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Prepare for Success in Cloud Migration: Elevate Above Infrastructure and Silo Tools

Eric Kraieski

Achieving success with cloud adoption remains an elusive challenge for many organizations. But why is that the case? After all, there are countless tools designed to facilitate the process of taking on-premises operations to the cloud. It is common to use these purpose-built tools for moving virtual images, automatically provisioning services, migrating data, right sizing deployments and optimizing cloud operations. But when it comes to application migration, this variety of infrastructure tools actually contributes to the problem.

These disparate systems certainly work well enough for their specific use and purpose. However, successful execution of application rehosting requires that users look above these infrastructure tools to see the full picture and select the tools appropriate to the specific migration method, or "R approach" — rehost, replatform, repurchase, refactor, retire, retain — for the application.

Perhaps the most interesting thing about these point solutions is that they all focus on infrastructure migration tasks, while selecting the R approach and the specific sequence of steps required are entirely dependent on the application and business goals. How is it possible to decide which R method is optimal or appropriate if you never look into the application requirements? Before committing to a specific path for each application, it is essential to consider the business needs for its availability, access, performance and the total cost to achieve the migration.

Of course there will be times where a straight rehost (often called a "lift and shift") will be appropriate and optimal. This is especially true for mobile workloads that are self contained on a specific server with few external dependencies, no external storage and no requirement for real time app-to-app communications. In other cases, a rehost may be possible, but not desired. For example, rehosting may violate corporate information security policies such as HIPAA or GDPR. Or perhaps some refactoring or reconfiguration is required to achieve target availability goals. Organizations should assume no more than 10-20% of their total server inventory will be highly mobile and ready for an automated rehosting process.

In our experience, organizations that proceed with an "infrastructure first" approach quickly recognize that it does not take into account the impact on critical business applications early enough in the process. Unfortunately, this approach often results in wasted time as plans must shift once the business factors are acknowledged and considered. Taking an application-centric approach is the only way to orchestrate a successful cloud migration.

Know Your Environment Before You Commit to a Migration Approach

Cloud-native platforms typically provide much more agility and flexibility than lifted premise ones. Refactoring apps can be costly and time consuming, so some organizations prefer the lift-and-shift approach. But before you make the decision to take a lift-and-shift migration approach for an application, carefully consider your current environment, your business and IT goals, and expected cost and staff impact first. Simply shifting a legacy, premise-based app to the cloud with all its limitations instead of refactoring it may limit you from taking full advantage of all the benefits associated with the cloud.

Bottom line, tools to support general planning and to assess server mobility can provide useful data points, but are not sufficient for comprehensive planning. Developing appropriate migration plans requires a comprehensive understanding of the entire environment including appropriate information from siloed tools and application SMEs (subject matter experts). Applications should be identified and assessed for readiness: some will be able to be migrated immediately, while others will have to be rewritten or modernized to be workable in the cloud. The indispensable first step for any migration is having an actionable picture of the entire data center, which includes:

■ Accurate information about where apps reside, who owns them, and what SLAs, RTOs, RPOs apply.

■ Complete knowledge regarding the application dependency landscape. Don't just rely on autodiscovery. Your subject matter experts know the ins and outs of your business and will be able to tell you things that machines can't detect.

■ A normalized view of the landscape.

■ Visual dependency mapping of the entire landscape, including what applications are dependent upon, and what is dependent upon them.

■ An understanding of what applications should generally be "grouped together" for a cloud move.

■ The ability to distinguish superfluous data from information that matters. For example, the operating system, manufacturer/model, and IP address are commonly used data points in migration analysis and planning activities, while other information such as CPU speed, MAC address, BIOS, and OS Install Date are simply not necessary or beneficial to the migration activities. Tracking unnecessary data will distract the team and slow down discovery. Don't boil the ocean; just capture the data you need.

Mastering Orchestration

Application migrations are among the most complex projects an organization can undertake and require a cautious approach. If you take the simplest path, assuming that rehost is the preferred approach, then rapid early progress can be achieved by first focusing on the easiest mobile workloads. But once you complete the migration of the easiest workloads, the progress will come to a screeching halt. The majority of your app-to-cloud migrations will require deeper analysis, more careful planning and choreographed execution to assure success.

Orchestration of such an ambitious and sometimes treacherous initiative may seem to be an elusive goal. To avoid mishaps or stalled projects, here are several tips for orchestrating successful outcomes of your cloud migration initiatives:

1. Move up the stack, take an application-centric approach

2. Establish visibility across all silos and users

3. Don't boil the ocean – leverage a sprint-based, iterative approach

4. Leverage existing info and tools where available

With a disciplined approach, you can drive successful outcomes for your cloud adoption initiatives. You'll achieve greater agility and scalability in hosting solutions while avoiding any unplanned outages of your business applications and services.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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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.

