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You Have 40 Monitoring Tools, Make the Next One Count

Richard Whitehead
Moogsoft

In our growing digital economy, end users have no tolerance for downtime. Consequently, IT leaders invest heavily in availability: DevOps and SRE (site reliability engineering) teams to ensure digital apps and services are continuously available and digital tools built to influence uptime.

As recent research uncovered, IT leaders invest in a lot of single-domain monitoring tools. In fact, teams rely on an average of 16 monitoring tools — and up to 40 — according to the Moogsoft State of Availability Report.

Despite this heavy investment, teams are not achieving positive availability outcomes. Perhaps most telling, monitoring tools only catch performance issues or outages about half of the time. Customers flag the rest.

In other words, monitoring tool investments are not paying dividends. They are not helping teams quickly catch data anomalies and expediently fix incidents, and they certainly are not creating a positive customer experience. Yet, DevOps and SREs need monitoring solutions as manually monitoring ever-complex IT ecosystems with ever more data would be impossible.

So what's the secret to modern availability? How can teams better leverage their tools?

The Point Solution Problem: Partial Information

Part of the proliferation of monitoring tools in the IT stack is due to a proliferation of tools in the incident management space in general. Over the past few years, software vendors have introduced a slew of specific point solutions that solve specific problems.

On the positive side, point solutions specialize in monitoring certain aspects of an organization's IT ecosystem: the network, application, IT infrastructure or digital experience. But, problematically, point solutions do not integrate and cannot enable continuous insights across an IT stack. This siloed approach to monitoring:

Costs time and resources

Licensing copious amounts of monitoring tools is expensive. Perhaps even more expensive, human teams need to spend time managing and maintaining these monitoring solutions. And that is likely why research finds engineers spend more time monitoring over any other activity, innovation and value creation included.

Expands operational risk

Siloed approaches to anything — monitoring included — increase operational efficiencies and slow progress. When knowledge sits in one tool, the information tends to get orphaned and this lengthens communication lines and delays incident triage and resolution.

Increases downtime

Issues within the IT ecosystem are typically connected. But, because point solutions lack insight across the entire system, alerts tend to show up in multiple tools, creating a lot of unnecessary noise and further compounding and slowing incident remediation.

The Availability Answer: Use AIOps to Connect Monitoring Tools

To extract value out of monitoring tools and ensure more uptime, engineering teams need to connect their point solutions, creating a single line of sight across the entire incident lifecycle. Domain-agnostic artificial intelligence for IT operations (AIOps) can be this connective tissue. By converging data from all aspects of the incident lifecycle, AIOps connects otherwise siloed point solutions. This integrated approach to monitoring:

Provides a unified dashboard

Point solutions require engineers to hop from tool and tool, monitoring and maintaining various dashboards and charts. AIOps, on the other hand, integrates and aggregates data from across an organization's entire tool stack. As a result, engineering teams can look at one single dashboard that summarizes the health of all of their systems.

Streamlines the incident lifecycle

In addition to providing a summary of system health, AIOps solutions provide one single system of incident engagement. In this incident home base, engineering teams can track the incident lifecycle: detection, notification and resolution. Seeing the full picture of the incident lifecycle in one platform simplifies and speeds the response, and in the meantime, helps engineers understand — and then reduce — the amount of time each phase takes.

Optimizes overall systems

Because AIOps tools take a holistic approach to monitoring, they act as the connective tissue between an organization's monitoring data and help fill data gaps. These solutions make sense of data pulled from multiple point solutions, deduplicating and correlating alerts, enriching data and adding context across systems. This helps teams eliminate noise and identify root causes faster.

Instead of adding another point solution to a growing monitoring toolbox, IT leaders should make their next investment count. And AIOps could be the key. By adopting an AIOps tool, teams understand the whole picture of system health and can sidestep unnecessary noise and alerts to expediently respond to service-disrupting incidents. DevOps and SREs, facing less unplanned work, can invest in the future, paying down technical debt and further increasing system stability.

