“You own your data” is easy to put in a contract. The real test begins when a customer leaves, changes a provider, questions a derived score or wants to understand why an automated decision was made. Ownership without practical control creates dependence disguised as reassurance. A data platform should therefore be judged by the exit it makes possible, not only the service it provides while the customer stays.
There are at least four distinct questions. Who is permitted to collect an event? Who can interpret and enrich it? Who may reuse a derived insight? And who can demand deletion, portability or a complete audit trail? These rights need not belong to the same party. Collapsing them into one vague ownership sentence hides the very negotiations that determine trust.
The architectural consequences are concrete. Keep tenant boundaries explicit. Separate source records from computed features. Track lineage, retention and the version of definitions used to create a metric. Make exports machine-readable; test restore to another system. An access token must not imply permission to train a model, and a data-processing agreement must not silently become a permanent license to business knowledge.
A powerful test is to simulate a difficult departure. Can the customer retrieve their original data and usable context? Can they prove which parties received it? Are derived artifacts governed by clear terms? The future data platform will compete on its ability to be trusted after the relationship ends. Portability is not only compliance. It is product quality.