A data clean room is an environment where two organisations can compute results from their combined data without either one gaining access to the other's underlying records. The restriction is the point of the arrangement.
The output is aggregate, not individual
Participants upload data into an environment neither of them controls unilaterally, and both agree in advance on which queries may be run against it.
Results are returned only as aggregates that exceed a minimum group size, so no answer can be traced back to a single person.
That threshold is the central safeguard, and it is why questions about very small segments cannot be answered in these environments at all.
Matching happens without exposing identifiers
The parties need to know which records refer to the same people, which is done by comparing transformed identifiers rather than the originals.
Neither side can reverse the transformation to recover the other's customer list, and neither learns anything about records that failed to match.
The proportion that does match varies considerably, and a low match rate limits what can be concluded regardless of how much data was contributed.
The use cases are measurement and audience definition
Advertisers use these environments to see whether people exposed to a campaign on a platform subsequently purchased, without the platform receiving the purchase records.
Publishers and retailers use them to let advertisers define audiences against first-party data that cannot leave the environment.
Both applications became more valuable as identifiers available on the open web weakened, since the alternative was losing the measurement entirely.
Query governance is where disputes occur
A permitted set of queries protects both parties, but it also means one participant defines what the other is allowed to learn.
Platforms operating their own clean rooms decide which questions may be asked about their inventory, which advertisers reasonably observe is not a neutral position.
Independent environments avoid that conflict at the cost of requiring both parties to move data into a third location, which raises its own concerns.
The limits are practical as well as legal
Analysis is confined to the questions the environment supports, so exploratory work of the kind analysts do with raw data is not possible.
Costs are meaningful, since compute is charged and the specialist skills required are not widely held inside marketing teams.
These environments are consequently worth the effort where the measurement question is specific, repeated and commercially significant, and rarely worth it for one-off curiosity.