Skip to main content

Why Network Change Initiatives Must be Data-Driven

Jay Botelho

Enterprise IT infrastructure never ceases to evolve, as companies continually re-examine and reimagine the network to incorporate new technology advancements and meet changing business requirements. But network change initiatives can be costly and time-consuming without a proactive approach to ensuring the right data is available to drive your initiatives.

Common network change initiatives today include cloud migrations, new SD-WAN deployments and adopting 802.11ax. Data should be your guide when removing, upgrading or replacing any IT infrastructure and managing a transition to such technologies. You must monitor key performance metrics for all network elements involved before, during and after every network change operation. Failing to do so will elevate your risks of failed deployments, as well as obfuscated performance issues, poor user experiences and more, throughout rollout and beyond.

Cloud migrations are particularly timely network change initiatives we can examine to understand importance of a data-drive approach. Given COVID-19's impact on how and where people leverage network resources, cloud adoption has spiked in 2020. In fact, nearly 60% of enterprises expect cloud technology usage to exceed prior plans due to increasing distributed and remote work as a result of the pandemic. Let's explore the consequences of a cloud migration project without the necessary data involved, how data impacts each step in the process and the visibility you need to succeed.

Cloud Migration Crises Abound Without Data

A cloud migration without foundational data can be an ugly affair. Without baselines for your existing network and application performance, you're likely to be greeted with a complex set of issues to untangle throughout the migration process. These can range from poor connectivity and higher latency, to even security issues.

For example, after migrating several key business applications users might experience increased latency. But is it truly worse than before?

Is it unacceptable?

And are the migrated applications really the cause?

Or could it be due to increased VPN connections and bandwidth as more users working remotely attempt to access the new cloud services?

Without solid data from before the migration, these are difficult questions to answer. And they need to be answered quickly because a perceived degradation in performance will encourage employees to circumvent established processes requiring VPN usage to access key cloud-based applications such as Salesforce, WebEx or Zoom. This would change the workflow before a clear diagnosis can be made, and make things less secure by reducing your visibility into user activity and any suspicious anomalies. Data is the key to getting in front of just about every cloud migration issue.

The Role of Data Throughout the Cloud Migration Lifecycle

Rooting your migration in data and leveraging data-driven insights throughout the initiative can deliver end-to-end visibility from on-premises environments into the public cloud, and help ensure a successful rollout. From "Day 0" planning and "Day 1" deployment to "Day 2" ongoing monitoring and optimization, here's why data is king when it comes to cloud migrations:

1. Planning a cloud migration should start with establishing a baseline across your existing IT infrastructure. Here you'll measure key data and metrics to define what's "normal" for network performance levels, application performance trends, and behaviors across users, devices, key services and more. You'll leverage all this information to map out existing bandwidth usage and throughput patterns, SLA requirements and quality of service (QoS) policies. Without collecting and understanding these data upfront, you'll lack the context and specifics you need to be able to truly determine, tune and control how your new cloud deployment is functioning. It's also critical that you have the solutions in place to ensure that the visibility and data you're able to access pre-deployment carries over across the cloud migration.

2. Implementing a new cloud deployment successfully will rely heavily on the data you've collected pre-rollout. The cloud migration phase itself will be a true test of how well your team has planned the initiative and if you've established the historical baselines needed to effectively measure and manage post-migration. You'll need to quickly identify and resolve any network or application performance issues such as poor connectivity, high latency, unforeseen capacity limitations, degraded user experiences and more, as well as verify the SLAs and QoS policies you established during the planning process.

Whether you're migrating limited portions of your system such as a few specific databases or servers, or an entire application stack or data center, you need deep, end-to-end visibility from on-prem into the public cloud, and into VPC traffic and the cloud services running through it.
Most cloud monitoring tools are burdensome to manage alongside existing monitoring products and can't provide a comprehensive view of network or application issues that extend across the hybrid environment. This goes for both monitoring dashboards from cloud providers themselves as well as specialized point solutions.

That's why it's critical to leverage advanced monitoring solutions capable of capturing network traffic that traverse the public cloud and converting it into flow data for in-depth 360-degree performance analytics and visualization, all using the same integrated solution. Without this level of detail, you'll lack a complete understanding of traffic behavior, application usage and performance within your new cloud infrastructure, and be unable to verify the new implementation is working as planned.

3. Effective cloud monitoring and optimization over the long term is heavily dependent on your level of visibility. The third and "final" phase of a cloud migration is all about continual improvement. Your goal is to continuously monitor the deployment, proactively identify and resolve issues before they happen, and optimize performance to meet your business' needs.

If you've established the baselines and visibility required to identify network and application performance issues across your cloud workloads, you should be able to take advantage of automated alerting that proactively "predicts" potential issues — or at least the warning signs that those issues might arise — so you can mitigate them before they impact the business.

For instance, if you have multiple automated alerts of application latency exceeding your defined threshold, and these reports span a wide range of users, you can proactively test the latency from your location and quickly determine if the source is in the cloud. And if so, assuming you have a solution in place that can also capture network data from the cloud, you can quickly isolate the issue and the source, and take corrective action.

End-to-end visibility that extends from the network to the cloud is a basic requirement for successful cloud migrations today. This is just one example of the many network change initiatives organizations often tackle, and the one major commonality across them all is that their success depends on data. Knowing your network through in-depth data will ensure your team is able to plan, deploy and optimize key network change initiatives that will better support and enable your business in 2021 and beyond.

Hot Topics

The Latest

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...

