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Event Management: Reactive, Proactive or Predictive?

Larry Dragich

Can event management help foster a curiosity for innovative possibilities to make application performance better? Blue-sky thinkers may not want to deal with the myriad of details on how to manage the events being generated operationally, but could learn something from this exercise.

Consider the major system failures in your organization over the last 12 to 18 months. What if you had a system or process in place to capture those failures and mitigate them from a proactive standpoint preventing them from reoccurring? How much better off would you be if you could avoid the proverbial “Groundhog Day” with system outages? The argument that system monitoring is just a nice to have, and not really a core requirement for operational readiness, dissipates quickly when a critical application goes down with no warning.

Starting with the Event management and Incident management processes may seem like a reactive approach when implementing an Application Performance Management (APM) solution, but is it really? If “Rome is burning”, wouldn’t the most prudent action be to extinguish the fire, then come up with a proactive approach for prevention? Managing the operational noise can calm the environment allowing you to focus on APM strategy more effectively.

Asking the right questions during a post-mortem review will help generate dialog, outlining options for alerting and prevention. This will direct your thinking towards a new horizon of continual improvement that will help galvanize proactive monitoring as an operational requirement.

Here are three questions that build on each other as you work to mature your solution:

1. Did we alert on it when it went down, or did the user community call us?

2. Can we get a proactive alert on it before it goes down, (e.g. dual power supply failure in server)?

3. Can we trend on the event creating a predictive alert before it is escalated, (e.g. disk space utilization to trigger a minor@90%, major@95%, critical@98%)?

The preceding questions are directly related to the following categories respectively: Reactive, Proactive, and Predictive.

Reactive – Alerts that Occur at Failure

Multiple events can occur before a system failure; eventually an alert will come in notifying you that an application is down. This will come from either the users calling the Service Desk to report an issue or it will be system generated corresponding with an application failure.

Proactive – Alerts that Occur Before Failure

These alerts will most likely come from proactive monitoring to tell you there are component failures that need attention but have not yet affected overall application availability, (e.g. dual power supply failure in server).

Predictive – Alerts that Trend on a Possible Failure

These alerts are usually set up in parallel with trending reports that will help predict subtle changes in the environment, (e.g. trending on memory usage or disk utilization before running out of resources).

Image removed.

Conclusion

Once you build awareness in the organization that you have a bird’s eye view of the technical landscape and have the ability to monitor the ecosystem of each application (as an ecologist), people become more meticulous when introducing new elements into the environment. They know that you are watching, taking samples, and trending on the overall health and stability leaving you free to focus on the strategic side of APM without distraction.

You can contact Larry on LinkedIn

Related Links:

For a high-level view of a much broader technology space refer to the slide show on BrightTALK.com which describes the “The Anatomy of APM - webcast” in more context.

For more information on the critical success factors in APM adoption and how this centers around the End-User-Experience (EUE), read The Anatomy of APM and the corresponding blog APM’s DNA – Event to Incident Flow.

Prioritizing Gartner's APM Model

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Event Management: Reactive, Proactive or Predictive?

Larry Dragich

Can event management help foster a curiosity for innovative possibilities to make application performance better? Blue-sky thinkers may not want to deal with the myriad of details on how to manage the events being generated operationally, but could learn something from this exercise.

Consider the major system failures in your organization over the last 12 to 18 months. What if you had a system or process in place to capture those failures and mitigate them from a proactive standpoint preventing them from reoccurring? How much better off would you be if you could avoid the proverbial “Groundhog Day” with system outages? The argument that system monitoring is just a nice to have, and not really a core requirement for operational readiness, dissipates quickly when a critical application goes down with no warning.

Starting with the Event management and Incident management processes may seem like a reactive approach when implementing an Application Performance Management (APM) solution, but is it really? If “Rome is burning”, wouldn’t the most prudent action be to extinguish the fire, then come up with a proactive approach for prevention? Managing the operational noise can calm the environment allowing you to focus on APM strategy more effectively.

Asking the right questions during a post-mortem review will help generate dialog, outlining options for alerting and prevention. This will direct your thinking towards a new horizon of continual improvement that will help galvanize proactive monitoring as an operational requirement.

Here are three questions that build on each other as you work to mature your solution:

1. Did we alert on it when it went down, or did the user community call us?

2. Can we get a proactive alert on it before it goes down, (e.g. dual power supply failure in server)?

3. Can we trend on the event creating a predictive alert before it is escalated, (e.g. disk space utilization to trigger a minor@90%, major@95%, critical@98%)?

The preceding questions are directly related to the following categories respectively: Reactive, Proactive, and Predictive.

Reactive – Alerts that Occur at Failure

Multiple events can occur before a system failure; eventually an alert will come in notifying you that an application is down. This will come from either the users calling the Service Desk to report an issue or it will be system generated corresponding with an application failure.

Proactive – Alerts that Occur Before Failure

These alerts will most likely come from proactive monitoring to tell you there are component failures that need attention but have not yet affected overall application availability, (e.g. dual power supply failure in server).

Predictive – Alerts that Trend on a Possible Failure

These alerts are usually set up in parallel with trending reports that will help predict subtle changes in the environment, (e.g. trending on memory usage or disk utilization before running out of resources).

Image removed.

Conclusion

Once you build awareness in the organization that you have a bird’s eye view of the technical landscape and have the ability to monitor the ecosystem of each application (as an ecologist), people become more meticulous when introducing new elements into the environment. They know that you are watching, taking samples, and trending on the overall health and stability leaving you free to focus on the strategic side of APM without distraction.

You can contact Larry on LinkedIn

Related Links:

For a high-level view of a much broader technology space refer to the slide show on BrightTALK.com which describes the “The Anatomy of APM - webcast” in more context.

For more information on the critical success factors in APM adoption and how this centers around the End-User-Experience (EUE), read The Anatomy of APM and the corresponding blog APM’s DNA – Event to Incident Flow.

Prioritizing Gartner's APM Model

APM and MoM – Symbiotic Solution Sets

Hot Topics

The Latest

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 ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...