Connecting two systems is rarely just an API call. The hard questions are which system owns a fact, when a change moves and what happens when one side is unavailable.
Draw the data flow
Identify the records, fields and events that move between systems. Mark the source of truth for each important field. If both systems can edit the same value, define which change wins or how a conflict is reviewed.
Define triggers and timing
Some updates can happen later; others must be confirmed immediately. Write down the acceptable delay and the user-facing behavior when an update is pending. Choose webhooks, scheduled sync or direct calls based on the workflow rather than convenience alone.
- Document field mappings and validation rules.
- Choose stable identifiers for matching records.
- Record how failed transfers are retried.
Expect partial failure
Network calls can time out after the receiving system completed the operation. Protect against duplicate processing and keep enough context to investigate an error. A visible reconciliation process is more reliable than silently hoping the next sync fixes everything.
Test with messy real data
Use anonymized examples of missing fields, duplicate contacts, changed statuses and old records. Confirm the integration handles those cases before a broad rollout. Keep an owner for the mapping as both systems evolve.
Practical next step
A durable integration has clear data ownership, observable failures and a way to reconcile differences between systems.
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