Prepare for Success in Cloud Migration: Elevate Above Infrastructure and Silo Tools

Eric Kraieski

Achieving success with cloud adoption remains an elusive challenge for many organizations. But why is that the case? After all, there are countless tools designed to facilitate the process of taking on-premises operations to the cloud. It is common to use these purpose-built tools for moving virtual images, automatically provisioning services, migrating data, right sizing deployments and optimizing cloud operations. But when it comes to application migration, this variety of infrastructure tools actually contributes to the problem.

These disparate systems certainly work well enough for their specific use and purpose. However, successful execution of application rehosting requires that users look above these infrastructure tools to see the full picture and select the tools appropriate to the specific migration method, or "R approach" — rehost, replatform, repurchase, refactor, retire, retain — for the application.

Perhaps the most interesting thing about these point solutions is that they all focus on infrastructure migration tasks, while selecting the R approach and the specific sequence of steps required are entirely dependent on the application and business goals. How is it possible to decide which R method is optimal or appropriate if you never look into the application requirements? Before committing to a specific path for each application, it is essential to consider the business needs for its availability, access, performance and the total cost to achieve the migration.

Of course there will be times where a straight rehost (often called a "lift and shift") will be appropriate and optimal. This is especially true for mobile workloads that are self contained on a specific server with few external dependencies, no external storage and no requirement for real time app-to-app communications. In other cases, a rehost may be possible, but not desired. For example, rehosting may violate corporate information security policies such as HIPAA or GDPR. Or perhaps some refactoring or reconfiguration is required to achieve target availability goals. Organizations should assume no more than 10-20% of their total server inventory will be highly mobile and ready for an automated rehosting process.

In our experience, organizations that proceed with an "infrastructure first" approach quickly recognize that it does not take into account the impact on critical business applications early enough in the process. Unfortunately, this approach often results in wasted time as plans must shift once the business factors are acknowledged and considered. Taking an application-centric approach is the only way to orchestrate a successful cloud migration.

Know Your Environment Before You Commit to a Migration Approach

Cloud-native platforms typically provide much more agility and flexibility than lifted premise ones. Refactoring apps can be costly and time consuming, so some organizations prefer the lift-and-shift approach. But before you make the decision to take a lift-and-shift migration approach for an application, carefully consider your current environment, your business and IT goals, and expected cost and staff impact first. Simply shifting a legacy, premise-based app to the cloud with all its limitations instead of refactoring it may limit you from taking full advantage of all the benefits associated with the cloud.

Bottom line, tools to support general planning and to assess server mobility can provide useful data points, but are not sufficient for comprehensive planning. Developing appropriate migration plans requires a comprehensive understanding of the entire environment including appropriate information from siloed tools and application SMEs (subject matter experts). Applications should be identified and assessed for readiness: some will be able to be migrated immediately, while others will have to be rewritten or modernized to be workable in the cloud. The indispensable first step for any migration is having an actionable picture of the entire data center, which includes:

■ Accurate information about where apps reside, who owns them, and what SLAs, RTOs, RPOs apply.

■ Complete knowledge regarding the application dependency landscape. Don't just rely on autodiscovery. Your subject matter experts know the ins and outs of your business and will be able to tell you things that machines can't detect.

■ A normalized view of the landscape.

■ Visual dependency mapping of the entire landscape, including what applications are dependent upon, and what is dependent upon them.

■ An understanding of what applications should generally be "grouped together" for a cloud move.

■ The ability to distinguish superfluous data from information that matters. For example, the operating system, manufacturer/model, and IP address are commonly used data points in migration analysis and planning activities, while other information such as CPU speed, MAC address, BIOS, and OS Install Date are simply not necessary or beneficial to the migration activities. Tracking unnecessary data will distract the team and slow down discovery. Don't boil the ocean; just capture the data you need.

Mastering Orchestration

Application migrations are among the most complex projects an organization can undertake and require a cautious approach. If you take the simplest path, assuming that rehost is the preferred approach, then rapid early progress can be achieved by first focusing on the easiest mobile workloads. But once you complete the migration of the easiest workloads, the progress will come to a screeching halt. The majority of your app-to-cloud migrations will require deeper analysis, more careful planning and choreographed execution to assure success.

Orchestration of such an ambitious and sometimes treacherous initiative may seem to be an elusive goal. To avoid mishaps or stalled projects, here are several tips for orchestrating successful outcomes of your cloud migration initiatives:

1. Move up the stack, take an application-centric approach

2. Establish visibility across all silos and users

3. Don't boil the ocean – leverage a sprint-based, iterative approach

4. Leverage existing info and tools where available

With a disciplined approach, you can drive successful outcomes for your cloud adoption initiatives. You'll achieve greater agility and scalability in hosting solutions while avoiding any unplanned outages of your business applications and services.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

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.