Richard Whitehead is Chief Evangelist at Moogsoft

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You Have 40 Monitoring Tools, Make the Next One Count

Richard Whitehead
Moogsoft

In our growing digital economy, end users have no tolerance for downtime. Consequently, IT leaders invest heavily in availability: DevOps and SRE (site reliability engineering) teams to ensure digital apps and services are continuously available and digital tools built to influence uptime.

As recent research uncovered, IT leaders invest in a lot of single-domain monitoring tools. In fact, teams rely on an average of 16 monitoring tools — and up to 40 — according to the Moogsoft State of Availability Report.

Despite this heavy investment, teams are not achieving positive availability outcomes. Perhaps most telling, monitoring tools only catch performance issues or outages about half of the time. Customers flag the rest.

In other words, monitoring tool investments are not paying dividends. They are not helping teams quickly catch data anomalies and expediently fix incidents, and they certainly are not creating a positive customer experience. Yet, DevOps and SREs need monitoring solutions as manually monitoring ever-complex IT ecosystems with ever more data would be impossible.

So what's the secret to modern availability? How can teams better leverage their tools?

The Point Solution Problem: Partial Information

Part of the proliferation of monitoring tools in the IT stack is due to a proliferation of tools in the incident management space in general. Over the past few years, software vendors have introduced a slew of specific point solutions that solve specific problems.

On the positive side, point solutions specialize in monitoring certain aspects of an organization's IT ecosystem: the network, application, IT infrastructure or digital experience. But, problematically, point solutions do not integrate and cannot enable continuous insights across an IT stack. This siloed approach to monitoring:

Costs time and resources

Licensing copious amounts of monitoring tools is expensive. Perhaps even more expensive, human teams need to spend time managing and maintaining these monitoring solutions. And that is likely why research finds engineers spend more time monitoring over any other activity, innovation and value creation included.

Expands operational risk

Siloed approaches to anything — monitoring included — increase operational efficiencies and slow progress. When knowledge sits in one tool, the information tends to get orphaned and this lengthens communication lines and delays incident triage and resolution.

Increases downtime

Issues within the IT ecosystem are typically connected. But, because point solutions lack insight across the entire system, alerts tend to show up in multiple tools, creating a lot of unnecessary noise and further compounding and slowing incident remediation.

The Availability Answer: Use AIOps to Connect Monitoring Tools

To extract value out of monitoring tools and ensure more uptime, engineering teams need to connect their point solutions, creating a single line of sight across the entire incident lifecycle. Domain-agnostic artificial intelligence for IT operations (AIOps) can be this connective tissue. By converging data from all aspects of the incident lifecycle, AIOps connects otherwise siloed point solutions. This integrated approach to monitoring:

Provides a unified dashboard

Point solutions require engineers to hop from tool and tool, monitoring and maintaining various dashboards and charts. AIOps, on the other hand, integrates and aggregates data from across an organization's entire tool stack. As a result, engineering teams can look at one single dashboard that summarizes the health of all of their systems.

Streamlines the incident lifecycle

In addition to providing a summary of system health, AIOps solutions provide one single system of incident engagement. In this incident home base, engineering teams can track the incident lifecycle: detection, notification and resolution. Seeing the full picture of the incident lifecycle in one platform simplifies and speeds the response, and in the meantime, helps engineers understand — and then reduce — the amount of time each phase takes.

Optimizes overall systems

Because AIOps tools take a holistic approach to monitoring, they act as the connective tissue between an organization's monitoring data and help fill data gaps. These solutions make sense of data pulled from multiple point solutions, deduplicating and correlating alerts, enriching data and adding context across systems. This helps teams eliminate noise and identify root causes faster.

Instead of adding another point solution to a growing monitoring toolbox, IT leaders should make their next investment count. And AIOps could be the key. By adopting an AIOps tool, teams understand the whole picture of system health and can sidestep unnecessary noise and alerts to expediently respond to service-disrupting incidents. DevOps and SREs, facing less unplanned work, can invest in the future, paying down technical debt and further increasing system stability.

Richard Whitehead is Chief Evangelist at Moogsoft

Hot Topics

The Latest

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 ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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 ...