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Best Practices for Successful Cloud Migration for Applications - Part 1

Lev Lesokhin
CAST

It shouldn't come as a surprise that IT leaders are putting a lot of eggs in the cloud basket. By the end of 2020, an estimated 83% of enterprise workloads will be based in the cloud. Platform choices are evolving too, and firms are grappling with the choices, weighing the differences between commodity and custom offerings to fit their application and architectural mix. However, regardless of platform choice, some organizations expect they can dump applications into the cloud and walk away — taking a hands-off approach.


Many aren't doing the due diligence needed to properly assess and facilitate a move of applications to the cloud. This is according to the recent 2019 Cloud Migration Report which revealed half of IT leaders at banks, insurance and telecommunications companies do not conduct adequate risk assessments prior to moving apps over to the cloud. Essentially, they are going in blind and expecting everything to turn out ok. Spoiler alert: It doesn't.

The report shows 50% of businesses don't prioritize what applications need to be moved to the cloud and one third aren't analyzing them before migration. IT decision makers are relying on their "sixth sense" — a gut feeling that it's time, or it's the next logical step in a company's digital transformation journey. The application might be cloud ready too and that becomes reason alone. But it's not enough. Business demand is leading the decision and applications expected to fit into the cloud without prior consideration.

As a result, 40% of cloud migrations are falling short of expectations — failing to meet targets for cost, resiliency and planned user benefits.

Fewer than 35% of technology leaders use freely-available analysis tools. There is a systematic failure to assess the underlying application readiness for cloud migration with a deep analysis of software architecture.

IT teams need to adopt an analysis led approach to cloud migration — assessing both the qualitative business impact and objective composition of their application portfolio. This will make the front-end migration easier and simplify the back-end maintenance over time — if you are ready to begin with, you won't have to overcome serious obstacles later. One small change to an application has a domino effect on the rest of the code set, so when something big, like a cloud migration, takes place and an application isn't ready, the effects can be detrimental with outcomes such as IT outages and loss of business.

Read Best Practices for Successful Cloud Migration for Applications - Part 2, for three best practices for successful cloud migration for applications.

Lev Lesokhin is EVP of Strategy and Analytics at CAST

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.

Best Practices for Successful Cloud Migration for Applications - Part 1

Lev Lesokhin
CAST

It shouldn't come as a surprise that IT leaders are putting a lot of eggs in the cloud basket. By the end of 2020, an estimated 83% of enterprise workloads will be based in the cloud. Platform choices are evolving too, and firms are grappling with the choices, weighing the differences between commodity and custom offerings to fit their application and architectural mix. However, regardless of platform choice, some organizations expect they can dump applications into the cloud and walk away — taking a hands-off approach.


Many aren't doing the due diligence needed to properly assess and facilitate a move of applications to the cloud. This is according to the recent 2019 Cloud Migration Report which revealed half of IT leaders at banks, insurance and telecommunications companies do not conduct adequate risk assessments prior to moving apps over to the cloud. Essentially, they are going in blind and expecting everything to turn out ok. Spoiler alert: It doesn't.

The report shows 50% of businesses don't prioritize what applications need to be moved to the cloud and one third aren't analyzing them before migration. IT decision makers are relying on their "sixth sense" — a gut feeling that it's time, or it's the next logical step in a company's digital transformation journey. The application might be cloud ready too and that becomes reason alone. But it's not enough. Business demand is leading the decision and applications expected to fit into the cloud without prior consideration.

As a result, 40% of cloud migrations are falling short of expectations — failing to meet targets for cost, resiliency and planned user benefits.

Fewer than 35% of technology leaders use freely-available analysis tools. There is a systematic failure to assess the underlying application readiness for cloud migration with a deep analysis of software architecture.

IT teams need to adopt an analysis led approach to cloud migration — assessing both the qualitative business impact and objective composition of their application portfolio. This will make the front-end migration easier and simplify the back-end maintenance over time — if you are ready to begin with, you won't have to overcome serious obstacles later. One small change to an application has a domino effect on the rest of the code set, so when something big, like a cloud migration, takes place and an application isn't ready, the effects can be detrimental with outcomes such as IT outages and loss of business.

Read Best Practices for Successful Cloud Migration for Applications - Part 2, for three best practices for successful cloud migration for applications.

Lev Lesokhin is EVP of Strategy and Analytics at CAST

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.