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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.

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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

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 ...