How to Document System Integrations — A Practical Guide
Most integration documentation efforts fail not because teams don't try, but because they don't capture the right things — and because the method they use has no way to stay current. This guide covers what to document, how to structure it, and how to keep it accurate after go-live.
Why integration documentation goes wrong
The typical pattern: a team goes live with a new integration, writes up a Confluence page or Visio diagram, and considers the documentation done. Three months later, a field mapping changes. The Confluence page isn't updated. A year later, the original author leaves. By the time someone tries to use the documentation during an incident, it describes an integration that no longer exists.
The failure isn't cultural — it's structural. Documentation written after the fact and separated from the change workflow will always decay. The fix is to integrate documentation into the workflow itself, so updating it has zero marginal cost above what the team is already doing.
The six-step documentation process
Identify the systems and direction
Start with the basics: source system, target system, trigger (event-driven or scheduled?), direction (one-way or bidirectional?), and transport protocol. This is your header — everything else hangs from it.
Map the fields being exchanged
This is where most integration documentation fails: it stops at the system level and never documents what data actually crosses the boundary. For each field: name, data type, source field path, target field path, and whether a transformation is applied.
Document the business logic
Transformations are not just format changes — they carry business rules. A field that says 'convert currency to GBP at trade date rate' is carrying a business rule that, if changed, could have regulatory consequences. Capture each rule explicitly.
Record dependencies
An integration doesn't exist in isolation. It depends on upstream data feeds, downstream consumers, reference data systems, and configuration. If any of those change, your integration is affected. Dependencies should be explicit, typed, and queryable.
Establish a baseline and approval
Once the documentation is complete, it needs to be reviewed and agreed. A baseline is the first formal version: both the delivering team and the receiving team have reviewed and signed off. This baseline is your reference for the life of the integration — and the starting point for every future change.
Make changes through a controlled workflow
After go-live, the most important thing is that changes go through the documentation before they go into production — not after. A change request names what will change, what the impact is, and who has approved it. The documentation is updated as part of the approval, not as a follow-up task.
What makes documentation queryable
A diagram or wiki page can tell you what an integration does. It cannot tell you what breaks if you change it. To answer impact analysis questions, your documentation needs to be structured — typed relationships between named entities, not prose or pictures.
When dependencies are explicit and typed, you can ask: "If I retire System A, what integrations stop working?" or "Which integrations carry PII data without an encryption control?" These questions are answerable in seconds against a structured model and unanswerable from a wiki in any reasonable time.
For regulated environments
If you're in financial services, healthcare, or another regulated industry, your documentation requirements go further. Regulators expect point-in-time reconstruction ("show us the data flow at the time of this incident"), field-level data lineage, and evidence of controls on high-risk data. This is not achievable with static documentation — it requires append-only versioning and field-level sensitivity classification baked into the model from the start.
See our guide on integration documentation for financial services for the regulatory-specific requirements.
Frequently asked questions
What should integration documentation include?
At minimum: the source and target systems, the trigger and direction of the data flow, the fields exchanged (with data types and sensitivity), any transformations or business rules applied, error handling, SLA, and the owner. For regulated environments, also include a sensitivity classification per field and a control mapping for high-risk data.
How do you keep integration documentation up to date?
The only sustainable method is to make documentation part of the change workflow itself — not a post-hoc artifact. When a change is proposed, the documentation is updated as part of the proposal. When it's approved, the updated version becomes the new baseline. This approach has zero marginal cost above what the team is already doing.
What is the difference between integration documentation and API documentation?
API documentation describes the interface contract — the endpoints, payloads, and schemas that a system exposes. Integration documentation describes how those interfaces are actually used in a specific business context — the field mappings, transformations, business rules, and dependencies specific to this integration between these two systems. Both are needed; neither substitutes for the other.
How do you document integration dependencies?
Dependencies should be modelled as typed, explicit relationships in your documentation — not implied by proximity in a diagram. Each dependency should name what relies on what, the type of dependency (data, control, configuration), and the direction. This enables automated impact analysis: 'what does this integration depend on?' and 'what depends on this integration?'
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