Legacy systems present a paradox - they run critical functions you can't replace, yet they block the new capabilities your business needs.
Wholesale replacement rarely works. The organisations that succeed take a pragmatic, incremental approach - protecting what works while gradually building new capability alongside it.
Assess Modernisation FeasibilityLegacy systems are both asset and liability:
Run critical business functions - can't simply turn them off
Block new capabilities - can't extend to meet new requirements
Expensive to maintain - knowledge scarce, costs escalating
Risky to change - documentation missing, dependencies unclear
Holding back business - can't deliver features customers need
Wholesale replacement is tempting but rarely succeeds. Pragmatic transformation works better.
Understand what you're dealing with before committing to a path.
Incremental replacement that protects business continuity.
Move existing systems to modern infrastructure when appropriate.
Complete rewrites when justified by business value.
Protect what's working while enabling new capability. Business continuity is non-negotiable.
Small, reversible changes with continuous validation. Big-bang transitions rarely work.
Train your team, don't create dependency. Knowledge transfer throughout.
Prioritise changes that unlock revenue or reduce cost. Technology for technology's sake doesn't deliver value.
When: Scaling bottlenecks, team velocity problems, different components need different scaling strategies.
Approach: Domain-driven decomposition, API gateways, gradual extraction of bounded contexts.
Don't create distributed monolith. Ensure operational capability before committing.
When: Capacity constraints, cost reduction goals, resilience requirements, need for elastic scaling.
Approach: Phased migration, hybrid period with careful dependency management, security-first design.
Cloud isn't automatically cheaper. Proper architecture and cost management required.
When: End-of-life frameworks, security vulnerabilities, can't recruit developers for old stack.
Approach: Assess in-place upgrade vs. parallel build. Depends on code quality and business logic complexity.
Test coverage essential. Business logic often poorly documented.
When: Analytics bottlenecks, real-time requirements, data silos preventing insight.
Approach: Modern data warehouse, event streaming architecture, data lake where appropriate.
Data governance crucial. Migration is opportunity to fix data quality issues.
Python, Node.js, Java/Spring Boot, .NET Core, Go
React, Vue, Modern JS (ES6+), PWAs
PostgreSQL, MySQL, MongoDB, DynamoDB, Redis
AWS (primary), Azure, GCP
Docker, Kubernetes, ECS, Lambda
REST, GraphQL, Message Queues, Event-driven
Understand current architecture, identify pain points, define success criteria.
Target architecture design, detailed migration roadmap, risk mitigation plans.
Incremental delivery with continuous validation and knowledge transfer.
Performance tuning, comprehensive documentation, support transition.
Understand what you're dealing with and what your options are.
Request Assessment