Avoid Disruption¶
Make changes gradually with clear interdependencies to avoid disruption across use cases.
An Enterprise Knowledge Graph serves many use cases on one platform—albeit a distributed, federated platform not controlled by any single party. Like the web itself, many parties have a say. Different stakeholders, different agendas, different priorities—all sharing the same foundation. They don't care about each other's use cases. Nor should they have to.
But here's the problem: a change to one use case can break another.
The Scaling Wall¶
What traditionally happens with enterprise systems is predictable and depressing. A system starts with ambitions to serve the whole enterprise. It grows. More teams depend on it. More integrations. More data flows.
Then it hits the wall.
Not a technology wall—the technology could scale further. A political wall. Nobody dares change anything anymore because nobody can oversee the impact. Too many parties might be disrupted. Too many stakeholders to consult. Too many approval processes.
The system fossilizes. It becomes "legacy" not because the technology is old, but because the organization lost the ability to evolve it safely. New requirements get bolted on as workarounds. Shadow systems spring up. The original vision of enterprise-wide coverage dies quietly in committee meetings.
This is not a technology problem. It's a visibility and governance problem.
The Shared Foundation Risk¶
When use cases share components—ontologies, datasets, services—a change that helps one team can blindside another:
- Updated ontology breaks a downstream query
- New validation rule rejects previously valid data
- Performance optimization slows down a different workflow
- "Improved" API response breaks an integration nobody told you about
This happens to every large system. Every knowledge graph project. Every enterprise platform. It always goes like this.
Unless you organize it properly.
Two Non-Negotiables¶
1. No big bang releases
Changes happen gradually. Small increments. Continuous delivery. If something breaks, you know exactly what caused it and can roll back fast.
Big bang releases are how organizations burn millions and deliver chaos. You test for months, release on a Friday night, and spend the weekend firefighting. Nobody learns anything except that they hate release weekends.
2. Clear, tested interdependencies
Every connection between use cases must be explicit, documented, and automatically tested. Continuously. Not "we'll test it before release." Continuously.
If use case A depends on use case B, and someone changes B, the tests for A must run immediately. Not tomorrow. Not next sprint. Now.
The Use Case Tree Makes This Possible¶
The Use Case Tree is specifically designed to solve this problem. It maps out precisely:
- Which use cases share which components
- Who depends on whom
- What the impact of any change will be
- What the cost of that change will be
Every use case, every story, every detail is rigorously tested in a continuous deployment setup. No surprises in production. When you change something, you know immediately what breaks—and you know before it reaches anyone.
This isn't just technical scalability. It's organizational scalability. The Use Case Tree method is designed for infinite scale—not by ignoring the political and governance challenges, but by making them visible and manageable.
Reuse Without Fear¶
The goal of modularity is reuse. But reuse creates dependencies. Dependencies create risk.
The Use Case Tree method doesn't pretend this risk doesn't exist. It makes the risk visible and manageable. Build reusable components, share them across use cases, but know exactly what you're sharing with whom—and test it relentlessly.