Best Practices for Successful Cloud Migration for Applications - Part 2

Lev Lesokhin
CAST

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.

Start with Best Practices for Successful Cloud Migration for Applications - Part 1

A solid start to migration can be approached three ways — all of which are ladder up to adopting a Software Intelligence strategy:

1. Businesses should lead with a benefit-based approach to cloud migration instead of targeting the low hanging fruit

Today, over 50% of financial firms don't do this. The majority (over 80%) are picking the easiest applications to migrate in order to generate early momentum in the cloud. Non-core customer applications are taking precedence over critical ones. Although the latter may be easier to implement, they often don't provide ROI and as a result can impede the migration of more business essential applications.

To avoid wasting time and resources on unnecessary migrations, software leaders need to decide why applications are being moved rather than opting for quick wins. Some applications may be better off not being migrated at all. There is always the option of replacing lower value applications with commercially available off-the-shelf alternatives saving time, cost, and resources.

2. Be advised: "lift and shift" is high risk for mainframe applications

To save time and resources the majority of IT leaders have tried a "lift-and-shift" approach, moving applications from their current environments to the cloud without adjusting or updating them. This only works for applications with up to three years before end of life, not mainframe applications. Moving workloads without changing the way they are shaped, creates issues.

Effective cloud migration demands increased data connectivity, performance and security. Legacy process firms experience further complications when they locate data on a different cloud platform to the applications.

This is also true for the connectivity back to the mainframe. As these systems become exposed to the outside world via APIs, the threat surface and complexity increases. Standing up replacement systems beside the mainframe will be the principal cloud migration option for large institutions.

And as some functionality goes live, equivalent components can be retired as long as the cost isn't too prohibitive. Planning needs to begin now in order to start this critical migration process. 

3. Choose the most effective migration plan and architecture approach per application

Grappling with the microservices versus miniservices question is a struggle for most firms, regardless of region, particularly for legacy processes. Setting the right architectural model is critical to avoid creating additional technical debt. In monolithic systems, resiliency suffers, but heading too far down the microservices path creates dependency and deployment risks. Microservices are ideal for rapidly evolving applications serving a diverse user base; miniservices enable a quick path to value. Refactoring — and grasping the relative opportunity for modernization in virtualized environments — is the battle to be won.

Overall, businesses need to follow these best practices, but they also must assess applications using four key criteria to determine whether to rehost or replatform, refactor or rebuild. They need to look at interconnectivity, reliability, effort to remediate, and the frequency of drops. This will help them to assess the best strategy for migration per application class.

Cloud migration is essential for digital transformation and the pressure is on for organizations to get it done quickly. However, fewer than 35% of technology leaders are using freely available analysis tools to prepare for migration, and over 70% of firms report cloud migration is driven inconsistently across application silos. 

Software leaders need to gain a deeper understanding of an application's cloud readiness before migration begins to mitigate any potential issues. They also need to adopt a Software Intelligence led approach, basically a cloud center of excellence which helps to centralize cloud migration activities, which will lead to faster progress.  Using an analysis-led approach over gut instincts will help avoid any additional costs or IT outages down the line. Without it, firms will miss the opportunity to truly modernize the IT environment and investment priorities and benefits will simply be lost. 

Lev Lesokhin is EVP of Strategy and Analytics at CAST

Related Links

Hot Topics

The Latest

UK Organizations Hit Observability Breaking Point

UK IT leaders are reaching a critical inflection point in how they manage observability, according to research from LogicMonitor. As infrastructure complexity grows and AI adoption accelerates, fragmented monitoring environments are driving organizations to rethink their operational strategies and consolidate tools ...

At the Edge, Performance Visibility Becomes a Reliability Requirement

For years, many infrastructure teams treated the edge as a deployment variation. It was seen as the same cloud model, only stretched outward: more devices, more gateways, more locations and a little more latency. That assumption is proving costly. The edge is not just another place to run workloads. It is a fundamentally different operating condition ...

Why Revenue Data Governance Is Now the CIO's Responsibility

AI can't fix broken data. CIOs who modernize revenue data governance unlock predictable growth-those who don't risk millions in failed AI investments. For decades, CIOs kept the lights on. Revenue was someone else's problem, owned by sales, led by the CRO, measured by finance. Those days are behind us ...

Beyond 2026: How 5G, Metaverse, and Edge AI Will Redefine Secure Remote Access

Over the past few years, organizations have made enormous strides in enabling remote and hybrid work. But the foundational technologies powering today's digital workplace were never designed for the volume, velocity, and complexity that is coming next. By 2026 and beyond, three forces — 5G, the metaverse, and edge AI — will fundamentally reshape how people connect, collaborate, and access enterprise resources ... The businesses that begin preparing now will gain a competitive head start. Those that wait will find themselves trying to secure environments that have already outgrown their architecture ...

Why IT Has Become the Proving Ground for Enterprise AI

Ask where enterprise AI is making its most decisive impact, and the answer might surprise you: not marketing, not finance, not customer experience. It's IT. Across three years of industry research conducted by Digitate, one constant holds true is that IT is both the testing ground and the proving ground for enterprise AI. Last year, that position only strengthened ...

