Mainframe to Cloud Migration Challenges: The 7 Biggest Obstacles and How to Overcome Them

Why Mainframe Migration Is the Most Complex Cloud
Project Your Enterprise Will Run

For more than fifty years, the mainframe has served as the clear, strong main compute center of the global tech market. The scale of need for these old setups is massive.

Mainframe setups right now process 87 percent of all credit card transactions. They handle close to $8 trillion in total payments yearly. They also run roughly $3 trillion in business deals every single day. Below are some points worth keeping in mind here:

1. Despite unmatched uptime, the modern tech market demands a new level of speed. It needs real-time data access and cloud-native teamwork. Older monolithic systems cannot natively give this built-in level of speed.
   
2. That said, migrating a mainframe to the cloud is widely known as the most tech-wise, work-wise, and cost-wise complex project a company can take on.
   
3. Which is why, treating a mainframe migration as a simple hardware swap surely leads to costly outcomes. The same is true for a basic lift-and-shift move.
   
4. In fact, a deep study of 29 recent mainframe-to-cloud migrations showed high failure rates with about 66 percent of these projects failing to meet their stated goals. When these migrations collapse, the money lost in investment can be HUGE.

Challenge 1: Undocumented Business Logic Hidden in Decades-Old COBOL Code

The main IP base of many banking firms, risk groups, and state groups lives in millions of lines of COBOL code. The first mainframe to cloud migration challenges is porting this asset into a cloud-native format is the sheer age and lack of clearness of the codebase.

On average, company mainframe apps are 24.7 years old. About 72 percent of these apps contain unmapped business rules. This issue leads to unknown code. This is a working, high-value software. But, the first intent, choice trees, and math algorithms are fully divorced from any formal docs.

In the past, groups tried to solve these challenges in mainframe to cloud migration through a Big Bang rewrite. This method carries a dismal 20 to 30 percent success rate. It often costs upwards of $2.5 million even for small apps. It also stops feature rollouts for 12 to 24 months.

How to Solve This:

• Take on AI-Helped Code Scans: Firms must shift from hands-on reverse-engineering. They should take on AI-helped code scans. This should be paired with step-by-step structure splitting to safely update unmapped business logic.
• Use GenAI for Logic Mapping: GenAI engines act as highly advanced thinking tools. They are able to parse low-context code types. Firms can lower the time needed to grasp and map out older codebases by up to 75 percent using AI for business logic finding.
• Deploy the Strangler Fig Pattern: Design-wise, the switch must be managed using the Strangler Fig pattern. This is much safer than a Big Bang rewrite.
• Migrate In Steps via APIs: Single workflows are wrapped in REST APIs. They are moved to cloud-native serverless setups like AWS Lambda or Azure Functions. The main real-time transaction engine remains safely on the mainframe.

Challenge 2: Data Migration at Scale Without Downtime or Data Loss

Freeing the base data structures presents a huge risk of business trouble and data damage.

Mainframes encode data using the Extended Binary Coded Decimal Interchange Code standard. Meaning, distributed cloud systems use ASCII or UTF-8. The sorting rules are fully reversed between these two formats. Which is why other major mainframe to cloud migration challenges are doing this without planning for this gap (It can quickly break any business logic!).

Also, COBOL relies on packed decimal data types. This is used to run exact, fixed-point arithmetic. Changing a packed decimal to a floating-point number brings in silent rounding errors. 

This transforms a precise $100.00 transaction into $99.99999999. Market study shows that wrong handling of decimal precision is the direct root cause of 67 percent of COBOL data migration failures.

How to Solve This:

• Use Asynchronous Replication: Firms must deploy asynchronous replication to finish data migration at scale without hurting business uptime. They also need strict rollout designs.
• Take on Expand and Contract Patterns: The bedrock of zero-downtime data migration is the Expand and Contract pattern. This tactic makes sure that database locks are never applied during peak working hours.
• Use CDC Technologies: Firms must use Change Data Capture tech to manage this real-time syncing. These agents read the transaction log streams. They copy data changes in real-time to cloud targets.
• Apply zIIP Engines: Chiefly, these copy agents use custom mainframe System z Integrated Information Processor engines. This makes sure that the constant pulling of data needs very low MIPS. It also exerts zero speed impact on live workloads.

