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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These processes can become convoluted and outdated quickly.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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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 ...

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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

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

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 ...

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 ...