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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These processes can become convoluted and outdated quickly.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!

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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

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

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...