Challenge 3: Security, Compliance, and and Legal Risk During the Switch

Moving highly central workloads to a highly distributed public or hybrid cloud setup deeply breaks this security posture. Another of the mainframe to cloud migration challenges is massive growth of the attack surface area during and post-migration.

Every new endpoint and network hop is a likely threat for data theft or rogue access. Modern cloud-native architectures rely deeply on package managers.

This means that 70 to 90 percent of an updated codebase is made up of third-party open-source parts. Sadly, 81 percent of company codebases contain high or severe flaws within these outside parts which are another of the challenges of mainframe to cloud migration.

Why? Well, legal frameworks such as the Digital Operational Resilience Act and PCI DSS 4.0 ask for constant threat checks. They also mandate multi-factor authentication and full data flow mapping.

How to Solve This:

• Track Dependency Trees: Firms must use constant Software Composition Analysis to stop supply chain attacks. This will watch dependency trees and build Software Bills of Materials.
• Apply SAST and DAST: At the same time, the ported code must be put through Static Application Security Testing and Dynamic Application Security Testing. This testing must be custom tuned for the new target language.
• Compare Threat Profiles: Scanners must compare the threat profiles of the older binaries against the newly built cloud microservices. This helps detect the entry of novel flaw classes.
• Take on Zero Trust and Archive Systems: Firms must take on a Zero Trust architecture to answer legal demands for data storage and audit needs. Past mainframe data migrated for compliance needs must be transferred to custom archive systems. These systems give Write Once, Read Many immutability.

Challenge 4: Performance Parity - Replicating Mainframe Throughput in Cloud

The key trait of a mainframe is its unmatched power for high-speed, high-volume transactional output. Mainframes allow data input and output tasks with sub-millisecond or even microsecond-level latency.

These tightly coupled workloads are very heavy. They are moved to a distributed cloud setup. Then they hit an issue known as the latency gap. Every database read or write task is transformed into a network call across the data center.

Online transaction response times that are often done in under 500 milliseconds on the mainframe often spike to 5 seconds or more in the cloud. This leads to very bad user flows.

One mainframe to cloud migration challenge worth keeping in mind is reaching exact performance is very hard. About 57 percent of groups see a 15 to 20 percent drop in total performance right after a migration.

How to Solve This:

• Rewrite for Set-Based SQL: Beating the latency gap needs a major shift in how the app connects with data. Coders must rewrite these routines to use set-based SQL tasks when changing procedural COBOL logic to modern cloud frameworks.
• Request Bulk Datasets: The app must be designed to request bulk datasets in a single network round-trip. It cannot request records one by one.
• Deploy In-Memory Caching: Company architects must firmly place distributed in-memory caching layers to further shield the app from network latency. The app can bypass the database network fully for most read tasks by storing often accessed reference data right in the memory of the compute nodes.
• Embrace Horizontal Scaling for Batch Jobs: The architecture must drop linear execution in favor of horizontal scaling for heavy batch compute workloads. Massive batch jobs can be split up and processed at once across hundreds of short-lived compute nodes.
  

Challenge 5: The COBOL Talent Gap and Dual-Platform Skills Shortage

The normal age of a COBOL coder is right now 58. About 10 percent of the mainframe workforce leaves each year. Forecasts warn that a massive 92 percent of the current COBOL dev workforce will either age out of the field or leave by 2027.

A major one of the mainframe to cloud migration challenges is the talent gap for experts in both platforms.

This shortage forces companies to rely on a very small cohort of senior experts to sustain daily tasks. At the same time, they expect them to map out complex, million-line upgrade plans.

How to Solve This:

• Use AI as a Knowledge Bridge: Companies must drop standard hiring methods to beat the dual-platform skills shortage. They must take on a mix of AI tools, cross-training, and agile contract work. Advanced Large Language Models can be used as active guides.
• Explain Older Intent to Modern Devs: Modern Java or Python coders can feed old COBOL routines into an AI agent. They receive natural-language details of the system intent, sequence diagrams, and math formulas.
• Apply Pair Architecting: Companies must firmly build in-house talent through structured knowledge transfer. This includes pair architecting. A senior older subject matter expert is straight paired with a senior cloud architect in this setup.
• Use Custom Outside Help: Building an in-house team of dual-skill tech staff takes one to two years. Teaming with custom upgrade firms or ready talent networks gives instant project speed. This fully skips the 90-day to 180-day hiring cycles.

