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Digital Intelligence - Why Traditional APM Tools Aren't Sufficient

Larry Dragich

The need for an improved end-user-experience starts with Digital Intelligence. That means IT Leaders need to understand and make decisions on what is happening within the ecosystem they support.

Digital Intelligence is the ability to perceive information, (i.e. from monitoring tools) and retain it as knowledge (aka. Big Data) to be applied towards adaptive behaviors (i.e. Machine Learning and/or AI) within the environment (e.g. Prod, Dev, etc.).

Although, using disparate monitoring tools to aggregate application and infrastructure metrics for a correlated end-to-end view can be difficult to manage.

Collecting the alerts and events from multiple tool sets creates a lot of noise for the support staff who then need to make decisions and create some type of repeatable processes for their teams to follow.

These processes can become convoluted and outdated quickly. For Example:

I can recall a time when I was leading a new team and we were all in an intense post mortem meeting looking for root cause from a major event that recently occurred.

While reviewing the IT processes that were in place for all the support teams, it came down to a critical process that we thought was missing. That's when one of my peers spoke up and said with conviction, "We already have a process in place for that!"

"…it's FULLY documented, THOROUGHLY understood, and UNIVERSALLY ignored!"

His witty delivery brought levity to the room, and everyone was able to take a deep breath and relax.

If no one is following a critical IT Process, then maybe it's time for a change

Although, when you think about it on a more serious note it does ring true. If no one is following a critical IT Process, then maybe it's time for a change. The process needs to make sense to the team and become something they can benefit from. The same goes for tool adoption.

Today most savvy IT Leaders are striving to partner with the business and attain complete visibility with the critical business applications they support. At a high level they are looking to collect Digital Intelligence about their business applications and the infrastructure it runs on, whether it's in the cloud or on-prem.

However, meaningful metrics can be difficult to obtain without a specific focus on business impact (transactions) and a concise way to collect them. Since the IT processes wrapped around those metrics are just as critical as the technology itself, it's imperative to have a strategy and communicate it frequently.

Digital Intelligence comes from assimilating multiple Application and Infrastructure events into a cross-domain layer designed for proactive rather than reactive IT Management and Planning. It is also about crafting simple and clean IT support processes with predictable outcomes.

When done correctly with the right tool selection and process development, an Enterprise Monitoring solution using Digital Intelligence can become a communication conduit for supporting the Business, Development and Operations.

Although, keep in mind despite what the most advanced technologies can provide, the best processes in place are the ones that are easy to follow and embraced by the teams that need them, not the ones UNIVERSALLY ignored!

Hot Topics

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 ...

Digital Intelligence - Why Traditional APM Tools Aren't Sufficient

Larry Dragich

The need for an improved end-user-experience starts with Digital Intelligence. That means IT Leaders need to understand and make decisions on what is happening within the ecosystem they support.

Digital Intelligence is the ability to perceive information, (i.e. from monitoring tools) and retain it as knowledge (aka. Big Data) to be applied towards adaptive behaviors (i.e. Machine Learning and/or AI) within the environment (e.g. Prod, Dev, etc.).

Although, using disparate monitoring tools to aggregate application and infrastructure metrics for a correlated end-to-end view can be difficult to manage.

Collecting the alerts and events from multiple tool sets creates a lot of noise for the support staff who then need to make decisions and create some type of repeatable processes for their teams to follow.

These processes can become convoluted and outdated quickly. For Example:

I can recall a time when I was leading a new team and we were all in an intense post mortem meeting looking for root cause from a major event that recently occurred.

While reviewing the IT processes that were in place for all the support teams, it came down to a critical process that we thought was missing. That's when one of my peers spoke up and said with conviction, "We already have a process in place for that!"

"…it's FULLY documented, THOROUGHLY understood, and UNIVERSALLY ignored!"

His witty delivery brought levity to the room, and everyone was able to take a deep breath and relax.

If no one is following a critical IT Process, then maybe it's time for a change

Although, when you think about it on a more serious note it does ring true. If no one is following a critical IT Process, then maybe it's time for a change. The process needs to make sense to the team and become something they can benefit from. The same goes for tool adoption.

Today most savvy IT Leaders are striving to partner with the business and attain complete visibility with the critical business applications they support. At a high level they are looking to collect Digital Intelligence about their business applications and the infrastructure it runs on, whether it's in the cloud or on-prem.

However, meaningful metrics can be difficult to obtain without a specific focus on business impact (transactions) and a concise way to collect them. Since the IT processes wrapped around those metrics are just as critical as the technology itself, it's imperative to have a strategy and communicate it frequently.

Digital Intelligence comes from assimilating multiple Application and Infrastructure events into a cross-domain layer designed for proactive rather than reactive IT Management and Planning. It is also about crafting simple and clean IT support processes with predictable outcomes.

When done correctly with the right tool selection and process development, an Enterprise Monitoring solution using Digital Intelligence can become a communication conduit for supporting the Business, Development and Operations.

Although, keep in mind despite what the most advanced technologies can provide, the best processes in place are the ones that are easy to follow and embraced by the teams that need them, not the ones UNIVERSALLY ignored!

Hot Topics

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 ...