Knowledge Graphs: The Secret Weapon for Legacy System Analysis
How Neo4j-powered knowledge graphs reveal hidden relationships in mainframe systems that traditional analysis tools miss.
Teams modernizing legacy platforms quickly discover that traditional documentation is incomplete, outdated, or missing entirely. Critical knowledge lives in code structure, data definitions, and implicit relationships spread across programs.
Knowledge graphs provide a practical way to model this hidden structure and make it accessible to engineers, architects, and subject-matter experts. Across multiple modernization efforts, we've observed that the hardest part is not rewriting code—but reconstructing intent, dependencies, and business meaning embedded in legacy systems.
The Relationship Problem
A typical enterprise mainframe portfolio contains:
- Thousands of COBOL programs
- Hundreds of copybooks shared between programs
- Complex screen flows through CICS transactions
- Batch jobs with intricate dependencies
- Business rules embedded across multiple layers
Traditional documentation tools capture what exists. They miss how everything connects.
Enter Knowledge Graphs
Knowledge graphs excel at representing and querying relationships. In Genesis MKE, we use Neo4j to model:
- Program dependencies: Which programs call which, and with what data
- Data flows: How information moves from screens to databases
- Business rules: Where validation logic lives and what it protects
- Screen interactions: User journey paths through the application
A Practical Example
Consider a simple question: "What happens if we change the customer address validation?"
With flat documentation, you'd manually trace through code. With a knowledge graph, you query:
MATCH (rule:BusinessRule {name: 'AddressValidation'})
-[:USED_BY]->(program)
-[:WRITES_TO]->(screen)
RETURN program.name, screen.name
In seconds, you see every program and screen affected.
Beyond Static Analysis
The real power emerges when you combine the graph with AI-assisted semantic analysis. The knowledge graph provides structure; Claude provides understanding of business intent.
Together, they surface insights that neither could find alone: implicit business rules, undocumented dependencies, and modernization risk factors.
Building Your Graph
Start with a focused scope—one subsystem or business domain. Extract, model, validate, then expand. The graph grows incrementally, and each addition increases the value of what's already captured.