Challenge 6: Organizational Change Management and Stakeholder Resistance

Mindset and culture roadblocks are often the root cause of project failure. Companies make massive amounts of money spent in tech. Despite this, 41 percent of complex IT upgrades only partly meet their goals. The mainframe to cloud migration challenge here? An extra 4 percent fail outright.

This is almost fully due to poor Organizational Change Management. Senior coders often view the launch of cloud platforms and auto DevOps pipelines as a threat to their skill sets and job safety.

But, the main reason behind this mainframe to cloud migration challenge is a basic lack of knowledge about the business reason pushing the upgrade. Stakeholders across the company often cannot answer the question of what is in it for them. Their pausing translates into workflow trouble.

How to Solve This:

• Treat Staff as Clients: Leadership must raise Organizational Change Management from an HR late thought to a major goal to break down pushback and sync the company. A highly strong game plan views in-house stakeholders not as assets to be managed. They are seen as clients whose buy-in must be earned.
• Secure Clear C-suite Backing: The project needs steady, highly clear backing from the C-suite.
• Deliver Value In Steps: Companies must avoid long-term closed off coding cycles where stakeholders see no progress for long times. Instead, leadership should mandate agile, step-by-step results.
• Create Change Champions: Project leaders can score early wins and hands-on train users on the new systems. This converts doubting critics into vocal change champions who push for the platform across the company.
  

Challenge 7: Cost Overruns and Scope Creep in Large-Scale Programs

Another challenge in mainframe to cloud migration is that the main driver of budget drain is the underestimating of tech depth. As project timelines stretch, scope creep always expands the project borders.

This challenge in mainframe to cloud migration adds massive testing and linking costs that inflates the budget. Long-term dual-running means running both the older mainframe and the newly set up cloud setup at once.

This phase can cost between $50,000 and $200,000 per month. Also, companies that apply a rushed lift-and-shift method often face instant cloud bill shock. Untuned apps consume high-end cloud compute instances non-stop. This fully negates the pay-per-use money edge of cloud computing.

How to Solve This:

• Invest in Deep Process Mapping: Budget strict rules must be built into the coding lifecycle to control the shifting costs of upgrades. Companies must invest 8 to 12 weeks in deep process finding and dependency mapping before a single line of code is ported or a cloud server is set up.
• Freeze the Project Scope: A formal change control framework must be applied at the project start. The scope of the migration must be strictly frozen. It should be managed by a set Project Management Office.
• Embrace FinOps Modeling: Firms must build active, multi-year Total Cost of Ownership models. They must take on a Cloud FinOps culture.
• Design for Elasticity: Cloud architects must clearly design workloads to scale down during off-peak hours. They should use containerization and serverless functions to deeply compress hardware costs.
  

Working With Professionals for Risk Management Frameworks During Your Mainframe Migration Programs

Mainframe upgrades carry extreme risks. These range from massive budget spikes to lasting data damage and legal fines. Success needs moving beyond simple project control. Companies must take on a highly layered Risk Management Framework.

This framework matches best practices from the National Institute of Standards and Technology and the Digital Operational Resilience Act. It makes sure that security, performance, and business uptime are strictly governed across five distinct phases.

• Phase 1: Assess and Discover. This removes tech blind spots by using auto code scan tools to build a complete list and dependency map.
• Phase 2: Prep and Architect. This step designs the target state with security and compliance baked in from the outset.
• Phase 3: Migrate and Update. This phase manages project control greatly through auto testing, auto porting via GenAI, and constant checking.
• Phase 4: Dual Workloads and Cutover. This protects business uptime by running the older mainframe and the cloud setup at the same time. It uses CDC data copying, removing Big Bang risk.
• Phase 5: Fine-tune and Govern. This step creates post-migration strength via constant tracking and Cloud FinOps rules.

Mitigating Mainframe to Cloud Migration Issues With Enterprise Modernization Experts

Carrying out a mainframe to cloud migration can seem daunting (and the truth is it is!). Which is why teaming with DevOps, older migration and data engineering experts can be very helpful.

Guided by a strict, layered risk management framework, companies can safely complete this massive switch (without having to worry too much about mainframe to cloud migration challenges).

At Entrans, we’ve teamed with Fortune 200 companies, along with healthcare and banking firms to improve their total mainframe upgrade journey based on what works best for them.

Run on COBOL or other older systems and aren’t sure what this process would look like? Book a free consultation call with our Mainframe migration experts!