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

Related Links

Hot Topics

The Latest

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Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...