Best Practices for Successful Cloud Migration for Applications - Part 2

Lev Lesokhin
CAST

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.

Start with Best Practices for Successful Cloud Migration for Applications - Part 1

A solid start to migration can be approached three ways — all of which are ladder up to adopting a Software Intelligence strategy:

1. Businesses should lead with a benefit-based approach to cloud migration instead of targeting the low hanging fruit

Today, over 50% of financial firms don't do this. The majority (over 80%) are picking the easiest applications to migrate in order to generate early momentum in the cloud. Non-core customer applications are taking precedence over critical ones. Although the latter may be easier to implement, they often don't provide ROI and as a result can impede the migration of more business essential applications.

To avoid wasting time and resources on unnecessary migrations, software leaders need to decide why applications are being moved rather than opting for quick wins. Some applications may be better off not being migrated at all. There is always the option of replacing lower value applications with commercially available off-the-shelf alternatives saving time, cost, and resources.

2. Be advised: "lift and shift" is high risk for mainframe applications

To save time and resources the majority of IT leaders have tried a "lift-and-shift" approach, moving applications from their current environments to the cloud without adjusting or updating them. This only works for applications with up to three years before end of life, not mainframe applications. Moving workloads without changing the way they are shaped, creates issues.

Effective cloud migration demands increased data connectivity, performance and security. Legacy process firms experience further complications when they locate data on a different cloud platform to the applications.

This is also true for the connectivity back to the mainframe. As these systems become exposed to the outside world via APIs, the threat surface and complexity increases. Standing up replacement systems beside the mainframe will be the principal cloud migration option for large institutions.

And as some functionality goes live, equivalent components can be retired as long as the cost isn't too prohibitive. Planning needs to begin now in order to start this critical migration process. 

3. Choose the most effective migration plan and architecture approach per application

Grappling with the microservices versus miniservices question is a struggle for most firms, regardless of region, particularly for legacy processes. Setting the right architectural model is critical to avoid creating additional technical debt. In monolithic systems, resiliency suffers, but heading too far down the microservices path creates dependency and deployment risks. Microservices are ideal for rapidly evolving applications serving a diverse user base; miniservices enable a quick path to value. Refactoring — and grasping the relative opportunity for modernization in virtualized environments — is the battle to be won.

Overall, businesses need to follow these best practices, but they also must assess applications using four key criteria to determine whether to rehost or replatform, refactor or rebuild. They need to look at interconnectivity, reliability, effort to remediate, and the frequency of drops. This will help them to assess the best strategy for migration per application class.

Cloud migration is essential for digital transformation and the pressure is on for organizations to get it done quickly. However, fewer than 35% of technology leaders are using freely available analysis tools to prepare for migration, and over 70% of firms report cloud migration is driven inconsistently across application silos. 

Software leaders need to gain a deeper understanding of an application's cloud readiness before migration begins to mitigate any potential issues. They also need to adopt a Software Intelligence led approach, basically a cloud center of excellence which helps to centralize cloud migration activities, which will lead to faster progress.  Using an analysis-led approach over gut instincts will help avoid any additional costs or IT outages down the line. Without it, firms will miss the opportunity to truly modernize the IT environment and investment priorities and benefits will simply be lost. 

Lev Lesokhin is EVP of Strategy and Analytics at CAST

Related Links

Hot Topics

The Latest

UK Organizations Hit Observability Breaking Point

UK IT leaders are reaching a critical inflection point in how they manage observability, according to research from LogicMonitor. As infrastructure complexity grows and AI adoption accelerates, fragmented monitoring environments are driving organizations to rethink their operational strategies and consolidate tools ...

At the Edge, Performance Visibility Becomes a Reliability Requirement

For years, many infrastructure teams treated the edge as a deployment variation. It was seen as the same cloud model, only stretched outward: more devices, more gateways, more locations and a little more latency. That assumption is proving costly. The edge is not just another place to run workloads. It is a fundamentally different operating condition ...

Why Revenue Data Governance Is Now the CIO's Responsibility

AI can't fix broken data. CIOs who modernize revenue data governance unlock predictable growth-those who don't risk millions in failed AI investments. For decades, CIOs kept the lights on. Revenue was someone else's problem, owned by sales, led by the CRO, measured by finance. Those days are behind us ...

Beyond 2026: How 5G, Metaverse, and Edge AI Will Redefine Secure Remote Access

Over the past few years, organizations have made enormous strides in enabling remote and hybrid work. But the foundational technologies powering today's digital workplace were never designed for the volume, velocity, and complexity that is coming next. By 2026 and beyond, three forces — 5G, the metaverse, and edge AI — will fundamentally reshape how people connect, collaborate, and access enterprise resources ... The businesses that begin preparing now will gain a competitive head start. Those that wait will find themselves trying to secure environments that have already outgrown their architecture ...

Why IT Has Become the Proving Ground for Enterprise AI

Ask where enterprise AI is making its most decisive impact, and the answer might surprise you: not marketing, not finance, not customer experience. It's IT. Across three years of industry research conducted by Digitate, one constant holds true is that IT is both the testing ground and the proving ground for enterprise AI. Last year, that position only strengthened ...