Skip to main content

One Trick To Creating Better and Faster Rollouts

Keith Bromley

If you have not been engaging in proactive monitoring efforts, it’s something you might want to consider. Most IT monitoring efforts are reactive — you either periodically try some basic attempts to find problems on the live network or you decide to wait and respond to customer complaints. This is supported by the EMA Network Management Megatrends 2016 report which states that approximately 40% of network problems are detected and reported by end users.

Everyone in IT understands this. It’s unfortunate but true — you can’t be everywhere doing everything for everyone. In addition, the report states that 26% of the respondents reported that one of their top networking challenges is the lack of end-to-end, multisite network visibility and troubleshooting capabilities. This is where visibility technology can help by giving you access to critical monitoring, when you need it and in the format you need it.

But improved visibility isn’t usually enough. You are probably going to need a more proactive troubleshooting approach as well. Proactive monitoring uses visibility technology to actively test your solution either before rollout, during rollout, or after rollout. For instance, it can be used to provide better and faster network and application rollouts by pre-testing the network with synthetic traffic to understand how the solution will perform against either specific application traffic or a combination of traffic types. The synthetic traffic provides you the network and/or application loading of a “busy hour” and the flexibility to perform evaluations during the network maintenance window.

Proactive monitoring has several fundamental benefits including the ability to:

■ Know the performance level of your network immediately

■ Understand how well your applications are running

■ Validate SLAs — both on-premises and in the cloud

■ Test upgrades during maintenance windows before company employees do

Network performance and application performance testing may sound simple, but these can actually be difficult to ascertain. To get a true indication of network performance, the network needs to have a large amount of traffic on it, which makes you dependent upon peak busy hours. This type of solution allows you to place probes anywhere in your network and test whenever you want to. It also allows you to accurately simulate the right type of traffic so that Application Performance Management (APM) tools can observe how well applications are truly performing. For instance, this allows you to simulate small packets or Skype-like data if you want to test your instant message (IM)/voice/video solution.

Once you’ve conducted your proactive monitoring test cases, you’ll have the information you need to either continue with your solution update (i.e. continue with the network or application rollout) or perform a rollback (before it affects any users outside of the maintenance window).

Proactive monitoring also allows you to perform SLA validation during business hours, since it is not service disrupting. This allows you validate the SLA performance at will. The information gathered can then be used to inform management about which goals are being met. If goals are not being met, you can use the impartial data you have collected and contact your vendor to have them either fix any observed network problems, or give you a discount if they are failing to meet agreed upon SLAs.

The final benefit is that there are proactive monitoring solutions on the market that let you test your on-premises solution as well as your cloud solution. This can be especially important if you have a hybrid solution right now, and are in the (often multi-year) process of transitioning from the physical to the virtual (cloud) world. A proactive monitoring testing and monitoring approach gives you the confidence that your application rollouts will be successful in either network.

Hot Topics

The Latest

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 ...

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 ...

One Trick To Creating Better and Faster Rollouts

Keith Bromley

If you have not been engaging in proactive monitoring efforts, it’s something you might want to consider. Most IT monitoring efforts are reactive — you either periodically try some basic attempts to find problems on the live network or you decide to wait and respond to customer complaints. This is supported by the EMA Network Management Megatrends 2016 report which states that approximately 40% of network problems are detected and reported by end users.

Everyone in IT understands this. It’s unfortunate but true — you can’t be everywhere doing everything for everyone. In addition, the report states that 26% of the respondents reported that one of their top networking challenges is the lack of end-to-end, multisite network visibility and troubleshooting capabilities. This is where visibility technology can help by giving you access to critical monitoring, when you need it and in the format you need it.

But improved visibility isn’t usually enough. You are probably going to need a more proactive troubleshooting approach as well. Proactive monitoring uses visibility technology to actively test your solution either before rollout, during rollout, or after rollout. For instance, it can be used to provide better and faster network and application rollouts by pre-testing the network with synthetic traffic to understand how the solution will perform against either specific application traffic or a combination of traffic types. The synthetic traffic provides you the network and/or application loading of a “busy hour” and the flexibility to perform evaluations during the network maintenance window.

Proactive monitoring has several fundamental benefits including the ability to:

■ Know the performance level of your network immediately

■ Understand how well your applications are running

■ Validate SLAs — both on-premises and in the cloud

■ Test upgrades during maintenance windows before company employees do

Network performance and application performance testing may sound simple, but these can actually be difficult to ascertain. To get a true indication of network performance, the network needs to have a large amount of traffic on it, which makes you dependent upon peak busy hours. This type of solution allows you to place probes anywhere in your network and test whenever you want to. It also allows you to accurately simulate the right type of traffic so that Application Performance Management (APM) tools can observe how well applications are truly performing. For instance, this allows you to simulate small packets or Skype-like data if you want to test your instant message (IM)/voice/video solution.

Once you’ve conducted your proactive monitoring test cases, you’ll have the information you need to either continue with your solution update (i.e. continue with the network or application rollout) or perform a rollback (before it affects any users outside of the maintenance window).

Proactive monitoring also allows you to perform SLA validation during business hours, since it is not service disrupting. This allows you validate the SLA performance at will. The information gathered can then be used to inform management about which goals are being met. If goals are not being met, you can use the impartial data you have collected and contact your vendor to have them either fix any observed network problems, or give you a discount if they are failing to meet agreed upon SLAs.

The final benefit is that there are proactive monitoring solutions on the market that let you test your on-premises solution as well as your cloud solution. This can be especially important if you have a hybrid solution right now, and are in the (often multi-year) process of transitioning from the physical to the virtual (cloud) world. A proactive monitoring testing and monitoring approach gives you the confidence that your application rollouts will be successful in either network.

Hot Topics

The Latest

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 ...

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 ...