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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

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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

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 4 covers negative impacts of AI ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 3 covers barriers and challenges for AI ...