Why Network Change Initiatives Must be Data-Driven

Jay Botelho

Enterprise IT infrastructure never ceases to evolve, as companies continually re-examine and reimagine the network to incorporate new technology advancements and meet changing business requirements. But network change initiatives can be costly and time-consuming without a proactive approach to ensuring the right data is available to drive your initiatives.

Common network change initiatives today include cloud migrations, new SD-WAN deployments and adopting 802.11ax. Data should be your guide when removing, upgrading or replacing any IT infrastructure and managing a transition to such technologies. You must monitor key performance metrics for all network elements involved before, during and after every network change operation. Failing to do so will elevate your risks of failed deployments, as well as obfuscated performance issues, poor user experiences and more, throughout rollout and beyond.

Cloud migrations are particularly timely network change initiatives we can examine to understand importance of a data-drive approach. Given COVID-19's impact on how and where people leverage network resources, cloud adoption has spiked in 2020. In fact, nearly 60% of enterprises expect cloud technology usage to exceed prior plans due to increasing distributed and remote work as a result of the pandemic. Let's explore the consequences of a cloud migration project without the necessary data involved, how data impacts each step in the process and the visibility you need to succeed.

Cloud Migration Crises Abound Without Data

A cloud migration without foundational data can be an ugly affair. Without baselines for your existing network and application performance, you're likely to be greeted with a complex set of issues to untangle throughout the migration process. These can range from poor connectivity and higher latency, to even security issues.

For example, after migrating several key business applications users might experience increased latency. But is it truly worse than before?

Is it unacceptable?

And are the migrated applications really the cause?

Or could it be due to increased VPN connections and bandwidth as more users working remotely attempt to access the new cloud services?

Without solid data from before the migration, these are difficult questions to answer. And they need to be answered quickly because a perceived degradation in performance will encourage employees to circumvent established processes requiring VPN usage to access key cloud-based applications such as Salesforce, WebEx or Zoom. This would change the workflow before a clear diagnosis can be made, and make things less secure by reducing your visibility into user activity and any suspicious anomalies. Data is the key to getting in front of just about every cloud migration issue.

The Role of Data Throughout the Cloud Migration Lifecycle

Rooting your migration in data and leveraging data-driven insights throughout the initiative can deliver end-to-end visibility from on-premises environments into the public cloud, and help ensure a successful rollout. From "Day 0" planning and "Day 1" deployment to "Day 2" ongoing monitoring and optimization, here's why data is king when it comes to cloud migrations:

1. Planning a cloud migration should start with establishing a baseline across your existing IT infrastructure. Here you'll measure key data and metrics to define what's "normal" for network performance levels, application performance trends, and behaviors across users, devices, key services and more. You'll leverage all this information to map out existing bandwidth usage and throughput patterns, SLA requirements and quality of service (QoS) policies. Without collecting and understanding these data upfront, you'll lack the context and specifics you need to be able to truly determine, tune and control how your new cloud deployment is functioning. It's also critical that you have the solutions in place to ensure that the visibility and data you're able to access pre-deployment carries over across the cloud migration.

2. Implementing a new cloud deployment successfully will rely heavily on the data you've collected pre-rollout. The cloud migration phase itself will be a true test of how well your team has planned the initiative and if you've established the historical baselines needed to effectively measure and manage post-migration. You'll need to quickly identify and resolve any network or application performance issues such as poor connectivity, high latency, unforeseen capacity limitations, degraded user experiences and more, as well as verify the SLAs and QoS policies you established during the planning process.

Whether you're migrating limited portions of your system such as a few specific databases or servers, or an entire application stack or data center, you need deep, end-to-end visibility from on-prem into the public cloud, and into VPC traffic and the cloud services running through it.
Most cloud monitoring tools are burdensome to manage alongside existing monitoring products and can't provide a comprehensive view of network or application issues that extend across the hybrid environment. This goes for both monitoring dashboards from cloud providers themselves as well as specialized point solutions.

That's why it's critical to leverage advanced monitoring solutions capable of capturing network traffic that traverse the public cloud and converting it into flow data for in-depth 360-degree performance analytics and visualization, all using the same integrated solution. Without this level of detail, you'll lack a complete understanding of traffic behavior, application usage and performance within your new cloud infrastructure, and be unable to verify the new implementation is working as planned.

3. Effective cloud monitoring and optimization over the long term is heavily dependent on your level of visibility. The third and "final" phase of a cloud migration is all about continual improvement. Your goal is to continuously monitor the deployment, proactively identify and resolve issues before they happen, and optimize performance to meet your business' needs.

If you've established the baselines and visibility required to identify network and application performance issues across your cloud workloads, you should be able to take advantage of automated alerting that proactively "predicts" potential issues — or at least the warning signs that those issues might arise — so you can mitigate them before they impact the business.

For instance, if you have multiple automated alerts of application latency exceeding your defined threshold, and these reports span a wide range of users, you can proactively test the latency from your location and quickly determine if the source is in the cloud. And if so, assuming you have a solution in place that can also capture network data from the cloud, you can quickly isolate the issue and the source, and take corrective action.

End-to-end visibility that extends from the network to the cloud is a basic requirement for successful cloud migrations today. This is just one example of the many network change initiatives organizations often tackle, and the one major commonality across them all is that their success depends on data. Knowing your network through in-depth data will ensure your team is able to plan, deploy and optimize key network change initiatives that will better support and enable your business in 2021 and beyond.

Hot Topics

The Latest

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...