As Australian enterprises accelerate their digital transformation journeys, migrating critical workloads from legacy mainframes to cloud platforms has become a strategic imperative. Yet, this shift is fraught with data related complexities that can derail timelines, inflate costs, and compromise business continuity.
From banking and insurance to government and logistics, many organisations still rely on mainframes for core operations. These systems, often decades old, are deeply embedded in business processes and compliance frameworks. However, their age and complexity introduce growing risks, particularly as support resources dwindle and failure points become hard to predict or repair. Migrating these workloads to cloud not only unlocks scalability, agility, and cost efficiency, but also enhances operational certainty by reducing reliance on brittle, hard to replace infrastructure. When done with a focus on preserving data integrity, security, and performance, cloud migration becomes a powerful strategy to derisk critical systems and ensure long-term resilience.
This article is a brief overview of the key issues that we have seen with Mainframe to Cloud migrations and our perspective on how organisations can derisk modernisations programs.
Top 5 Challenges in Mainframe to Cloud Migrations
- Data Integrity and Consistency: Mainframe migrations involve transferring vast volumes of often sensitive data. Without robust validation, discrepancies can arise due to encoding mismatches (e.g. EBCDIC vs UTF-8), concurrent updates during phased cutovers, or schema misalignment. Inconsistent data can erode trust, delay go-lives and trigger costly rollbacks.
- Data Compatibility and Format Translation: Legacy formats like VSAM, IMS, or COBOL based structures don’t map cleanly to modern cloud databases. Teams often face challenges converting hierarchical data into relational models, especially when legacy applications expect specific layouts. This adds complexity to ETL pipelines and increases testing overhead.
- Security and Compliance Risks: Mainframes often house regulated data such as financial records, health information, or personal identifiers. Migrating this data introduces new risks, including interception during transit, misconfigured cloud access, and potential non-compliance with regulations like APRA, OAIC, and the Cyber Security Act 2024 in Australia or international regulations like GDPR or HIPAA. These frameworks impose strict controls on data handling, breach reporting, and cloud risk classification. Security failures can lead to legal exposure and reputational damage. Additionally, state level regulations, especially in sectors like health or energy, may impose further obligations, making a jurisdiction aware migration strategy essential.
- Performance Bottlenecks: Transferring terabytes of data over limited bandwidth can take days. Cloud ingestion rates may lag behind mainframe throughput, causing sync delays or SLA breaches. Without tuning, migrated applications may underperform, forcing extended dual-running and inflated operational costs.
- Governance and Validation Gaps: Legacy systems often have informal but effective governance. In the cloud, these controls must be redefined. Without clear ownership, audit trails, and validation frameworks, migrated data may be incomplete, duplicated, or misclassified, especially in regulated sectors like banking, insurance or superannuation.
Data issues are a leading cause of migration overruns. In Australia where many organisations that operate under strict compliance and performance mandates, unresolved data challenges can halt migrations midstream. Hybrid approaches keeping some data on-premise may offer short-term relief but undermine long-term cloud benefits.
Data Migration Strategies for Enterprises
Robust Validation Frameworks
Use checksums, record counts, and CDC (Change Data Capture) to ensure data integrity. Tools like Candela’s Control Framework log every load and anomaly, building trust in migrated data.
Automated Data Transformation
Leverage ETL tools and cloud native utilities (e.g. AWS Mainframe Modernization, Azure Data Factory) to convert legacy formats and schemas. Pilot migrations help refine conversion scripts before full-scale rollout.
End-to-End Security
Encrypt data in transit and at rest. Use role-based access controls (RBAC) and conduct penetration testing pre-migration. Classify data sensitivity and mask personal data in test environments to meet Australian Privacy Standards (or other jurisdiction relevant standards).
Optimised Transfer Logic
Access network capacity and use high-throughput options. Schedule transfers during off-peak hours and apply compression/deduplication to accelerate throughput.
Governance and Business Validation
Define retention policies, assign data stewards, and engage business users to validate migrated data. Compare reports across systems to confirm consistency before decommissioning the mainframe.
Phased Migration and Testing
Avoid ‘big-bang’ cutovers. Migrate non-critical datasets first, conduct dress rehearsals, and iterate through ‘learn fast’ cycles. This localises risk and ensures readiness for production.
Data as a Centrepiece
Mainframe to cloud migrations are not just technical exercises, they’re strategic transformations, where compliance, performance, and trust are paramount. Treating data as the centrepiece of migration planning is essential.
By anticipating and addressing data challenges with structured governance, modern tooling, and phased execution, organisations can unlock the full potential of cloud platforms, modernising infrastructure while preserving the integrity of their most valuable asset, data.
To learn how Candela Data can assist with your Mainframe to Cloud Migration, contact us or connect with our founders on LinkedIn Ben Papps Wesley de Koning Michael Grantham, MBA FAICD.