working to restore the same puzzle is important for achieving service levels required by any business.

Additionally, it is crucial that they adjust their mindset to be more proactive – anticipating and resolving potential issues before they're disruptive – rather than being reactive, and only acting after an end-user reports an issue.

It seems pretty straight-forward to say that proactive, comprehensive, swift, efficient and predictive processes are superior to outdated reactive, slow and compartmentalized systems. Yet, so many organizations are trapped in outdated models, unable to get a firm handle on their processes, let alone the IT puzzle pieces they're attempting to manage. It is all so complicated, making it difficult to navigate the variety of options for new monitoring tools that can handle an organization's IT management needs.

Self-Updating IT Landscape Pictures Keep Every Silo's Puzzle Piece in Place

Fortunately, more advanced IT environment discovery and monitoring solutions are becoming available. Savvy businesses are deploying systems that are change-aware and automatically discover and update the "big picture" of their inter-dependent IT environment, providing each team with a transparent view into how their piece contributes to the overall business. Moreover, they're implementing unified processes, investing in 360-degree views that unite their silos, and using the appropriate tools to analyze and manage their IT puzzle pieces. IT and business teams must work more holistically and collaboratively across departments so they're all building the same – ever changing – picture comprised of hundreds of puzzle pieces.

Now, thankfully, there are modern solutions that help organizations transition from the silo approach to a holistic environment no matter how complex or distributed one's hybrid environment becomes. Think about providing every person on your team with an always up-to-date picture for their IT landscape puzzle with a guide depicting each piece's exact place in that puzzle. This is available today. Using these solutions allows companies to elevate their systems, processes, and approaches to operate in a more productive, efficient, innovative, and successful manner.

Matthew Carr is Business Development Manager at Savision.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.